Geospatial Assessment of Landslide Hazard in Kinnaur District Using AHP-Based Multi-Criteria Decision Analysis

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This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7092462/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 19 You are reading this latest preprint version Abstract In mountainous regions, landslides cause substantial socio-economic harms since they are among the most common and damaging natural disasters. The Upper Satluj River Basin in Kinnaur district of Himachal Pradesh is particularly vulnerable to landslides due to its mountainous terrain and intricate geology. The Analytical Hierarchy Process (AHP) method of analysis is the foundation for the landslide hazard zonation (LHZ) mapping in study area. The authors of this study evaluated twelve factors based on how much they contributed to landslide incidents. The AHP method allows for an objective factor weighting process by determining the importance of each factor for the decision-making process through pairwise comparisons. The final landslide hazard zone map has been categorised into five hazard zones, from very low to low to moderate and high to very high levels. 36% of the study area is in the high to very high hazard zone, 30% is in the moderate hazard zone, and 34% is in the low to very low hazard zone. Most of these high-susceptibility zones are located in the northern, northeastern, and some central regions of the study area. A test using ROC curves and AUC measurements showed that the model's performance evaluation accuracy was 71%. The AHP-based model's prediction results for the study area demonstrate a trustworthy approach to landslide susceptibility assessment. The study provides crucial information for planning land use, disaster management, and infrastructure development across the Upper Satluj River Basin. To lessen the impact of landslides on infrastructure and communities, local governments and legislators must focus their mitigation efforts on high-risk areas that have been identified. This study proves that multi-criteria spatial analysis should be included in future research while also proving that the Analytic Hierarchy Process (AHP) provides effective landslide hazard evaluation. Landslide Hazard Zonation (LHZ) Basin Susceptibility AHP ROC-AUC Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Landslides represent a major natural hazard defined by the movement of rock, soil, and other debris along a slope due to gravitational forces. Numerous scholars have studied landslides extensively, and they have all provided definitions based on their disciplinary backgrounds and areas of study. Varnes ( 1954 ) gave one of the first systematic definitions of landslides, defining them as “the movement of rock, debris, or earth down a slope as a result of gravity”. His classification system has been widely used in geological and geotechnical research and served as the basis for landslide studies. Cruden and Varnes ( 1996 ) expanded on this by underscoring the significance of material composition and movement mechanisms. In order to standardise landslide studies across different disciplines, this definition was important. To further elaborate on these ideas, Highland and Bobrowsky ( 2008 ) distinguished landslides from other kinds of slope failures by defining them as a particular type of mass wasting. They emphasised that a number of things, such as precipitation, seismic activity, and human activity, can cause landslides. In order to bring the Varnes landslide classification up to date with geological and geotechnical terminology, Hungr et al. ( 2014 ) refined the material categories. To increase the clarity of hazard assessments, they broadened the classification to include 32 different landslide types. They suggested composite naming based on movement transitions rather than treating complex landslides as a distinct category. Their updated system made translations easier for worldwide use while guaranteeing backward compatibility with earlier classifications. By improving landslide hazard studies, these changes increased the classification's applicability in risk management and geotechnical engineering. The occurrence of landslides and their complexity are clarified by all of these definitions and descriptions, which highlight the significance of evaluating these risks as a component of disaster risk management and responsible land use planning. Every year, landslides result in significant financial losses as well as fatalities, making them a global threat. Some areas, such as India, Nepal, Tajikistan, and Colombia, are especially vulnerable to landslides, with death rates of more than one person per 100 km² per year (Nadim et al. 2006 ). Because of its steep terrain, active tectonics, and abundant monsoonal precipitation, the Indian Himalayan region is particularly susceptible to landslides. Froude and Petley ( 2018 ) found that precipitation was the main cause of 477 of the 580 landslide events that occurred in the Indian Himalayas between 2004 and 2017, accounting for 14.52% of all landslides that have been reported globally. Delineating high-risk zones becomes essential for effective hazard mitigation because mountainous terrain is inherently unstable. An essential tool for locating and categorising regions according to their landslide hazard is the Landslide Hazard Zonation (LHZ). In order to help policymakers, urban planners, and disaster management authorities create effective mitigation strategies, the main goal of LHZ is to identify areas with different levels of hazard, from very low to very high susceptibility. Planning for sustainable land use, creating early warning systems, and putting risk reduction strategies into action all depend on an accurate LHZ map. Therefore, a thorough LHZ assessment that takes into account several conditioning factors is essential to reducing the negative effects of landslides and guaranteeing both human and environmental safety. Heuristic, statistical, and machine learning-based approaches are the three categories into which LHZ methodologies fall. Heuristic techniques mostly depend on the opinion of experts and field observations, while statistical techniques, like logistic regression models and the frequency ratio etc. create empirical connections between past landslide events and different causes to create susceptibility maps (Shano et al. 2020 ). As computational methods have advanced, machine learning models have been widely used to increase reliability of predictions. Examples of these methods include Artificial Neural Networks (Hsu et al. 2011 ; Sweta et al. 2022 ; Youssef et al. 2023 ) and Support Vector Machines (SVM) (Shukla et al. 2016 ; Hussain et al. 2025 ; Saha et al. 2023 ). The Analytical Hierarchy Process (AHP), one of the popular semi-quantitative methods for LHZ, is especially well-known for its methodical framework in multi-criteria decision-making. Saaty ( 1980 ) introduced the Analytical Hierarchy Process (AHP), a structured decision-making methodology that evaluates the relative significance of various landslide conditioning factors through pairwise comparisons. By using a consistency ratio check, this method incorporates expert judgment while preserving uniformity in weight assignment. Because AHP can be used in data-scarce regions and is flexible in incorporating multiple conditioning factors, it has been widely used in LHZ studies. AHP offers a quantitative framework for evaluating hazard susceptibility by allocating relative weights to various landslide causative factors. This makes it easier to create hazard maps that divide regions into zones with different degrees of danger, from low to high. AHP's reliance on researcher knowledge adds a certain amount of subjectivity, but consistency checks, and sensitivity analysis greatly increase its dependability. Additionally, by facilitating the spatial analysis and visualisation of areas that are prone to hazards, the integration of AHP with Geographic Information Systems (GIS) increases its efficacy. The usefulness of AHP in landslide susceptibility assessments in a variety of geographical contexts has been shown in numerous studies. Saha et al. (2005), for example, used AHP in the Bhagirathi Valley, India, combining several causative factors for mapping the LHZ. Similar to this, Yalcin ( 2008 ) used AHP in Turkey to show how effective it is at categorising areas that are prone to hazards. Pourghasemi et al. ( 2012 ) further validated the applicability of AHP in hazard assessment by using it for landslide susceptibility mapping in Iran. Furthermore, an AHP-based study in Nepal's Tinau watershed was carried out by Kayastha et al. (2013), confirming its dependability in hazard classification. Dai et al. ( 2001 ) expanded the use of the Analytical Hierarchy Process (AHP) in China, applying it to urban land use planning with a special emphasis on landslide susceptibility. AHP-based LHZ research was carried out in the Indian Himalayas more recently by Singh et al. ( 2024 ), who confirmed the method's applicability for challenging terrains and achieved high predictive accuracy. These studies highlight how versatile AHP is in different geographic settings and how important it is for managing landslide hazards, especially in high-risk areas. 2. Study Area The present study focuses on the Upper Satluj River Basin, located within the Kinnaur district of Himachal Pradesh. The Satluj River Basin includes the whole Kinnaur district except for the northeastern part of Sangla tehsil. The entire study area is roughly 6245 km 2 , excluding this northeastern section that falls into the Yamuna Basin. With elevations ranging from 1193 meters in the lower valleys to 6725 meters in the high-altitude peaks, the region is distinguished by extremely rugged and mountainous topography, resulting in a relative elevation variation of 5532 meters. The main drainage system is the Satluj River, which rises in the Tibetan Plateau and has the Spiti, Baspa, Tidong and Ropa rivers as its principal tributaries. The geomorphology and water resources of the area are significantly shaped by these hydrological systems. The climate of Kinnaur is varied, with temperate climate at lower elevations to frigid desert features in the higher reaches. The annual precipitation varies from 500 to 1200 mm, with snowfall making up the majority of precipitation at higher elevations, particularly in the winter. Significant differences in vegetation and land cover across various altitudinal zones are a result of the monsoonal influence gradually waning from west to east. Coniferous forests, which are mostly made up of pine, deodar and fir predominate in the lower and mid-altitude areas. On the other hand, alpine meadows, arid rocky terrains, and vast glacial landscapes are features of the higher elevations. Furthermore, horticultural pursuits and apple orchards are common in valley areas like Kalpa and Sangla, greatly boosting the local agrarian economy. The Upper Satluj Basin is extremely vulnerable to earthquakes and frequent landslides due to its geological location within the Himalayan orogenic belt. The geotechnical stability of the area is greatly impacted by the Kanwar lithological group, which is prominent in this area and is mainly composed of quartzite and black shale with nodules. The region's dynamic geological processes and complex geomorphological evolution make it a valuable location for research on environmental sustainability, natural hazard susceptibility, and landscape evolution. Because of its rough terrain and harsh climate, the sparsely populated Kinnaur district poses serious challenges to human habitation. Nonetheless, the study area contains a number of noteworthy communities, such as Sangla, Kalpa, Pooh, and Reckong Peo (the district headquarters). A sizable portion of the local population is made up of the indigenous Kinnaura tribes, who are renowned for their distinctive socioeconomic practices, rich cultural heritage, and traditional way of life. Topographical limitations, climatic hardship, and geographic isolation have all impacted the region's land-use patterns and development patterns, making sustainable environmental management and hazard mitigation techniques necessary. 3. Methodology In this study, some of the steps involved in developing landslide hazard zonation (LHZ) include selecting relevant causative factors, assessing for multicollinearity, allocating weights using the Analytical Hierarchy Process (AHP), and validating the model using Receiver Operating Characteristic-Area under the Curve (ROC-AUC) analysis. The methodology used in the current study is explained below. 3.1 Causative Factors: Landslides are a major geological hazard that cause significant damage to infrastructure, disrupt human settlements, and lead to loss of life globally. Geological, geomorphological, hydrological, and climatic conditions are among the many natural and man-made elements that interact intricately to cause them. Deforestation, unplanned urbanisation, and climate change have all contributed to an increase in the frequency and size of landslides in recent years (Guzzetti et al. 1999 ; Varnes 1984 ; Dai et al. 2001 ). A crucial part of disaster risk management is landslide hazard zoning (LHZ), which offers a scientific foundation for mitigation and land-use planning. It involves identifying areas prone to landslides by analyzing various causative factors and assigning hazard levels accordingly. For LHZ, a variety of methods have been created, from data-driven and machine-learning models to heuristic approaches (Lee & Pradhan 2007 ). Twelve causative factors were chosen for this investigation with regard to their impact on slope stability in the study area and their applicability in earlier studies. A multicollinearity test was performed to remove highly correlated variables in order to guarantee the model's dependability. The factors were given weights based pairwise comparisons using the Analytical Hierarchy Process (AHP), which was created by Saaty in 1980. Lastly, the predictive performance of the model was evaluated by validating it using the Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) method. 3.1.1 Topographic Factors Slope stability is greatly impacted by topography since it dictates surface runoff, soil properties, and gravitational forces. In order to determine the dominant topographic factors in landslide mapping analysis, a study was conducted and presented in this paper. a. Slope Slope gradient is a crucial element in landslide hazard because steeper slopes are subject to greater gravitational force, which decreases the stability of soil and rock masses. Particularly during periods of intense rainfall or seismic activity, slopes that have an angle larger than the internal friction limit of the material are extremely prone to failure (Gokceoglu & Aksoy 1996; Yalcin 2008 ). Steeper slopes also encourage faster surface runoff, which increases the possibility of erosion while decreasing water infiltration (Ayalew & Yamagishi 2005 ; Lee 2005 ; Pradhan & Lee 2010 ). Slope has been divided into five classes i.e. less than 15˚, 15˚-25˚, 25˚-35˚, 35˚-45˚, and more than 45˚. Less than 15˚ class has the lowest percentage of the area covered, at 12%, while 25˚ to 35˚ class has the highest percentage, at 30%. b. Aspect By controlling vegetation distribution and moisture retention, two factors that are crucial for determining soil strength and slope stability, slope aspect has a major influence on the initiation of landslides. Slope failure risk is changed by aspect differences, which also affect soil water content and root reinforcement. Additionally, when influenced by dominant wind patterns, rainfall distribution can result in uneven precipitation levels across slope orientations, which in turn can alter the susceptibility to landslides (Wieczorek et al. 1997 ). Furthermore, monsoonal winds and orographic effects lead to spatial differences in rainfall, which increases the pore water pressure and saturation on windward slopes, making them more susceptible to landslides (Saha et al. 2005). Its importance in landslide hazard assessments is highlighted by the interaction of slope aspect, geological features, vegetation patterns, and climatic variables, especially in the Himalayan region. Table 1 Data sources of landslide conditioning factors Sr. no. Parameter Source Scale/ Resolution 1 Slope gradient ALOS PALSAR DEM 12.5 m 2 Slope aspect ALOS PALSAR DEM 12.5 m 3 Relative Relief ALOS PALSAR DEM 12.5 m 4 Topographic wetness Index ALOS PALSAR DEM 12.5 m 5 Stream Power Index ALOS PALSAR DEM 12.5 m 6 Drainage Density ALOS PALSAR DEM 12.5 m 7 Lithology Geological survey of India 1:50000 8 Lineament Geological survey of India 1:50000 9 Thrust Geological survey of India 1:50000 10 Rainfall IMD Gridded Data 0.25 ˚× 0.25 ˚ 11 LULC Sentinal-2 10 m 12 NDVI Sentinal-2 10 m c. Relative relief When evaluating the ruggedness of a given terrain, relative relief—which is the elevation difference within that area—is a crucial consideration. Steep gradients and deep valleys are characteristics of high relative relief regions, which make them more vulnerable to landslides because of increased rates of erosion and gravitational stress. According to studies by Anbalagan ( 1992 ), Saha et al. ( 2002 ), and others, regions with a large elevation contrast are more likely to experience mass movements as a result of differential weathering effects and gravitational pull. Five classes—very low, low, moderate, high, and very high—have been established for the relative relief map. 3.1.2 Hydrological Factors By changing soil moisture levels, decreasing shear strength, and accelerating erosion, hydrological conditions can cause slope instability. a. Drainage Density “Drainage density defines length of streams per unit of drainage area” (Horten 1932). Because increased water movement causes erosion and slope undercutting, high drainage density is frequently associated with increased slope instability (Ayalew & Yamagishi 2005 ). Furthermore, in steep terrain, concentrated water flow can cause channelised landslides and debris flows (Gorsevski et al. 2006 ; Lee & Pradhan 2007 ). b. Stream Power Index (SPI) The erosive capacity of flowing water is measured by the Stream Power Index (SPI), which is dependent on slope gradient and discharge. Areas with higher SPI values are those where concentrated water flow puts a lot of force on slopes, raising the risk of erosion and slope failure. Prior research by Moore et al. ( 1991 ), Costanzo et al. (2014) has demonstrated the importance of SPI in landslide-prone areas, especially those with high precipitation. Following Moore et al. ( 1991 ), the stream power index (SPI) was calculated and is shown below: SP \(\:\text{I}=\text{ln}\:\left(\left({\text{A}}_{\text{s}}\right)\text{*}\left({\text{t}\text{a}\text{n}}_{{\beta\:}}\right)\:\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\) where tanβ is the slope, As is the flow accumulation, and ln is the natural log. Utilizing the method of natural break, the SPI map has been divided into five classes, ranging from very low to very high. c. Topographic Wetness Index (TWI) TWI measures the amount of water that accumulates in a landscape, which affects soil moisture content and the likelihood of slope failure. Zones where water tends to accumulate, resulting in prolonged saturation and decreased shear strength, are indicated by high TWI values. TWI is a crucial factor in forecasting shallow landslides, especially in areas with heavy rainfall, according to Beven and Kirkby ( 1979 ), Gokceoglu and Aksoy (1996), and Lee & Evangelista ( 2006 ). Topographic wetness index (TWI) has been derived after Moore et al. ( 1991 ) given below- $$\:\text{T}\text{W}\text{I}=\text{ln}\left(\frac{{\text{A}}_{\text{s}}}{{\text{t}\text{a}\text{n}}_{{\beta\:}}}\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:$$ where tanβ is the slope, As is the flow accumulation, and ln is the natural log. Utilizing the method of natural break, the TWI map has been divided into five classes, ranging from very low to very high. d. Rainfall Landslides are mostly caused by heavy precipitation because it saturates the soil, raising the pressure in the pores and decreasing shear strength. Slope failures are frequently caused by intense or protracted rainfall events, especially in areas with loose or fractured rock formations. Glade ( 1998 ), Guzzetti et al. ( 2006 ), Dahal and Hasegawa ( 2008 ) have shown that landslide occurrences are strongly correlated with areas that experience heavy rainfall either annually or seasonally. The study area's rainfall map was produced using IMD Gridded data. 3.1.3 Geological and Structural Factors Slope stability is greatly impacted by geological characteristics and structural elements, which affect failure mechanisms and material strength a. Lithology group The resistance of various lithological formations to erosion and weathering varies. Because of their low cohesiveness and vulnerability to water infiltration, weak rock types like schist and shale are particularly vulnerable to landslides (Gupta and Joshi 1990 ). Twelve lithological groups—Kanawar, Lilang, Vaikrita, Rampur (Naraul), Jeori-Wangtu banded Gneissic complex, Kulu, Sanugba, Kuling, Haimanta, Jutogh, Rakcham Granite, and Nako Granite—are identified in the lithology map created for this study using data from the Geological Survey of India. b. Lineaments Density Faults and fractures are examples of lineaments that produce weak spots that make slope failure easier. Active tectonic zones, where stress accumulation causes frequent slope failures, are frequently linked to areas with high lineament densities. It has been demonstrated by Gupta & Joshi ( 1990 ), Saha et al. ( 2002 ), and Pradhan & Lee ( 2010 ) that areas that are faulted and fractured are much more vulnerable to landslides. Lineament Density has been divided into 5 categories ranges from very low to very high on the basis of natural break system and low density class covers the highest area which is approx. The lowest area is roughly covered by 28% and very high class. 6 percent of the total. c. Distance from thrust Zones of extreme deformation, where rock masses are severely sheared and fractured, are represented by thrust faults. Due to their inherent instability, these areas are frequently the site of landslides (Anbalagan 1992 ). For instance, there are many landslides connected to significant thrust faults in the Himalayas. Distance from the thrust factor, which is divided into three categories—0–50 m, 50–200 m, and more than 200 m—has been taken into account in this study. 3.1.4 Anthropogenic factors Slopes' inherent stability is altered by human activity, which makes them more vulnerable to landslides. a. Land Cover and Land Use (LULC) By changing vegetation cover and water infiltration rates, LULC changes—such as deforestation, urbanisation, and agricultural expansion—have an effect on slope stability. Human-modified landscapes are more vulnerable to landslides because of decreased root binding strength and increased surface runoff, as shown by Saha et al. (2005), Pradhan & Lee ( 2010 ). Utilizing Sentinal 2A and 2b satellite imagery, map of land use and landcover have been produced, with categories such as barren land, shrubs, forests, agri-land, builtup, snow and glaciers, and water bodies. b. Normalised Difference Vegetation Index NDVI is a remote sensing-derived index that measures vegetation health. While low NDVI values signify bare or degraded land, raising the risk of landslides, dense vegetation stabilises slopes by strengthening soil and lowering surface runoff (Gupta and Joshi 1990 ; Lee and Pradhan 2007 ). NDVI maps have been created using sentinel satellite images. 3.2 Multicollinearity Multicollinearity is a statistical condition in which two or more independent variables show a high degree of correlation, making it difficult to determine how each of them affects the dependent variable separately (Gujarati and Porter 2009 ). Multicollinearity can skew the contribution of individual conditioning factors in landslide susceptibility modelling, making it challenging to identify the variables that actually affect landslide occurrences. Strong correlations can result in skewed estimations and unstable predictions, which is especially problematic for models that depend on weight assignments, such as the Analytical Hierarchy Process (AHP), Logistic Regression, and Frequency Ratio models (Dormann et al. 2013 ). Values for the Variance Inflation Factor (VIF) and Tolerance were calculated in order to evaluate multicollinearity among the 12 landslide conditioning factors that were chosen. Tolerance (1/VIF) values below 0.1 indicate high redundancy, while VIF values above 10 indicate severe collinearity (Kutner et al. 2004; Wang et al. 2008 ). The following are the outcomes (Table no. 3.2): Table 2 Multicollinearity Analysis of Causative Factors Collinearity Statistics VIF Tolerance Aspect 1.03 0.973 Drainage Density 1.12 0.892 Lineament Density 1.07 0.931 Lithology 1.11 0.899 NDVI 1.18 0.845 Rainfall 1.15 0.868 Relative Relief 1.1 0.906 SPI 1.21 0.83 Distance from Thrust 1.15 0.87 TWI 1.21 0.828 LULC 1.1 0.906 Slope 1.13 0.883 The lack of significant multicollinearity among the chosen factors is confirmed by the fact that all VIF values are less than 2 and that tolerance values stay above 0.8. Aspect (1.03) showed the lowest collinearity, while SPI (1.21) and TWI (1.21), which showed the highest VIF values, indicated a moderate correlation. The variables can be used in the landslide hazard zonation (LHZ) model without risk of distortion because none of the VIF values are greater than 5, which is typically regarded as acceptable in regression modelling (Kutner et al. 2004; Dormann et al. 2013 ). 3.3 Landslide Hazard Zonation Using the Analytical Hierarchy Process (AHP) Saaty ( 1980 ) created the Analytical Hierarchy Process (AHP), a multi-criteria decision-making (MCDM) technique that is frequently applied in landslide hazard zonation (LHZ) to systematically allocate weight to different causative factors. This technique guarantees a structured approach to decision-making in hazard analysis by enabling a pairwise comparison of specific factors based on expert judgement. AHP is structured into several steps: a. Constructing the Pairwise Comparison Matrix A pairwise comparison matrix is constructed to evaluate the relative importance of each causative factor. The importance of one factor over another is assigned based on Saaty’s fundamental scale of judgment, which ranges from 1 (equal importance) to 9 (extreme importance) (Saaty, 1980 ). Table 3 Fundamental scale of judgment Scale Definition 1 Equal importance 3 Moderate importance 5 Strong importance 7 Very strong importance 9 Extreme importance 2, 4, 6, 8 Intermediate values Source : Saaty, 1980 b. Pairwise Comparison Matrix Normalisation A normalised matrix is obtained by dividing each matrix element by the sum of the columns. The normalised matrix's rows are averaged to produce the priority vector, also known as the weightage. c. Check for Consistency with CI and CR To ensure that expert judgments are logically consistent, a Consistency Index (CI) and Consistency Ratio (CR) are calculated. This is how the Consistency Index (CI) is calculated: $$\:CI=\frac{{\lambda\:}_{max}-n}{n-1}$$ where λ_max is the largest eigenvalue of the matrix, and n is the number of factors. The following provides the Consistency Ratio (CR): $$\:CR=\frac{CI}{RI}$$ where RI (Random Index) is a standard reference value that depends on the matrix size, as shown in Table 4 . Table 4 Random Index (RI) Values No. of variables 1 2 3 4 5 6 7 8 9 10 RI 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 Source : Saaty, 1980 The pairwise comparisons are regarded as consistent if CR ≤ 0.1. The judgements must be examined and updated if CR is greater than 0.1. d. Final Weights and Landslide Hazard Zonation Map Derivation Once the weights are finalized, they are assigned to each causative factor in the GIS environment and calculate landslide hazard index using the following equation: LHI = Σ Weight of factor (w i ) × Weight of factor classes (w ij ) Where w ij denotes weight of ith class of factor jth. Table 5 AHP scores of factors, classes, CR and CI Factors and Classes 1 2 3 4 5 6 7 8 9 10 11 12 Weights Factors comparison Slope (1) 1 0.21 Lithology (2) 0.333 1 0.167 Lineaments (3) 0.5 0.333 1 0.137 Distance from thrust (4) 0.2 0.333 0.333 1 0.118 Rainfall (5) 0.333 0.333 0.333 0.333 1 0.095 LULC (6) 0.5 0.5 0.5 0.333 0.5 1 0.08 DD (7) 0.25 0.25 0.333 0.25 0.25 0.5 1 0.055 RR (8) 0.333 0.333 0.333 0.333 0.333 0.333 0.5 1 0.045 NDVI (9) 0.25 0.25 0.25 0.25 0.25 0.25 0.333 0.5 1 0.036 SPI (10) 0.2 0.2 0.2 0.2 0.2 0.25 0.25 0.25 0.333 1 0.023 TWI (11) 0.167 0.167 0.167 0.167 0.167 0.2 0.2 0.2 0.25 0.5 1 0.017 Aspect (12) 0.167 0.167 0.167 0.167 0.167 0.167 0.2 1 0.25 0.333 1 1 0.017 Consistency Index (CI) = 0.1407, Consistency Ratio (CR) = 0.091673 Factors Classes Comparison Slope Category(in degrees) > 45 (1) 1 0.457 35–45 (2) 0.5 1 0.305 25–35 (3) 0.25 0.333 1 0.127 15–25 (4) 0.167 0.2 0.5 1 0.069 < 15 (5) 0.125 0.143 0.25 0.5 1 0.041 Consistency Index (CI) = 0.019335, Consistency Ratio (CR) = 0.017194 Lithology Jeori (1) 1 0.221 Haimanta(2) 0.5 1 0.185 kanwar(3) 0.5 0.5 1 0.157 Sanugba(4) 0.333 0.333 0.5 1 0.101 Kadcham(5) 0.25 0.333 0.333 1 1 0.095 Vaikrita (6) 0.25 0.25 0.25 0.5 0.5 1 0.063 Jutogh(7) 0.25 0.25 0.25 0.333 0.333 1 1 0.056 Rampur(8) 0.2 0.2 0.2 0.25 0.333 0.5 0.5 1 0.035 Kulu(9) 0.167 0.2 0.2 0.25 0.25 0.333 0.333 1 1 0.033 Lilang(10) 0.167 0.167 0.167 0.2 0.2 0.25 0.333 0.5 0.5 1 0.023 Kuling(11) 0.143 0.167 0.167 0.167 0.167 0.2 0.25 0.333 0.333 0.5 1 0.018 Nako(12) 0.125 0.143 0.167 0.167 0.167 0.167 0.2 0.333 0.25 0.5 1 1 0.015 Consistency Index (CI) = 0.06531, Consistency Ratio (CR) = 0.042545 Aspect S(1) 1 0.215 SE(2) 1 1 0.215 SW(3) 0.5 0.5 1 0.159 W(4) 0.5 0.5 0.5 1 0.122 E(5) 0.333 0.333 0.5 0.5 1 0.09 NE(6) 0.333 0.333 0.333 0.5 0.5 1 0.074 N(7) 0.25 0.25 0.333 0.333 0.5 0.5 1 0.054 NW(8) 0.25 0.25 0.25 0.333 0.333 0.333 0.5 1 0.042 Flate(9) 0.2 0.2 0.2 0.25 0.333 0.333 0.333 0.333 1 0.028 Consistency Index (CI) = 0.046045, Consistency Ratio (CR) = 0.031817 Drainage Density Very high(1) 1 0.433 High(2) 0.5 1 0.298 Moderate(3) 0.25 0.333 1 0.159 Low(4) 0.2 0.2 0.333 1 0.072 Very low(5) 0.143 0.167 0.143 0.333 1 0.037 Consistency Index (CI) = 0.0715, Consistency Ratio (CR) = 0.0644 Lineament Density Very high(1) 1 0.323 High(2) 1 1 0.262 moderate(3) 0.5 1 1 0.185 low(4) 0.333 0.5 1 1 0.128 very low(5) 0.333 0.333 0.5 1 1 0.102 Consistency Index (CI) = 0.022047, Consistency Ratio (CR) = 0.019606 LULC Barren Land(1) 1 0.347 Built-up(2) 0.5 1 0.24 Agricultural land(3) 0.333 0.5 1 0.152 shrubs(4) 0.25 0.333 0.5 1 0.107 water body(5) 0.2 0.25 0.333 0.5 1 0.07 snow(6) 0.2 0.2 0.333 0.333 0.5 1 0.049 forest(7) 0.167 0.2 0.25 0.25 0.333 0.5 1 0.035 Consistency Index (CI) = 0.041872, Consistency Ratio (CR) = 0.031721 Normalized Difference Vegetation Index Very high(1) 1 0.327 High(2) 1 1 0.289 Moderate(3) 0.5 0.5 1 0.191 Low(4) 0.333 0.5 0.5 1 0.107 Very low(5) 0.25 0.333 0.333 1 1 0.086 Consistency Index (CI) = 0.0188185, Consistency Ratio (CR) = 0.016734 Rainfall very high(1) 1 0.372 high(2) 0.5 1 0.249 moderate(3) 0.5 0.5 1 0.187 low(4) 0.333 0.5 0.5 1 0.119 very low(5) 0.25 0.333 0.333 0.5 1 0.073 Consistency Index (CI) = 0.0229082, Consistency Ratio (CR) = 0.020371 Relative Relief very high(1) 1 0.325 high(2) 1 1 0.282 moderate(3) 0.5 1 1 0.215 low(4) 0.333 0.333 0.5 1 0.11 very low(5) 0.25 0.25 0.333 0.5 1 0.069 Consistency Index (CI) = 0.0139415, Consistency Ratio (CR) = 0.012398 Stream Power Index Very high(1) 1 0.389 High(2) 0.5 1 0.27 Moderate(3) 0.5 0.5 1 0.196 Low(4) 0.25 0.333 0.333 1 0.096 Very low(5) 0.167 0.2 0.25 0.333 1 0.048 Consistency Index (CI) = 0.033774, Consistency Ratio (CR) = 0.030034 Distance from thrust Below 500(1) 1 0.54 500–1500(2) 0.5 1 0.297 Above 1500(3) 0.333 0.5 1 0.163 Consistency Index (CI) = 0.0046045, Consistency Ratio (CR) = 0.007938 Topographic Wetness Index Very high(1) 1 0.375 High(2) 1 1 0.313 Moderate(3) 0.333 0.5 1 0.159 Low(4) 0.25 0.333 0.5 1 0.097 Very low(5) 0.167 0.2 0.333 0.5 1 0.056 Consistency Index (CI) = 0.009275, Consistency Ratio (CR) = 0.00828191 Source: Calculated by researcher 4. Results The Analytical Hierarchy Process (AHP), which was used to assign weights to different causative factors and their respective classes, was used to carry out the Landslide Hazard Zonation (LHZ). Cell-by-cell weight values were applied to each factor's raster map, and a hazard index equation was then used to integrate the weighted maps. A Landslide Hazard Index (LHI) map was produced as a result of this integration; higher LHI values denote regions that are more vulnerable to landslides, while lower values represent areas that are more stable. The Natural Breaks classification method was used to establish threshold values for categorising the susceptibility levels. Five susceptibility classes; Very Low, Low, Moderate, High, and Very High Hazard zones, were applied to the LHI map. The findings show that while Low Hazard zones make up 23.4% (1460 km2) of the entire study area, Very Low Hazard areas make up 10.5% (658 km²). Spatially, these zones are primarily concentrated in the southern and southwestern parts of the study area, with some smaller pockets appearing along the eastern boundaries. Table 6 Landslide hazard zones Hazard Zone Description Area (in km) Area (%) I Very Low Hazard 658.34 10.54 II Low Hazard 1460.49 23.42 III Moderate Hazard 1863.82 29.84 IV High Hazard 1528.34 24.47 V Very High Hazard 732.26 11.73 Of the entire study area, 29.84% (1863 km2) is in the Moderate Hazard Zone. Spatially, it is primarily located in the central areas, extending to the study area's eastern and northeastern regions. 24.47% (1528 km2), is occupied by the High Hazard Zone, while 11.7% (732.26 km2) is occupied by the Very High Hazard Zone. Within the study area's northern, northeastern, and some central regions are home to the majority of these high-susceptibility zones. Notably, these zones include important areas like Kalpa and Peo in Kalpa tehsil, Nichar in Nichar tehsil, and Nako and Chango in Hangrang district. Additionally, Pooh Tehsil's northern, northeastern, and southern regions are all included in these high-susceptibility zones. 5. Validation In order to evaluate the predictive accuracy and dependability of any Landslide Hazard Zonation (LHZ) map, validation is an essential step. Landslide susceptibility models can be validated using a variety of techniques, such as field-based inventory validation, cross-validation methods, prediction rate curves, and success rate curves (Akgun et al. 2012; Zezere et al. 2017 ). Among these, ROC-AUC analysis is widely preferred due to its independence from threshold selection and its ability to effectively evaluate the discriminatory power of the model (Chung and Fabbri 2003 ). The analysis of the Receiver Operating Characteristic (ROC) curve was used in this study as a reliable statistical method for validation. The ROC curve is a plot which indicates how accurately a model discriminates between classes by comparing the True Positive Rate (sensitivity) and False Positive Rate (specificity) for varying thresholds. It aids in the visualization of the balance between the correctly detected positives and the false detection (Chung and Fabbri 2003 ). Landslide inventory data from the GSI Bhookosh portal was used to verify the findings. This dataset, which includes landslide incidents that have been documented, offered a solid foundation for assessing the predictive accuracy of the model. The model's ability to identify landslide-prone areas was evaluated by contrasting the susceptibility zones shown on the LHZ map with the actual locations of landslides found in the inventory. According to the validation results, the LHZ model had 71% prediction accuracy with an Area Under the Curve (AUC) value of 0.71. According to Regmi et al. ( 2014 ), the AUC value is a comprehensive indicator of model performance. A value of 1 indicates perfect prediction, while a value of 0.5 indicates a model that is no better than chance. The 70% accuracy suggests a good level of reliability for landslide hazard assessment. The validation results verify that the spatial distribution of landslides within the study area has been accurately captured by the Analytical Hierarchy Process (AHP) modelling approach and the chosen causative factors. The results demonstrate how the produced LHZ map can be used practically for disaster risk reduction, land-use planning, and decision-making in areas vulnerable to landslides. To improve the accuracy and dependability of landslide susceptibility assessments, future studies can incorporate high-resolution datasets and real-time landslide inventories. 6. Conclusions The Analytical Hierarchy Process (AHP) as a multi-criteria decision-making method for identifying landslide-prone areas investigation. Weights were assigned to different factors and their corresponding classes using the AHP method. The landslide susceptibility map that was produced provided a thorough evaluation of the various factor classes that were present in the study area. Slope steepness is one of the most important factors influencing the risk of landslides; steeper terrains are more unstable. The analysis suggests that only 12.84% of areas with slopes below 25 degrees are in the High to Very High Hazard zones are generally stable. In contrast, landslide risk is much higher in areas with slopes greater than 35 degrees, with only 8.94% of the area categorised as Very Low to Low Hazard and a resounding 74.46% in the High to Very High Hazard range. This pattern emphasises the clear correlation between the incidence of landslides and increasing slope angle. Additionally, there is a strong correlation between landslide susceptibility and hydrological factors. 29% of the study area is made up of low to very low drainage density areas, which have a comparatively lower risk of landslides. Of these, 60% are classified as Very Low to Low Hazard zones, while only 15.8% are classified as High to Very High Hazard zones. This implies that surface runoff-induced instability is less common in places with low drainage densities. High drainage density areas, on the other hand, exhibit the opposite trend, with only 14.1% of their extent falling into Low Hazard categories and 56.3% of their extent falling into High to Very High Hazard zones. This connection emphasises how concentrated water flow destabilises slopes and encourages landslides. The importance of hydrological conditions in landslide susceptibility is further supported by the Topographic Wetness Index (TWI). The areas with the highest percentage of their extent in hazardous zones are those classified as High TWI (43.35%) and Very High TWI (64.49%), underscoring the part that excessive moisture retention plays in slope failure. Nevertheless, when combined with other elements like slope gradient, Low TWI areas make up the largest percentage (26.82%) of all High Hazard zones, indicating that even moderately wet terrains are vulnerable to landslides. The most stable terrain category is represented by Very Low TWI areas, which contribute the least (8.72%) to landslide-prone zones, while Moderate TWI areas (25.37%) are also very important. The erosive forces' role in landslide susceptibility is further supported by the Stream Power Index (SPI) analysis. The largest percentage of High to Very High Hazard zones are found in High SPI areas (42.19%) and Very High SPI areas (63.15%), suggesting that significant erosional processes are a contributing factor to slope instability. Nonetheless, in absolute terms, the majority of the High Hazard zones (29.37%) are composed of Moderate SPI areas, indicating that moderate stream power is also a major contributor to landslide activity. While Very Low SPI areas have the least impact (6.83%), indicating that they are relatively stable, Low SPI areas also make a significant contribution (18.46%). Lithological characteristics also play a crucial role in landslide susceptibility, as certain rock formations are inherently more prone to failure. With an area of about 769.22 km2, or 34.19% of the entire landslide hazard area, the Haimanta Group makes up the largest portion of the High to Very High Hazard zones. Rakcham Granite (293.53 km² or 13.04%) and the Vaikrita Group (530.54 km² or 23.58%) come next, together accounting for 70.81% of the entire hazardous area. With 40.5% of its total area categorised as High Hazard zones and only 30.5% as Low Hazard zones, the Vaikrita formation in particular shows a high degree of susceptibility, suggesting that there are few stable zones within this lithology. With 47.2% of its land classified as High Hazard and another 31.6% as Moderate Hazard, the Haimanta Group is similarly extremely vulnerable. While the Rakcham Granite formation is a moderately susceptible lithological unit, its hazard distribution is more balanced, with 30% of its area categorised as High Hazard and nearly equal portions under Moderate and Low Hazard categories. With 52.6% of its area categorised as Low Hazard and just 19.1% as High Hazard, the Sanugba formation stands out as one of the most stable lithological units. However, with 48.1% of its area falling under High Hazard zones and only 21.9% under Low Hazard, the Jeori-Wangtu Bandeissic Complex is one of the most unstable formations in the study area and shows significant landslide vulnerability. Land use and land cover (LULC) have a significant impact on landslide susceptibility; the most affected areas are shrub-covered and barren lands, which together make up almost 87% of the High and Very High Hazard areas. Barren land makes up 40.04% and shrubland 46.51%, suggesting that barren landscapes and vegetated but brittle slopes are especially vulnerable to landslides because of things like loose soil, steep terrain, and the absence of deeply rooted vegetation to provide stability. Relative Relief (RR) exhibits a strong correlation with landslide hazard, as areas with higher elevation differences experience greater instability. The highest percentage (66.72%) of High to Very High Hazard zones is found in Very High RR areas, indicating that landslides are most likely to occur in steep, elevated terrain. Even moderately elevated areas are vulnerable to landslides, as evidenced by the fact that moderate RR zones contribute the most in absolute terms (36.84%) to all high hazard areas, despite having a lower relative proportion. High RR areas also make up a significant portion of high hazard zones (53.91%). Relatively flat landscapes are more stable, as evidenced by the Low RR (15.79%) and Very Low RR (4.62%) areas contributing the least to high hazard zones. Lastly, landslide susceptibility clearly shows the impact of structural factors, especially Lineament Density (LD). Significantly, 54.9% of High LD areas and 67.8% of Very High LD areas are located in High to Very High Hazard zones, indicating that landslides are more likely to occur in structurally weak zones. Since they encompass a greater area within the study region, Moderate LD areas make the largest absolute contribution to High to Very High Hazard zones. Given that 38.1% of Moderate LD, 17.6% of Low LD, and only 6.8% of Very Low LD areas are classified as high-risk zones, it is clear that hazard susceptibility decreases with lower LD values. This suggests that areas with fewer structural disturbances are typically more stable. The study concludes that steep slopes, high drainage density, particular lithological formations, and structurally weak zones are the main factors influencing landslide hazard. These results highlight how crucial it is to combine various geospatial and geomorphological factors in order to effectively assess the hazard of landslides. This study's conclusions can help with disaster mitigation plans, infrastructure development in landslide-prone areas, and land-use planning, resulting in a better approach to risk management and sustainable development. Declarations Author Contribution "Dr. Ram Lal and Mr. Gulshan Verma both have made substantial contributions to the conception or design of the work, analysis, or interpretation of data. Both the authors reviewed the manuscript." References AkgunA, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B. An easy to- use MATLAB program for landslide susceptibility mapping. Comput Geosci. 2012;38(1):23–34. Anbalagan R. Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol. 1992;32(4):269–77. Ayalew L, Yamagishi H. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology. 2005;65(1–2):15–31. Beven K, Kirkby MJ. A physically based, variable contributing area model of basin hydrology. Hydrol Sci J. 1979;24(1):43–69. Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB. Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci. 2011;45:199–211. Chung CJF, Fabbri AG. Validation of spatial prediction models for landslide hazard mapping. Nat Hazards. 2003;30(3):451–72. Costanz D, Chacón J, Conoscent C, Irigara C, Rotigliano E. Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy). Landslides. 2014;11:639–53. Cruden DM, Varnes DJ. (1996) Landslide types and processes Landslides, investigation and mitigation: special report. 247:36–75. Dai C, LeeCF, Li J, Xu ZW. Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol. 2001;40:381–91. Da FC, Lee CF, Zhan XH. GIS-based geo-environmental evaluation for urban land-use planning: a case study. Eng Geol. 2001;61(4):257–71. Dahal RK, Hasegawa S. Representative rainfall thresholds for landslides in the Nepal Himalaya. Geomorphology. 2008;100(3–4):429–43. Dormann CF, Elith J, Bacher S, Buchman C, Carl G, Carré G, Lautenbac S. (2013) Collinearity: A review of methods to deal with it and a simulation study evaluating their performance.Ecography 36(1):27–46. Froude J, Petley DN. Global fatal landslide occurrence from 2004 to 2016. Nat Hazards Earth Syst Sci. 2018;18(8):2161–81. Glade T. Establishing the frequency and magnitude of landslide-triggering rainstorm events in New Zealand. Environ Geol. 1998;35(2–3):160–74. Gökceoglu C, Aksoy HÜ. Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Eng Geol. 1996;44(1–4):147–61. Gorsevski PV, GesslerPE, Foltz RB, Elliot WJ. Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans GIS. 2006;10(3):395–415. Gujarati DN, Porter DC. Basic econometrics. 5th ed. McGraw-Hill; 2009. Gupta RP, Joshi BC. Landslide hazard zoning using the GIS approach—a case study from the Ramganga catchment. Himalayas Eng Geol. 1990;28(1–2):119–31. Guzzetti F, Carrara A, Cardinali M, Reichenbach P. Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology. 1999;31(1–4):181–216. Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M. Estimating the quality of landslide susceptibility models. Geomorphology. 2006;81(1–2):166–84. Highland LM, Bobrowsky P. (2008) The landslide handbook-A guide to understanding landslides (No 1325) US Geological Survey. Horton RE. Drainage-basin characteristics. Trans Am Geophys union. 1932;13(1):350–61. Hsu PH, Su WR, Chan CC. Hazards analysis and prediction from remote sensing and GIS using spatial data mining and knowledge discovery: A case study for landslide hazard zonation. Earth Resour Environ Remote Sensing/GIS Appl II. 2011;8181:164–71. Hungr O, Leroueil S, Picarelli L. The Varnes classification of landslide types, an update. Landslides. 2014;11(2):167–94. Hussain MA, Chen Z, Zho Y, MeenaSR, Ali N, Sha U. (2025) Landslide susceptibility mapping using artificial intelligence models: a case study in the Himalayas. Landslides:1–15. Kayasth P, De DhitalMR F. Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal. Comput Geosci. 2013;52:398–408. Kutner MH, Nachtsheim CJ, Neter J, Li W. (2005) Applied linear statistical models.McGraw-hill. Lee S. Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens. 2005;26(7):1477–91. Lee S, Evangelista DG. Earthquake-induced landslide-susceptibility mapping using an artificial neural network. Nat Hazards Earth Syst Sci. 2006;6(5):687–95. Lee S, Pradhan B. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 2007;4(1):33–41. Moore ID, Grayson RB, Ladson AR. Digital terrain modeling: A review of hydrological, geomorphological, and biological applications. Hydrol Process. 1991;5(1):3–30. Nadim F, Kjekstad O, Peduzzi P, Herold C, Jaedicke C. Global landslide and avalanche hotspot. Landslide. 2006;3:159–73. Pourghasemi HR, Pradhan B, Gokceoglu C. (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural hazards63:965–996. Pradhan B, Lee S. Landslide susceptibility assessment and factor effect analysis: Back-propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environ Model Softw. 2010;25(6):747–59. Regmi AD, Yoshida K, Nagata H, Pradhan B, Pourghasemi HR. Landslide susceptibility mapping along Bhalubang-Shiwapur area of mid-western Nepal using frequency ratio and conditional probability models. J Mt Sci. 2014;11(5):1266–85. Saaty TL. The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill; 1980. Saaty TL. The analytic hierarchy process (AHP). J Oper Res Soc. 1980;41(11):1073–6. Saha AK, Gupta RP, Arora MK. GIS-based landslide hazard zonation in the Bhagirathi (Ganga) valley, Himalayas. Int J Remote Sens. 2002;23(2):357–69. Sah AK, Gupta RP, Sarkar I, Arora MK, Csaplovics E. An approach for GIS-based statistical landslide susceptibility zonation- a case study in the Himalayas. Landslides. 2005;2:61–9. Saha S, Majumdar P, Bera B. Deep learning and benchmark machine learning based landslide susceptibility investigation, Garhwal Himalaya (India). Quaternary Sci Adv. 2023;10:100075. Shano L, Raghuvanshi TK, Meten M. Landslide susceptibility evaluation and hazard zonation techniques–a review. Geoenvironmental Disasters. 2020;7:1–19. Shukla DP, Gupta S, Dubey CS, Thakur M. (2016) Geo-spatial Technology for Landslide Hazard Zonation and prediction.In: Marghany M, editor Environmental applications of remote sensing,IntechOpen, pp 281–308. Singh M, Khajuria V, Singh S, Singh K. Landslide susceptibility evaluation in the Beas River Basin of North-Western Himalaya: A geospatial analysis employing the Analytical Hierarchy Process (AHP) method. Quaternary Sci Adv. 2024;14:100180. Süzen ML, Doyuran V. A comparison of the GIS-based landslide susceptibility assessment methods: Multivariate versus bivariate. Environ Geol. 2004;45(5):665–79. Sweta K, Goswami A, Peethambaran B, Bahuguna IM, Rajawat AS. Landslide susceptibility zonation around Dharamshala, Himachal Pradesh, India: an artificial intelligence model–based assessment. Bull Eng Geol Environ. 2022;81(8):310. Varnes DJ. (1954) Landslide types and processes In: Eckel EB, editor Landslides and engineering practice, special report 28 Highway research board National Academy of Sciences, Washington, DC 24, pp 20–47. Varnes DJ. Landslide hazard zonation: a review of principles and practice. Quetigny, Paris: Darantiere; 1984. Wang H, Wang G, Wang F, Sassa K, Chen Y. (2008) Probabilistic modeling of seismically triggered landslides using Monte Carlo simulations Landslides, 5:387–95. Wieczorek G, Mandrone G, DeCola L. (1997) The influence of hillslope on debris-flow initiation. In: Debris-Flow hazard mitigation: mechanics, prediction and assessment ASCE, USA, pp 21–31. Yalcin A. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. CATENA. 2008;72(1):1–12. Yesilnacar E, Topal T. Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol. 2005;79(3–4):251–66. Youssef K, Shao K, Moon S, Bouchard LS. Landslide susceptibility modeling by interpretable neural network. Commun Earth Environ. 2023;4(1):162. Zezere JL, Pereira S, Melo R, Oliveira SC, GarciaRA. Mapping landslide susceptibility using data-driven methods. Sci Total Environ. 2017;589:250–67. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":339424,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eStudy Area\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7092462/v1/9163f9300da86b4a23456222.jpeg"},{"id":91861951,"identity":"485650ba-d614-4f21-8f02-9b274a7173d7","added_by":"auto","created_at":"2025-09-22 12:42:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":170713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMethodological flow chart\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7092462/v1/37e4c7b2f4a92d448d81e9d0.png"},{"id":91861952,"identity":"86f5a355-1412-4141-a235-715dad84e0e4","added_by":"auto","created_at":"2025-09-22 12:42:45","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":398633,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFactors that the present research used to determine landslide hazard areas, (A) Slope, (B) Aspect, (C) Relative relief, (D) Drainage density, (E) Stream Power Index, (F) Topographic Wetness Index\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7092462/v1/9da1ad08ae9f85c2f8db30c9.jpeg"},{"id":91862325,"identity":"1c32f02b-ef94-4859-894e-a1d7559a2b07","added_by":"auto","created_at":"2025-09-22 12:50:45","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":523966,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFactors that the present research used to determine landslide hazard areas, (G) Rainfall, (H) Lithology, (I) Lineament density, (J) Distance from Thrust, (K) LULC, (L) NDVI\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7092462/v1/f18054500bc2a14e8d946a1a.jpeg"},{"id":91862326,"identity":"495dc3f5-fa51-4a51-a904-7f35d117f142","added_by":"auto","created_at":"2025-09-22 12:50:46","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":602143,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLandslide hazard Zonation map\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7092462/v1/7593108ce8950e638a13866e.jpeg"},{"id":91861955,"identity":"3265d071-a97e-489c-af26-cf89f739f806","added_by":"auto","created_at":"2025-09-22 12:42:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":21624,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eValidation through ROC – AUC\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7092462/v1/0d98b0951c6c59db17eb9993.png"},{"id":91865813,"identity":"c9f9096c-1cf5-4b77-a70a-688a6227fe51","added_by":"auto","created_at":"2025-09-22 13:14:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4171140,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7092462/v1/5c1db3a8-335b-48f8-9d4b-5dacda26f51a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Geospatial Assessment of Landslide Hazard in Kinnaur District Using AHP-Based Multi-Criteria Decision Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLandslides represent a major natural hazard defined by the movement of rock, soil, and other debris along a slope due to gravitational forces. Numerous scholars have studied landslides extensively, and they have all provided definitions based on their disciplinary backgrounds and areas of study. Varnes (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1954\u003c/span\u003e) gave one of the first systematic definitions of landslides, defining them as \u0026ldquo;the movement of rock, debris, or earth down a slope as a result of gravity\u0026rdquo;. His classification system has been widely used in geological and geotechnical research and served as the basis for landslide studies. Cruden and Varnes (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) expanded on this by underscoring the significance of material composition and movement mechanisms. In order to standardise landslide studies across different disciplines, this definition was important. To further elaborate on these ideas, Highland and Bobrowsky (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) distinguished landslides from other kinds of slope failures by defining them as a particular type of mass wasting. They emphasised that a number of things, such as precipitation, seismic activity, and human activity, can cause landslides. In order to bring the Varnes landslide classification up to date with geological and geotechnical terminology, Hungr et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) refined the material categories. To increase the clarity of hazard assessments, they broadened the classification to include 32 different landslide types. They suggested composite naming based on movement transitions rather than treating complex landslides as a distinct category. Their updated system made translations easier for worldwide use while guaranteeing backward compatibility with earlier classifications. By improving landslide hazard studies, these changes increased the classification's applicability in risk management and geotechnical engineering. The occurrence of landslides and their complexity are clarified by all of these definitions and descriptions, which highlight the significance of evaluating these risks as a component of disaster risk management and responsible land use planning.\u003c/p\u003e\u003cp\u003eEvery year, landslides result in significant financial losses as well as fatalities, making them a global threat. Some areas, such as India, Nepal, Tajikistan, and Colombia, are especially vulnerable to landslides, with death rates of more than one person per 100 km\u0026sup2; per year (Nadim et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Because of its steep terrain, active tectonics, and abundant monsoonal precipitation, the Indian Himalayan region is particularly susceptible to landslides. Froude and Petley (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that precipitation was the main cause of 477 of the 580 landslide events that occurred in the Indian Himalayas between 2004 and 2017, accounting for 14.52% of all landslides that have been reported globally. Delineating high-risk zones becomes essential for effective hazard mitigation because mountainous terrain is inherently unstable.\u003c/p\u003e\u003cp\u003eAn essential tool for locating and categorising regions according to their landslide hazard is the Landslide Hazard Zonation (LHZ). In order to help policymakers, urban planners, and disaster management authorities create effective mitigation strategies, the main goal of LHZ is to identify areas with different levels of hazard, from very low to very high susceptibility. Planning for sustainable land use, creating early warning systems, and putting risk reduction strategies into action all depend on an accurate LHZ map. Therefore, a thorough LHZ assessment that takes into account several conditioning factors is essential to reducing the negative effects of landslides and guaranteeing both human and environmental safety.\u003c/p\u003e\u003cp\u003eHeuristic, statistical, and machine learning-based approaches are the three categories into which LHZ methodologies fall. Heuristic techniques mostly depend on the opinion of experts and field observations, while statistical techniques, like logistic regression models and the frequency ratio etc. create empirical connections between past landslide events and different causes to create susceptibility maps (Shano et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As computational methods have advanced, machine learning models have been widely used to increase reliability of predictions. Examples of these methods include Artificial Neural Networks (Hsu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sweta et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Youssef et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Support Vector Machines (SVM) (Shukla et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hussain et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Saha et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Analytical Hierarchy Process (AHP), one of the popular semi-quantitative methods for LHZ, is especially well-known for its methodical framework in multi-criteria decision-making.\u003c/p\u003e\u003cp\u003eSaaty (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) introduced the Analytical Hierarchy Process (AHP), a structured decision-making methodology that evaluates the relative significance of various landslide conditioning factors through pairwise comparisons. By using a consistency ratio check, this method incorporates expert judgment while preserving uniformity in weight assignment. Because AHP can be used in data-scarce regions and is flexible in incorporating multiple conditioning factors, it has been widely used in LHZ studies. AHP offers a quantitative framework for evaluating hazard susceptibility by allocating relative weights to various landslide causative factors. This makes it easier to create hazard maps that divide regions into zones with different degrees of danger, from low to high. AHP's reliance on researcher knowledge adds a certain amount of subjectivity, but consistency checks, and sensitivity analysis greatly increase its dependability. Additionally, by facilitating the spatial analysis and visualisation of areas that are prone to hazards, the integration of AHP with Geographic Information Systems (GIS) increases its efficacy.\u003c/p\u003e\u003cp\u003eThe usefulness of AHP in landslide susceptibility assessments in a variety of geographical contexts has been shown in numerous studies. Saha et al. (2005), for example, used AHP in the Bhagirathi Valley, India, combining several causative factors for mapping the LHZ. Similar to this, Yalcin (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) used AHP in Turkey to show how effective it is at categorising areas that are prone to hazards. Pourghasemi et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) further validated the applicability of AHP in hazard assessment by using it for landslide susceptibility mapping in Iran. Furthermore, an AHP-based study in Nepal's Tinau watershed was carried out by Kayastha et al. (2013), confirming its dependability in hazard classification. Dai et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) expanded the use of the Analytical Hierarchy Process (AHP) in China, applying it to urban land use planning with a special emphasis on landslide susceptibility. AHP-based LHZ research was carried out in the Indian Himalayas more recently by Singh et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who confirmed the method's applicability for challenging terrains and achieved high predictive accuracy. These studies highlight how versatile AHP is in different geographic settings and how important it is for managing landslide hazards, especially in high-risk areas.\u003c/p\u003e"},{"header":"2. Study Area","content":"\u003cp\u003eThe present study focuses on the Upper Satluj River Basin, located within the Kinnaur district of Himachal Pradesh. The Satluj River Basin includes the whole Kinnaur district except for the northeastern part of Sangla tehsil. The entire study area is roughly 6245 km\u003csup\u003e2\u003c/sup\u003e, excluding this northeastern section that falls into the Yamuna Basin. With elevations ranging from 1193 meters in the lower valleys to 6725 meters in the high-altitude peaks, the region is distinguished by extremely rugged and mountainous topography, resulting in a relative elevation variation of 5532 meters. The main drainage system is the Satluj River, which rises in the Tibetan Plateau and has the Spiti, Baspa, Tidong and Ropa rivers as its principal tributaries. The geomorphology and water resources of the area are significantly shaped by these hydrological systems.\u003c/p\u003e\u003cp\u003eThe climate of Kinnaur is varied, with temperate climate at lower elevations to frigid desert features in the higher reaches. The annual precipitation varies from 500 to 1200 mm, with snowfall making up the majority of precipitation at higher elevations, particularly in the winter. Significant differences in vegetation and land cover across various altitudinal zones are a result of the monsoonal influence gradually waning from west to east. Coniferous forests, which are mostly made up of pine, deodar and fir predominate in the lower and mid-altitude areas. On the other hand, alpine meadows, arid rocky terrains, and vast glacial landscapes are features of the higher elevations. Furthermore, horticultural pursuits and apple orchards are common in valley areas like Kalpa and Sangla, greatly boosting the local agrarian economy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Upper Satluj Basin is extremely vulnerable to earthquakes and frequent landslides due to its geological location within the Himalayan orogenic belt. The geotechnical stability of the area is greatly impacted by the Kanwar lithological group, which is prominent in this area and is mainly composed of quartzite and black shale with nodules. The region's dynamic geological processes and complex geomorphological evolution make it a valuable location for research on environmental sustainability, natural hazard susceptibility, and landscape evolution.\u003c/p\u003e\u003cp\u003eBecause of its rough terrain and harsh climate, the sparsely populated Kinnaur district poses serious challenges to human habitation. Nonetheless, the study area contains a number of noteworthy communities, such as Sangla, Kalpa, Pooh, and Reckong Peo (the district headquarters). A sizable portion of the local population is made up of the indigenous Kinnaura tribes, who are renowned for their distinctive socioeconomic practices, rich cultural heritage, and traditional way of life. Topographical limitations, climatic hardship, and geographic isolation have all impacted the region's land-use patterns and development patterns, making sustainable environmental management and hazard mitigation techniques necessary.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eIn this study, some of the steps involved in developing landslide hazard zonation (LHZ) include selecting relevant causative factors, assessing for multicollinearity, allocating weights using the Analytical Hierarchy Process (AHP), and validating the model using Receiver Operating Characteristic-Area under the Curve (ROC-AUC) analysis. The methodology used in the current study is explained below.\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Causative Factors:\u003c/h2\u003e\n \u003cp\u003eLandslides are a major geological hazard that cause significant damage to infrastructure, disrupt human settlements, and lead to loss of life globally. Geological, geomorphological, hydrological, and climatic conditions are among the many natural and man-made elements that interact intricately to cause them. Deforestation, unplanned urbanisation, and climate change have all contributed to an increase in the frequency and size of landslides in recent years (Guzzetti et al. \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e; Varnes \u003cspan class=\"CitationRef\"\u003e1984\u003c/span\u003e; Dai et al. \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eA crucial part of disaster risk management is landslide hazard zoning (LHZ), which offers a scientific foundation for mitigation and land-use planning. It involves identifying areas prone to landslides by analyzing various causative factors and assigning hazard levels accordingly. For LHZ, a variety of methods have been created, from data-driven and machine-learning models to heuristic approaches (Lee \u0026amp; Pradhan \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTwelve causative factors were chosen for this investigation with regard to their impact on slope stability in the study area and their applicability in earlier studies. A multicollinearity test was performed to remove highly correlated variables in order to guarantee the model\u0026apos;s dependability. The factors were given weights based pairwise comparisons using the Analytical Hierarchy Process (AHP), which was created by Saaty in 1980. Lastly, the predictive performance of the model was evaluated by validating it using the Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) method.\u003c/p\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1 Topographic Factors\u003c/h2\u003e\n \u003cp\u003eSlope stability is greatly impacted by topography since it dictates surface runoff, soil properties, and gravitational forces. In order to determine the dominant topographic factors in landslide mapping analysis, a study was conducted and presented in this paper.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ea. Slope\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSlope gradient is a crucial element in landslide hazard because steeper slopes are subject to greater gravitational force, which decreases the stability of soil and rock masses. Particularly during periods of intense rainfall or seismic activity, slopes that have an angle larger than the internal friction limit of the material are extremely prone to failure (Gokceoglu \u0026amp; Aksoy 1996; Yalcin \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). Steeper slopes also encourage faster surface runoff, which increases the possibility of erosion while decreasing water infiltration (Ayalew \u0026amp; Yamagishi \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Lee \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Pradhan \u0026amp; Lee \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). Slope has been divided into five classes i.e. less than 15˚, 15˚-25˚, 25˚-35˚, 35˚-45˚, and more than 45˚. Less than 15˚ class has the lowest percentage of the area covered, at 12%, while 25˚ to 35˚ class has the highest percentage, at 30%.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eb. Aspect\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBy controlling vegetation distribution and moisture retention, two factors that are crucial for determining soil strength and slope stability, slope aspect has a major influence on the initiation of landslides. Slope failure risk is changed by aspect differences, which also affect soil water content and root reinforcement. Additionally, when influenced by dominant wind patterns, rainfall distribution can result in uneven precipitation levels across slope orientations, which in turn can alter the susceptibility to landslides (Wieczorek et al. \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e). Furthermore, monsoonal winds and orographic effects lead to spatial differences in rainfall, which increases the pore water pressure and saturation on windward slopes, making them more susceptible to landslides (Saha et al. 2005). Its importance in landslide hazard assessments is highlighted by the interaction of slope aspect, geological features, vegetation patterns, and climatic variables, especially in the Himalayan region.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eData sources of landslide conditioning factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSr. no.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScale/ Resolution\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlope gradient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALOS PALSAR DEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.5 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlope aspect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALOS PALSAR DEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.5 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelative Relief\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALOS PALSAR DEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.5 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTopographic wetness Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALOS PALSAR DEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.5 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStream Power Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALOS PALSAR DEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.5 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrainage Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALOS PALSAR DEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.5 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLithology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeological survey of India\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1:50000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLineament\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeological survey of India\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1:50000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThrust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeological survey of India\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1:50000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIMD Gridded Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25 ˚\u0026times; 0.25 ˚\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSentinal-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSentinal-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ec. Relative relief\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWhen evaluating the ruggedness of a given terrain, relative relief\u0026mdash;which is the elevation difference within that area\u0026mdash;is a crucial consideration. Steep gradients and deep valleys are characteristics of high relative relief regions, which make them more vulnerable to landslides because of increased rates of erosion and gravitational stress. According to studies by Anbalagan (\u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e), Saha et al. (\u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e), and others, regions with a large elevation contrast are more likely to experience mass movements as a result of differential weathering effects and gravitational pull. Five classes\u0026mdash;very low, low, moderate, high, and very high\u0026mdash;have been established for the relative relief map.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2 Hydrological Factors\u003c/h2\u003e\n \u003cp\u003eBy changing soil moisture levels, decreasing shear strength, and accelerating erosion, hydrological conditions can cause slope instability.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ea. Drainage Density\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026ldquo;Drainage density defines length of streams per unit of drainage area\u0026rdquo; (Horten 1932). Because increased water movement causes erosion and slope undercutting, high drainage density is frequently associated with increased slope instability (Ayalew \u0026amp; Yamagishi \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). Furthermore, in steep terrain, concentrated water flow can cause channelised landslides and debris flows (Gorsevski et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Lee \u0026amp; Pradhan \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eb. Stream Power Index (SPI)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe erosive capacity of flowing water is measured by the Stream Power Index (SPI), which is dependent on slope gradient and discharge. Areas with higher SPI values are those where concentrated water flow puts a lot of force on slopes, raising the risk of erosion and slope failure. Prior research by Moore et al. (\u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e), Costanzo et al. (2014) has demonstrated the importance of SPI in landslide-prone areas, especially those with high precipitation.\u003c/p\u003e\n \u003cp\u003eFollowing Moore et al. (\u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e), the stream power index (SPI) was calculated and is shown below:\u003c/p\u003e\n \u003cp\u003eSP\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{I}=\\text{ln}\\:\\left(\\left({\\text{A}}_{\\text{s}}\\right)\\text{*}\\left({\\text{t}\\text{a}\\text{n}}_{{\\beta\\:}}\\right)\\:\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003ewhere tan\u0026beta; is the slope, As is the flow accumulation, and ln is the natural log.\u003c/p\u003e\n \u003cp\u003eUtilizing the method of natural break, the SPI map has been divided into five classes, ranging from very low to very high.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ec. Topographic Wetness Index (TWI)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTWI measures the amount of water that accumulates in a landscape, which affects soil moisture content and the likelihood of slope failure. Zones where water tends to accumulate, resulting in prolonged saturation and decreased shear strength, are indicated by high TWI values. TWI is a crucial factor in forecasting shallow landslides, especially in areas with heavy rainfall, according to Beven and Kirkby (\u003cspan class=\"CitationRef\"\u003e1979\u003c/span\u003e), Gokceoglu and Aksoy (1996), and Lee \u0026amp; Evangelista (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Topographic wetness index (TWI) has been derived after Moore et al. (\u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e) given below-\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:\\text{T}\\text{W}\\text{I}=\\text{ln}\\left(\\frac{{\\text{A}}_{\\text{s}}}{{\\text{t}\\text{a}\\text{n}}_{{\\beta\\:}}}\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere tan\u0026beta; is the slope, As is the flow accumulation, and ln is the natural log.\u003c/p\u003e\n \u003cp\u003eUtilizing the method of natural break, the TWI map has been divided into five classes, ranging from very low to very high.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ed. Rainfall\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLandslides are mostly caused by heavy precipitation because it saturates the soil, raising the pressure in the pores and decreasing shear strength. Slope failures are frequently caused by intense or protracted rainfall events, especially in areas with loose or fractured rock formations. Glade (\u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e), Guzzetti et al. (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e), Dahal and Hasegawa (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e) have shown that landslide occurrences are strongly correlated with areas that experience heavy rainfall either annually or seasonally. The study area\u0026apos;s rainfall map was produced using IMD Gridded data.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.3 Geological and Structural Factors\u003c/h2\u003e\n \u003cp\u003eSlope stability is greatly impacted by geological characteristics and structural elements, which affect failure mechanisms and material strength\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ea. Lithology group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe resistance of various lithological formations to erosion and weathering varies. Because of their low cohesiveness and vulnerability to water infiltration, weak rock types like schist and shale are particularly vulnerable to landslides (Gupta and Joshi \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e). Twelve lithological groups\u0026mdash;Kanawar, Lilang, Vaikrita, Rampur (Naraul), Jeori-Wangtu banded Gneissic complex, Kulu, Sanugba, Kuling, Haimanta, Jutogh, Rakcham Granite, and Nako Granite\u0026mdash;are identified in the lithology map created for this study using data from the Geological Survey of India.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eb. Lineaments Density\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFaults and fractures are examples of lineaments that produce weak spots that make slope failure easier. Active tectonic zones, where stress accumulation causes frequent slope failures, are frequently linked to areas with high lineament densities. It has been demonstrated by Gupta \u0026amp; Joshi (\u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e), Saha et al. (\u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e), and Pradhan \u0026amp; Lee (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) that areas that are faulted and fractured are much more vulnerable to landslides. Lineament Density has been divided into 5 categories ranges from very low to very high on the basis of natural break system and low density class covers the highest area which is approx. The lowest area is roughly covered by 28% and very high class. 6 percent of the total.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ec. Distance from thrust\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eZones of extreme deformation, where rock masses are severely sheared and fractured, are represented by thrust faults. Due to their inherent instability, these areas are frequently the site of landslides (Anbalagan \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e). For instance, there are many landslides connected to significant thrust faults in the Himalayas. Distance from the thrust factor, which is divided into three categories\u0026mdash;0\u0026ndash;50 m, 50\u0026ndash;200 m, and more than 200 m\u0026mdash;has been taken into account in this study.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.4 Anthropogenic factors\u003c/h2\u003e\n \u003cp\u003eSlopes\u0026apos; inherent stability is altered by human activity, which makes them more vulnerable to landslides.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ea. Land Cover and Land Use (LULC)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBy changing vegetation cover and water infiltration rates, LULC changes\u0026mdash;such as deforestation, urbanisation, and agricultural expansion\u0026mdash;have an effect on slope stability. Human-modified landscapes are more vulnerable to landslides because of decreased root binding strength and increased surface runoff, as shown by Saha et al. (2005), Pradhan \u0026amp; Lee (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). Utilizing Sentinal 2A and 2b satellite imagery, map of land use and landcover have been produced, with categories such as barren land, shrubs, forests, agri-land, builtup, snow and glaciers, and water bodies.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eb. Normalised Difference Vegetation Index\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNDVI is a remote sensing-derived index that measures vegetation health. While low NDVI values signify bare or degraded land, raising the risk of landslides, dense vegetation stabilises slopes by strengthening soil and lowering surface runoff (Gupta and Joshi \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e; Lee and Pradhan \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). NDVI maps have been created using sentinel satellite images.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Multicollinearity\u003c/h2\u003e\n \u003cp\u003eMulticollinearity is a statistical condition in which two or more independent variables show a high degree of correlation, making it difficult to determine how each of them affects the dependent variable separately (Gujarati and Porter \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). Multicollinearity can skew the contribution of individual conditioning factors in landslide susceptibility modelling, making it challenging to identify the variables that actually affect landslide occurrences. Strong correlations can result in skewed estimations and unstable predictions, which is especially problematic for models that depend on weight assignments, such as the Analytical Hierarchy Process (AHP), Logistic Regression, and Frequency Ratio models (Dormann et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eValues for the Variance Inflation Factor (VIF) and Tolerance were calculated in order to evaluate multicollinearity among the 12 landslide conditioning factors that were chosen. Tolerance (1/VIF) values below 0.1 indicate high redundancy, while VIF values above 10 indicate severe collinearity (Kutner et al. 2004; Wang et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). The following are the outcomes (Table no. 3.2):\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eMulticollinearity Analysis of Causative Factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCollinearity Statistics\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTolerance\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAspect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrainage Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLineament Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLithology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelative Relief\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance from Thrust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe lack of significant multicollinearity among the chosen factors is confirmed by the fact that all VIF values are less than 2 and that tolerance values stay above 0.8. Aspect (1.03) showed the lowest collinearity, while SPI (1.21) and TWI (1.21), which showed the highest VIF values, indicated a moderate correlation. The variables can be used in the landslide hazard zonation (LHZ) model without risk of distortion because none of the VIF values are greater than 5, which is typically regarded as acceptable in regression modelling (Kutner et al. 2004; Dormann et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Landslide Hazard Zonation Using the Analytical Hierarchy Process (AHP)\u003c/h2\u003e\n \u003cp\u003eSaaty (\u003cspan class=\"CitationRef\"\u003e1980\u003c/span\u003e) created the Analytical Hierarchy Process (AHP), a multi-criteria decision-making (MCDM) technique that is frequently applied in landslide hazard zonation (LHZ) to systematically allocate weight to different causative factors. This technique guarantees a structured approach to decision-making in hazard analysis by enabling a pairwise comparison of specific factors based on expert judgement. AHP is structured into several steps:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ea. Constructing the Pairwise Comparison Matrix\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eA pairwise comparison matrix is constructed to evaluate the relative importance of each causative factor. The importance of one factor over another is assigned based on Saaty\u0026rsquo;s fundamental scale of judgment, which ranges from 1 (equal importance) to 9 (extreme importance) (Saaty, \u003cspan class=\"CitationRef\"\u003e1980\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eFundamental scale of judgment\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEqual importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrong importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery strong importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtreme importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2, 4, 6, 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermediate values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\u003cem\u003eSource\u003c/em\u003e: Saaty, \u003cspan class=\"CitationRef\"\u003e1980\u003c/span\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eb. Pairwise Comparison Matrix Normalisation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eA normalised matrix is obtained by dividing each matrix element by the sum of the columns. The normalised matrix\u0026apos;s rows are averaged to produce the priority vector, also known as the weightage.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ec. Check for Consistency with CI and CR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo ensure that expert judgments are logically consistent, a Consistency Index (CI) and Consistency Ratio (CR) are calculated.\u003c/p\u003e\n \u003cp\u003eThis is how the Consistency Index (CI) is calculated:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:CI=\\frac{{\\lambda\\:}_{max}-n}{n-1}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere \u0026lambda;_max is the largest eigenvalue of the matrix, and n is the number of factors.\u003c/p\u003e\u003cp\u003eThe following provides the Consistency Ratio (CR):\u003c/p\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:CR=\\frac{CI}{RI}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere RI (Random Index) is a standard reference value that depends on the matrix size, as shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cstrong\u003eRandom Index (RI) Values\u003c/strong\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\u003cp\u003eNo. of variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eRI\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003eSource\u003c/em\u003e: Saaty, \u003cspan class=\"CitationRef\"\u003e1980\u003c/span\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe pairwise comparisons are regarded as consistent if CR\u0026thinsp;\u0026le;\u0026thinsp;0.1. The judgements must be examined and updated if CR is greater than 0.1.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ed. Final Weights and Landslide Hazard Zonation Map Derivation\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOnce the weights are finalized, they are assigned to each causative factor in the GIS environment and calculate landslide hazard index using the following equation:\u003c/p\u003e\u003cp\u003e\u003cem\u003eLHI\u0026thinsp;=\u0026thinsp;\u0026Sigma; Weight of factor (w\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e) \u0026times; Weight of factor classes (w\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWhere w\u003csub\u003eij\u003c/sub\u003e denotes weight of ith class of factor jth.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cstrong\u003eAHP scores of factors, classes, CR and CI\u003c/strong\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\u003cp\u003eFactors and Classes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eWeights\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"15\"\u003e\u003cp\u003eFactors comparison\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eSlope (1)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eLithology (2)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eLineaments (3)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eDistance from thrust (4)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eRainfall (5)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eLULC (6)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eDD (7)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eRR (8)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eNDVI (9)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eSPI (10)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eTWI (11)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eAspect (12)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003eConsistency Index (CI)\u0026thinsp;=\u0026thinsp;0.1407, Consistency Ratio (CR)\u0026thinsp;=\u0026thinsp;0.091673\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003e\u003cstrong\u003eFactors Classes Comparison\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003e\u003cstrong\u003eSlope Category(in degrees)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003e\u0026gt;\u0026thinsp;45 (1)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.457\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003e35\u0026ndash;45 (2)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003e25\u0026ndash;35 (3)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003e15\u0026ndash;25 (4)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;15 (5)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003eConsistency Index (CI)\u0026thinsp;=\u0026thinsp;0.019335, Consistency Ratio (CR)\u0026thinsp;=\u0026thinsp;0.017194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003e\u003cstrong\u003eLithology\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eJeori (1)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eHaimanta(2)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003ekanwar(3)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eSanugba(4)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eKadcham(5)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eVaikrita (6)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eJutogh(7)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eRampur(8)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eKulu(9)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eLilang(10)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eKuling(11)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eNako(12)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003eConsistency Index (CI)\u0026thinsp;=\u0026thinsp;0.06531, Consistency Ratio (CR)\u0026thinsp;=\u0026thinsp;0.042545\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003e\u003cstrong\u003eAspect\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eS(1)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eSE(2)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eSW(3)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eW(4)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eE(5)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eNE(6)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eN(7)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eNW(8)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eFlate(9)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003eConsistency Index (CI)\u0026thinsp;=\u0026thinsp;0.046045, Consistency Ratio (CR)\u0026thinsp;=\u0026thinsp;0.031817\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003e\u003cstrong\u003eDrainage Density\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eVery high(1)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.433\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eHigh(2)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.298\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eModerate(3)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eLow(4)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eVery low(5)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003eConsistency Index (CI)\u0026thinsp;=\u0026thinsp;0.0715, Consistency Ratio (CR)\u0026thinsp;=\u0026thinsp;0.0644\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003e\u003cstrong\u003eLineament Density\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eVery high(1)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eHigh(2)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.262\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003emoderate(3)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003elow(4)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003every low(5)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003eConsistency Index (CI)\u0026thinsp;=\u0026thinsp;0.022047, Consistency Ratio (CR)\u0026thinsp;=\u0026thinsp;0.019606\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003e\u003cstrong\u003eLULC\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eBarren Land(1)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.347\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eBuilt-up(2)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eAgricultural land(3)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eshrubs(4)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003ewater body(5)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003esnow(6)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eforest(7)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003eConsistency Index (CI)\u0026thinsp;=\u0026thinsp;0.041872, Consistency Ratio (CR)\u0026thinsp;=\u0026thinsp;0.031721\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003e\u003cstrong\u003eNormalized Difference Vegetation Index\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eVery high(1)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eHigh(2)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.289\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eModerate(3)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eLow(4)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eVery low(5)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003eConsistency Index (CI)\u0026thinsp;=\u0026thinsp;0.0188185, Consistency Ratio (CR)\u0026thinsp;=\u0026thinsp;0.016734\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003e\u003cstrong\u003eRainfall\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003every high(1)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.372\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003ehigh(2)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003emoderate(3)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003elow(4)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003every low(5)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003eConsistency Index (CI)\u0026thinsp;=\u0026thinsp;0.0229082, Consistency Ratio (CR)\u0026thinsp;=\u0026thinsp;0.020371\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003e\u003cstrong\u003eRelative Relief\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003every high(1)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003ehigh(2)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003emoderate(3)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003elow(4)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003every low(5)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003eConsistency Index (CI)\u0026thinsp;=\u0026thinsp;0.0139415, Consistency Ratio (CR)\u0026thinsp;=\u0026thinsp;0.012398\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003e\u003cstrong\u003eStream Power Index\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eVery high(1)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eHigh(2)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eModerate(3)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eLow(4)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eVery low(5)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003eConsistency Index (CI)\u0026thinsp;=\u0026thinsp;0.033774, Consistency Ratio (CR)\u0026thinsp;=\u0026thinsp;0.030034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003e\u003cstrong\u003eDistance from thrust\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eBelow 500(1)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003e500\u0026ndash;1500(2)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eAbove 1500(3)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003eConsistency Index (CI)\u0026thinsp;=\u0026thinsp;0.0046045, Consistency Ratio (CR)\u0026thinsp;=\u0026thinsp;0.007938\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003e\u003cstrong\u003eTopographic Wetness Index\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eVery high(1)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.375\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eHigh(2)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.313\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eModerate(3)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eLow(4)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eVery low(5)\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"15\"\u003e\u003cp\u003eConsistency Index (CI)\u0026thinsp;=\u0026thinsp;0.009275, Consistency Ratio (CR)\u0026thinsp;=\u0026thinsp;0.00828191\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"15\"\u003e\u003cem\u003eSource: Calculated by researcher\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe Analytical Hierarchy Process (AHP), which was used to assign weights to different causative factors and their respective classes, was used to carry out the Landslide Hazard Zonation (LHZ). Cell-by-cell weight values were applied to each factor's raster map, and a hazard index equation was then used to integrate the weighted maps. A Landslide Hazard Index (LHI) map was produced as a result of this integration; higher LHI values denote regions that are more vulnerable to landslides, while lower values represent areas that are more stable. The Natural Breaks classification method was used to establish threshold values for categorising the susceptibility levels.\u003c/p\u003e\u003cp\u003eFive susceptibility classes; Very Low, Low, Moderate, High, and Very High Hazard zones, were applied to the LHI map. The findings show that while Low Hazard zones make up 23.4% (1460 km2) of the entire study area, Very Low Hazard areas make up 10.5% (658 km\u0026sup2;). Spatially, these zones are primarily concentrated in the southern and southwestern parts of the study area, with some smaller pockets appearing along the eastern boundaries.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eLandslide hazard zones\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHazard Zone\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eArea (in km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArea (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery Low Hazard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e658.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eII\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow Hazard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1460.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIII\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate Hazard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1863.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh Hazard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1528.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery High Hazard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e732.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOf the entire study area, 29.84% (1863 km2) is in the Moderate Hazard Zone. Spatially, it is primarily located in the central areas, extending to the study area's eastern and northeastern regions. 24.47% (1528 km2), is occupied by the High Hazard Zone, while 11.7% (732.26 km2) is occupied by the Very High Hazard Zone. Within the study area's northern, northeastern, and some central regions are home to the majority of these high-susceptibility zones. Notably, these zones include important areas like Kalpa and Peo in Kalpa tehsil, Nichar in Nichar tehsil, and Nako and Chango in Hangrang district. Additionally, Pooh Tehsil's northern, northeastern, and southern regions are all included in these high-susceptibility zones.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"5. Validation","content":"\u003cp\u003eIn order to evaluate the predictive accuracy and dependability of any Landslide Hazard Zonation (LHZ) map, validation is an essential step. Landslide susceptibility models can be validated using a variety of techniques, such as field-based inventory validation, cross-validation methods, prediction rate curves, and success rate curves (Akgun et al. 2012; Zezere et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Among these, ROC-AUC analysis is widely preferred due to its independence from threshold selection and its ability to effectively evaluate the discriminatory power of the model (Chung and Fabbri \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The analysis of the Receiver Operating Characteristic (ROC) curve was used in this study as a reliable statistical method for validation. The ROC curve is a plot which indicates how accurately a model discriminates between classes by comparing the True Positive Rate (sensitivity) and False Positive Rate (specificity) for varying thresholds. It aids in the visualization of the balance between the correctly detected positives and the false detection (Chung and Fabbri \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLandslide inventory data from the GSI Bhookosh portal was used to verify the findings. This dataset, which includes landslide incidents that have been documented, offered a solid foundation for assessing the predictive accuracy of the model. The model's ability to identify landslide-prone areas was evaluated by contrasting the susceptibility zones shown on the LHZ map with the actual locations of landslides found in the inventory.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAccording to the validation results, the LHZ model had 71% prediction accuracy with an Area Under the Curve (AUC) value of 0.71. According to Regmi et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), the AUC value is a comprehensive indicator of model performance. A value of 1 indicates perfect prediction, while a value of 0.5 indicates a model that is no better than chance. The 70% accuracy suggests a good level of reliability for landslide hazard assessment. The validation results verify that the spatial distribution of landslides within the study area has been accurately captured by the Analytical Hierarchy Process (AHP) modelling approach and the chosen causative factors. The results demonstrate how the produced LHZ map can be used practically for disaster risk reduction, land-use planning, and decision-making in areas vulnerable to landslides. To improve the accuracy and dependability of landslide susceptibility assessments, future studies can incorporate high-resolution datasets and real-time landslide inventories.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThe Analytical Hierarchy Process (AHP) as a multi-criteria decision-making method for identifying landslide-prone areas investigation. Weights were assigned to different factors and their corresponding classes using the AHP method. The landslide susceptibility map that was produced provided a thorough evaluation of the various factor classes that were present in the study area.\u003c/p\u003e\u003cp\u003eSlope steepness is one of the most important factors influencing the risk of landslides; steeper terrains are more unstable. The analysis suggests that only 12.84% of areas with slopes below 25 degrees are in the High to Very High Hazard zones are generally stable. In contrast, landslide risk is much higher in areas with slopes greater than 35 degrees, with only 8.94% of the area categorised as Very Low to Low Hazard and a resounding 74.46% in the High to Very High Hazard range. This pattern emphasises the clear correlation between the incidence of landslides and increasing slope angle.\u003c/p\u003e\u003cp\u003eAdditionally, there is a strong correlation between landslide susceptibility and hydrological factors. 29% of the study area is made up of low to very low drainage density areas, which have a comparatively lower risk of landslides. Of these, 60% are classified as Very Low to Low Hazard zones, while only 15.8% are classified as High to Very High Hazard zones. This implies that surface runoff-induced instability is less common in places with low drainage densities. High drainage density areas, on the other hand, exhibit the opposite trend, with only 14.1% of their extent falling into Low Hazard categories and 56.3% of their extent falling into High to Very High Hazard zones. This connection emphasises how concentrated water flow destabilises slopes and encourages landslides.\u003c/p\u003e\u003cp\u003eThe importance of hydrological conditions in landslide susceptibility is further supported by the Topographic Wetness Index (TWI). The areas with the highest percentage of their extent in hazardous zones are those classified as High TWI (43.35%) and Very High TWI (64.49%), underscoring the part that excessive moisture retention plays in slope failure. Nevertheless, when combined with other elements like slope gradient, Low TWI areas make up the largest percentage (26.82%) of all High Hazard zones, indicating that even moderately wet terrains are vulnerable to landslides. The most stable terrain category is represented by Very Low TWI areas, which contribute the least (8.72%) to landslide-prone zones, while Moderate TWI areas (25.37%) are also very important.\u003c/p\u003e\u003cp\u003eThe erosive forces' role in landslide susceptibility is further supported by the Stream Power Index (SPI) analysis. The largest percentage of High to Very High Hazard zones are found in High SPI areas (42.19%) and Very High SPI areas (63.15%), suggesting that significant erosional processes are a contributing factor to slope instability. Nonetheless, in absolute terms, the majority of the High Hazard zones (29.37%) are composed of Moderate SPI areas, indicating that moderate stream power is also a major contributor to landslide activity. While Very Low SPI areas have the least impact (6.83%), indicating that they are relatively stable, Low SPI areas also make a significant contribution (18.46%).\u003c/p\u003e\u003cp\u003eLithological characteristics also play a crucial role in landslide susceptibility, as certain rock formations are inherently more prone to failure. With an area of about 769.22 km2, or 34.19% of the entire landslide hazard area, the Haimanta Group makes up the largest portion of the High to Very High Hazard zones. Rakcham Granite (293.53 km\u0026sup2; or 13.04%) and the Vaikrita Group (530.54 km\u0026sup2; or 23.58%) come next, together accounting for 70.81% of the entire hazardous area. With 40.5% of its total area categorised as High Hazard zones and only 30.5% as Low Hazard zones, the Vaikrita formation in particular shows a high degree of susceptibility, suggesting that there are few stable zones within this lithology. With 47.2% of its land classified as High Hazard and another 31.6% as Moderate Hazard, the Haimanta Group is similarly extremely vulnerable. While the Rakcham Granite formation is a moderately susceptible lithological unit, its hazard distribution is more balanced, with 30% of its area categorised as High Hazard and nearly equal portions under Moderate and Low Hazard categories. With 52.6% of its area categorised as Low Hazard and just 19.1% as High Hazard, the Sanugba formation stands out as one of the most stable lithological units. However, with 48.1% of its area falling under High Hazard zones and only 21.9% under Low Hazard, the Jeori-Wangtu Bandeissic Complex is one of the most unstable formations in the study area and shows significant landslide vulnerability.\u003c/p\u003e\u003cp\u003eLand use and land cover (LULC) have a significant impact on landslide susceptibility; the most affected areas are shrub-covered and barren lands, which together make up almost 87% of the High and Very High Hazard areas. Barren land makes up 40.04% and shrubland 46.51%, suggesting that barren landscapes and vegetated but brittle slopes are especially vulnerable to landslides because of things like loose soil, steep terrain, and the absence of deeply rooted vegetation to provide stability.\u003c/p\u003e\u003cp\u003eRelative Relief (RR) exhibits a strong correlation with landslide hazard, as areas with higher elevation differences experience greater instability. The highest percentage (66.72%) of High to Very High Hazard zones is found in Very High RR areas, indicating that landslides are most likely to occur in steep, elevated terrain. Even moderately elevated areas are vulnerable to landslides, as evidenced by the fact that moderate RR zones contribute the most in absolute terms (36.84%) to all high hazard areas, despite having a lower relative proportion. High RR areas also make up a significant portion of high hazard zones (53.91%). Relatively flat landscapes are more stable, as evidenced by the Low RR (15.79%) and Very Low RR (4.62%) areas contributing the least to high hazard zones.\u003c/p\u003e\u003cp\u003eLastly, landslide susceptibility clearly shows the impact of structural factors, especially Lineament Density (LD). Significantly, 54.9% of High LD areas and 67.8% of Very High LD areas are located in High to Very High Hazard zones, indicating that landslides are more likely to occur in structurally weak zones. Since they encompass a greater area within the study region, Moderate LD areas make the largest absolute contribution to High to Very High Hazard zones. Given that 38.1% of Moderate LD, 17.6% of Low LD, and only 6.8% of Very Low LD areas are classified as high-risk zones, it is clear that hazard susceptibility decreases with lower LD values. This suggests that areas with fewer structural disturbances are typically more stable.\u003c/p\u003e\u003cp\u003eThe study concludes that steep slopes, high drainage density, particular lithological formations, and structurally weak zones are the main factors influencing landslide hazard. These results highlight how crucial it is to combine various geospatial and geomorphological factors in order to effectively assess the hazard of landslides. This study's conclusions can help with disaster mitigation plans, infrastructure development in landslide-prone areas, and land-use planning, resulting in a better approach to risk management and sustainable development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\"Dr. Ram Lal and Mr. Gulshan Verma both have made substantial contributions to the conception or design of the work, analysis, or interpretation of data. Both the authors reviewed the manuscript.\"\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkgunA, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B. An easy to- use MATLAB program for landslide susceptibility mapping. Comput Geosci. 2012;38(1):23\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnbalagan R. Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol. 1992;32(4):269\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAyalew L, Yamagishi H. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology. 2005;65(1\u0026ndash;2):15\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeven K, Kirkby MJ. A physically based, variable contributing area model of basin hydrology. Hydrol Sci J. 1979;24(1):43\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBui DT, Pradhan B, Lofman O, Revhaug I, Dick OB. Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci. 2011;45:199\u0026ndash;211.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChung CJF, Fabbri AG. Validation of spatial prediction models for landslide hazard mapping. Nat Hazards. 2003;30(3):451\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCostanz D, Chac\u0026oacute;n J, Conoscent C, Irigara C, Rotigliano E. Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy). Landslides. 2014;11:639\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCruden DM, Varnes DJ. (1996) Landslide types and processes Landslides, investigation and mitigation: special report. 247:36\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDai C, LeeCF, Li J, Xu ZW. Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol. 2001;40:381\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDa FC, Lee CF, Zhan XH. GIS-based geo-environmental evaluation for urban land-use planning: a case study. Eng Geol. 2001;61(4):257\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDahal RK, Hasegawa S. Representative rainfall thresholds for landslides in the Nepal Himalaya. Geomorphology. 2008;100(3\u0026ndash;4):429\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDormann CF, Elith J, Bacher S, Buchman C, Carl G, Carr\u0026eacute; G, Lautenbac S. (2013) Collinearity: A review of methods to deal with it and a simulation study evaluating their performance.Ecography 36(1):27\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFroude J, Petley DN. Global fatal landslide occurrence from 2004 to 2016. Nat Hazards Earth Syst Sci. 2018;18(8):2161\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlade T. Establishing the frequency and magnitude of landslide-triggering rainstorm events in New Zealand. Environ Geol. 1998;35(2\u0026ndash;3):160\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eG\u0026ouml;kceoglu C, Aksoy H\u0026Uuml;. Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques. Eng Geol. 1996;44(1\u0026ndash;4):147\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGorsevski PV, GesslerPE, Foltz RB, Elliot WJ. Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans GIS. 2006;10(3):395\u0026ndash;415.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGujarati DN, Porter DC. Basic econometrics. 5th ed. McGraw-Hill; 2009.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGupta RP, Joshi BC. Landslide hazard zoning using the GIS approach\u0026mdash;a case study from the Ramganga catchment. Himalayas Eng Geol. 1990;28(1\u0026ndash;2):119\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuzzetti F, Carrara A, Cardinali M, Reichenbach P. Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology. 1999;31(1\u0026ndash;4):181\u0026ndash;216.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M. Estimating the quality of landslide susceptibility models. Geomorphology. 2006;81(1\u0026ndash;2):166\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHighland LM, Bobrowsky P. (2008) The landslide handbook-A guide to understanding landslides (No 1325) US Geological Survey.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHorton RE. Drainage-basin characteristics. Trans Am Geophys union. 1932;13(1):350\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHsu PH, Su WR, Chan CC. Hazards analysis and prediction from remote sensing and GIS using spatial data mining and knowledge discovery: A case study for landslide hazard zonation. Earth Resour Environ Remote Sensing/GIS Appl II. 2011;8181:164\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHungr O, Leroueil S, Picarelli L. The Varnes classification of landslide types, an update. Landslides. 2014;11(2):167\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHussain MA, Chen Z, Zho Y, MeenaSR, Ali N, Sha U. (2025) Landslide susceptibility mapping using artificial intelligence models: a case study in the Himalayas. Landslides:1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKayasth P, De DhitalMR F. Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal. Comput Geosci. 2013;52:398\u0026ndash;408.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKutner MH, Nachtsheim CJ, Neter J, Li W. (2005) Applied linear statistical models.McGraw-hill.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee S. Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens. 2005;26(7):1477\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee S, Evangelista DG. Earthquake-induced landslide-susceptibility mapping using an artificial neural network. Nat Hazards Earth Syst Sci. 2006;6(5):687\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee S, Pradhan B. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 2007;4(1):33\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoore ID, Grayson RB, Ladson AR. Digital terrain modeling: A review of hydrological, geomorphological, and biological applications. Hydrol Process. 1991;5(1):3\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNadim F, Kjekstad O, Peduzzi P, Herold C, Jaedicke C. Global landslide and avalanche hotspot. Landslide. 2006;3:159\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePourghasemi HR, Pradhan B, Gokceoglu C. (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural hazards63:965\u0026ndash;996.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePradhan B, Lee S. Landslide susceptibility assessment and factor effect analysis: Back-propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling. Environ Model Softw. 2010;25(6):747\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRegmi AD, Yoshida K, Nagata H, Pradhan B, Pourghasemi HR. Landslide susceptibility mapping along Bhalubang-Shiwapur area of mid-western Nepal using frequency ratio and conditional probability models. J Mt Sci. 2014;11(5):1266\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaaty TL. The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill; 1980.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaaty TL. The analytic hierarchy process (AHP). J Oper Res Soc. 1980;41(11):1073\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaha AK, Gupta RP, Arora MK. GIS-based landslide hazard zonation in the Bhagirathi (Ganga) valley, Himalayas. Int J Remote Sens. 2002;23(2):357\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSah AK, Gupta RP, Sarkar I, Arora MK, Csaplovics E. An approach for GIS-based statistical landslide susceptibility zonation- a case study in the Himalayas. Landslides. 2005;2:61\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaha S, Majumdar P, Bera B. Deep learning and benchmark machine learning based landslide susceptibility investigation, Garhwal Himalaya (India). Quaternary Sci Adv. 2023;10:100075.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShano L, Raghuvanshi TK, Meten M. Landslide susceptibility evaluation and hazard zonation techniques\u0026ndash;a review. Geoenvironmental Disasters. 2020;7:1\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShukla DP, Gupta S, Dubey CS, Thakur M. (2016) Geo-spatial Technology for Landslide Hazard Zonation and prediction.In: Marghany M, editor Environmental applications of remote sensing,IntechOpen, pp 281\u0026ndash;308.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh M, Khajuria V, Singh S, Singh K. Landslide susceptibility evaluation in the Beas River Basin of North-Western Himalaya: A geospatial analysis employing the Analytical Hierarchy Process (AHP) method. Quaternary Sci Adv. 2024;14:100180.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eS\u0026uuml;zen ML, Doyuran V. A comparison of the GIS-based landslide susceptibility assessment methods: Multivariate versus bivariate. Environ Geol. 2004;45(5):665\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSweta K, Goswami A, Peethambaran B, Bahuguna IM, Rajawat AS. Landslide susceptibility zonation around Dharamshala, Himachal Pradesh, India: an artificial intelligence model\u0026ndash;based assessment. Bull Eng Geol Environ. 2022;81(8):310.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVarnes DJ. (1954) Landslide types and processes In: Eckel EB, editor Landslides and engineering practice, special report 28 Highway research board National Academy of Sciences, Washington, DC 24, pp 20\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVarnes DJ. Landslide hazard zonation: a review of principles and practice. Quetigny, Paris: Darantiere; 1984.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang H, Wang G, Wang F, Sassa K, Chen Y. (2008) Probabilistic modeling of seismically triggered landslides using Monte Carlo simulations Landslides, 5:387\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWieczorek G, Mandrone G, DeCola L. (1997) The influence of hillslope on debris-flow initiation. In: Debris-Flow hazard mitigation: mechanics, prediction and assessment ASCE, USA, pp 21\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYalcin A. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. CATENA. 2008;72(1):1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYesilnacar E, Topal T. Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol. 2005;79(3\u0026ndash;4):251\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoussef K, Shao K, Moon S, Bouchard LS. Landslide susceptibility modeling by interpretable neural network. Commun Earth Environ. 2023;4(1):162.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZezere JL, Pereira S, Melo R, Oliveira SC, GarciaRA. Mapping landslide susceptibility using data-driven methods. Sci Total Environ. 2017;589:250\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Landslide Hazard Zonation (LHZ), Basin, Susceptibility, AHP, ROC-AUC","lastPublishedDoi":"10.21203/rs.3.rs-7092462/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7092462/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn mountainous regions, landslides cause substantial socio-economic harms since they are among the most common and damaging natural disasters. The Upper Satluj River Basin in Kinnaur district of Himachal Pradesh is particularly vulnerable to landslides due to its mountainous terrain and intricate geology. The Analytical Hierarchy Process (AHP) method of analysis is the foundation for the landslide hazard zonation (LHZ) mapping in study area. The authors of this study evaluated twelve factors based on how much they contributed to landslide incidents. The AHP method allows for an objective factor weighting process by determining the importance of each factor for the decision-making process through pairwise comparisons. The final landslide hazard zone map has been categorised into five hazard zones, from very low to low to moderate and high to very high levels. 36% of the study area is in the high to very high hazard zone, 30% is in the moderate hazard zone, and 34% is in the low to very low hazard zone. Most of these high-susceptibility zones are located in the northern, northeastern, and some central regions of the study area. A test using ROC curves and AUC measurements showed that the model's performance evaluation accuracy was 71%. The AHP-based model's prediction results for the study area demonstrate a trustworthy approach to landslide susceptibility assessment. The study provides crucial information for planning land use, disaster management, and infrastructure development across the Upper Satluj River Basin. To lessen the impact of landslides on infrastructure and communities, local governments and legislators must focus their mitigation efforts on high-risk areas that have been identified. This study proves that multi-criteria spatial analysis should be included in future research while also proving that the Analytic Hierarchy Process (AHP) provides effective landslide hazard evaluation.\u003c/p\u003e","manuscriptTitle":"Geospatial Assessment of Landslide Hazard in Kinnaur District Using AHP-Based Multi-Criteria Decision Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 12:42:41","doi":"10.21203/rs.3.rs-7092462/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-30T13:00:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-30T03:13:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-29T08:00:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T09:56:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-19T13:15:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-19T08:53:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T10:04:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251160771317020794732227066924124085849","date":"2025-09-16T06:24:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33717813103990464423837444050617730266","date":"2025-09-14T09:26:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337405928093573105333453623615904936219","date":"2025-09-13T13:07:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59537323710236564161087529738570319230","date":"2025-09-13T12:36:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2713000397507721085585405246625976657","date":"2025-09-13T08:27:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290793587115666588678739650900189332324","date":"2025-09-13T03:36:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146161470320382995724487267434536966756","date":"2025-09-12T20:26:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-12T19:34:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-04T21:49:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T09:58:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-29T09:54:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Geoscience","date":"2025-07-10T11:07:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d359222d-b8f4-41d2-bf8b-ff4eeee2fbab","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T08:23:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 12:42:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7092462","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7092462","identity":"rs-7092462","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}