Enhancing Decision Support System by Prioritizing Critical Areas in Landslide Disaster Management of Kinnaur District, Himachal Pradesh

Enhancing Decision Support System by Prioritizing Critical Areas in Landslide Disaster Management of Kinnaur District, Himachal Pradesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing Decision Support System by Prioritizing Critical Areas in Landslide Disaster Management of Kinnaur District, Himachal Pradesh Rabisankar Karmakar, Ankur Kumar Srivastava, Dr. Harish Bahuguna, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7918993/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Himalayan region is highly susceptible to landslides and has witnessed several fatal incidents in recent years. Implementing a priority-based ranking system for landslide management is essential to help decision-makers identify the most vulnerable or high-risk zones. This approach ensures that limited resources—such as funding, manpower, and technical expertise—are allocated efficiently. The prioritization process involves systematically evaluating and categorizing areas or projects based on the severity of risk, potential impact, and urgency of required interventions. By focusing on mitigation efforts, emergency preparedness, and infrastructure enhancement in the most at-risk zones, authorities can significantly reduce the likelihood of landslide-related disasters and strengthen community resilience. In this study, we propose a priority score ranking system for the Kinnaur district of Himachal Pradesh, based on the assessment of exposure levels of individual elements at risk, both in terms of landslide initiation and runout paths, along with their strategic importance. The resulting individual priority score maps and the integrated priority map are intended to support decision-making for a landslide early warning system and the effective allocation of resources for disaster risk reduction. Our findings indicate that approximately 5% area of the Kinnaur district should be prioritized for resource allocation to implement targeted mitigation strategies. This framework can be readily adapted on a regional scale for use in other landslide-prone regions to support focused planning and execution of disaster risk reduction initiatives. Geology Priority ranking exposure landslide initiation runout decision support system Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Landslides refers to as mass movement of rock, earth or debris material down the slope under the influence of gravity (Varnes, 1978 ), and it is one of the major concern for the people living in the Himalayan region as it causes loss of life, damages to households, roads, agricultural lands, infrastructures, electric supply, water supply, etc (Schuster and Flemming, 1986; Oberoi and Thakur, 2004 ; Chandel and Brar, 2010 , 2011 , 2012 ; Petley, 2012 ; Kahlon et al, 2014 ; Froude and Petley, 2018 ; Singh et al, 2018 ; Shrestha et al, 2025 ). This disaster will increase in future due to increasing trend of urbanization, deforestation as well as climatic change (Poussin et al. 2012 ; IPCC 2013; Sköld and Nyberg, 2016). Thus, it is necessary to take proactive measures to minimize the consequences of the landslides (Dai et al. 2002 ; Brooks 2003 ; Sarewitz et al. 2003 ). It can be done with effective planning, mobilization and allocation of the resources in the pre, post and during the time of disaster (Bahuguna et al. 2022; Khan et al. 2024). In this context, identifying and prioritizing critical areas exposed to landslides will help in planning the disaster management strategies and facilitate faster decision making while prioritizing target areas and resource allocation in the management of disaster. With the advancement of geographic information systems (GIS) technology, utilization of GIS in assessment of disaster risk to provide critical information that enhances decision-making and the effectiveness of relief efforts in disaster management has been done throughout the world (Herold and Sawada 2012 , Schmidt et al, 2011 ; Keskin et al., 2018 ; Rezvani et al., 2023 ; Wang et al., 2024 ; Yang, 2024 ). In the present study the landslide risk has been determined by combining hazard with vulnerability and exposure i.e. elements at risk, utilizing the Varnes equation (Varnes, 1984 ; Khan et al. 2023 ), however, due to non-availability of temporal landslide inventory data in the data scarce area, the risk assessment has been performed using the landslide susceptibility map (Brabb, 1985 ; Kornejady et al, 2015 ; Bicer and Ercanoglu, 2020; Psomiadis et al 2020 ; Shah et al, 2023 ). Besides, incorporating the landslide initiation susceptibility several workers have estimated the landslide risk using the debris flow data (Jaiswal et al 2011 , Liu et al, 2012 ; Han et al, 2021 ; Marchesini et al, 2021), yet combining the susceptibility with respect to landslide initiation zone and run out zone in demarcating the risk of an area is rare (Liu et al, 2012 , Rahman et al, 2017 , Singh et al, 2025 ). Bridging this gap, our study identifies the capability of damage due to landslide initiation and its runout path, the combined susceptibility i.e. initiation susceptibility and debris flow susceptibility, have been considered to locate the vulnerable areas. We propose an expert rating system for the exposures of elements at risks to landslides for prioritizing the area for effective landslide risk reduction. This rating system can be repeated and replicated on a regional or national scale in landslide prone areas. The proposed methodology has been applied in Kinnaur district of Himachal Pradesh which is a one of the major landslide prone districts in Himachal Pradesh. The area experienced disastrous landslide events in the recent past which takes life, damages property, create blockage of National Highway (NH5, Hindustan-Tibetan road), the main road corridor connecting Kinnaur with the rest of the country, etc. (Memorandum of Damages Due to Flash Floods, Floods, Cloudbursts, and Landslides During Monsoon Season, Government of Himachal Pradesh Revenue Department − 2021, 2023, 2024). In the year 2021, a major landslide took place along the NH-5, the life line of Kinnaur district near Nigulsari village on 11th August (Mahapatra et al, 2021). This tragic incident has taken the lives of 28 nos. of people, while 13 others were injured, and 4 vehicles were buried under the debris, and the road was blocked for few days (Mahapatra et al, 2021; Times of India, 17 August, 2021). The stretch of NH05 near the Nigulsari village has been damaged, blocked in subsequent years in the monsoon season of 2023, 2024 causing delays in the cash crops transportation to the market (Times of India, 18 September, 2023; Bhandari, 2024 ). An incidence of rockfall on 25th July, 2021 was reported from Batseri village of Kinnaur district which took 9 lives (Kumar et al, 2021 ). Further, during July, 2000 and June, 2005 due to the landslide lake outburst flood (LLOF) occurred in the Trans-Himalayan region of Satluj and Spiti valley causing downstream cascading effect in parts of Kinnaur district located along the Satluj and Spiti river (Gupta and Sah, 2007 ). Total loss estimated due the LLOF for about US $ 222 m and US $ 177 m in 2000 and 2005 respectively (Gupta and Sah, 2007 ). The above disastrous events warrant to identify the probable risk zones with respect to the landslides. The resultant prioritized map will help the government agencies as well as private sectors in constructive planning for the disaster risk reduction and prevention in line with the priority action of Sendai Framework for Disaster Risk Reduction 2015–2030 (UNISDR, 2015 ). 2. Study area Kinnaur district covering an area of about 6401 sq. km, located in the Indian state of Himachal Pradesh (Fig. 1 ), is known for its rugged topography and mountainous terrain, making it prone to landslides, especially during the monsoon season. Most of the district is entirely mountainous with a few deep, and incised valleys in between. The district is bounded by Lahaul & Spiti district in the north, Tibet (China) in the east, Uttarakhand state in the south, Shimla in the southwestern part, and Kullu in the northwest. The altitudes in the district are ranging from 1,500 m to 6,500 m asl, with a general increase in elevation from west to east and from south to north. The district experiences moderate to heavy rainfall during the monsoon season (July-September), with an annual average of 816 mm (District Irrigation Plan, 2015–2020) which makes certain areas vulnerable to landslides. Kinnaur located in the Higher Himalayas towards the northwestern part of the Himalayan Fold-Thrust-Belt (FTB) is seismically active and falls in seismic zone IV (BIS,2002), and high damage risk zone (MSK VIII, Source: BMTPC Vulnerability Atlas for Himachal Pradesh). According to Geological Survey of India (GSI), approximately 40% area of Kinnaur district is highly susceptible to landslides ( https://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx ). The area has witnessed several major landslide events over the years, particularly during the monsoon (Gupta and Sah, 2008 ; Chandel and Brar, 2010 , 2011 , 2012 ; Kahlon et al, 2014 ; Yadav, 2017 ; Jamwal and Sharma, 2022 ). The landslides had a significant impact on infrastructure, leading to damage or destruction of roads, buildings, and agricultural lands. Local communities have been severely affected by the landslides, with many villages being cut off from the rest of the district headquarter and the state capital. The district’s roads and the National Highway 5 (connecting Kinnaur to Shimla and further to Tibet), have been frequently impacted by landslides, leading to road blockages, infrastructural damage, and at times, fatalities. In addition, landslides on local roads have isolated entire villages, delayed relief efforts and affecting the local economy ( https://ddmakinnaur.hp.gov.in/DDMA-kinnaur/page/ Landslides.aspx, Chand and Gupta, 2025 ). 3. Data and Methodology The landslide risk assessment includes the evaluation of landslide probability, how often it can occur and their consequences i.e. damages to the elements at risk like people, infrastructure, economy, etc (Nasa, 2002 ; Corominas et al, 2023 ). The quantitative risk analysis requires the information on potential loss due to landslides (Einstein, 1988 ; Corominas et al, 2014 ). However, due to the lack of availability on the detailed value of the vulnerable elements, the risk can be deduced in the GIS environment using the spatial association of landslide susceptibility and exposure of elements at risk (Leone et al., 1996 ). In order to prioritize area for the follow up action, a ranking system has been introduced to different slopes based on their propensity to fail (Wong, 1998 ). In the present study, to identify the vulnerable areas towards supporting the stakeholders in their effecting planning and execution in order to tackle the disaster judiciously, a priority score ranking has been assessed to the individual elements at risk based on their strategic importance. Here, open-source data has been utilized either directly or the data prepared using the freely available data like landslide susceptibility (GSI, https://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx ), landslide (GSI, https://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx ), earth observation data, report, media, census data, etc. The objective of utilization of the open-source data is to ensure that the proposed methodology can be easily replicated in any given terrain for evolving a landslide decision support system. The landslide susceptibility status of the study area, i.e. landslide and susceptibility map has been taken from the Bhukosh’ portal of the Geological Survey of India ( https://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx ). The debris flow susceptibility has been prepared using the Aster DEM of 30 m resolution along with debris flow inventory data prepared from multitemporal google earth observatory data and converted into 50 m resolution map. The initiation and runout susceptibility maps have been combined for better depiction of the comprehensive landslide susceptible condition of the study area. After that individual element at risk (EatR) exposure to landslide has been prepared. All these maps have been converted into priority maps based on their degree of importance. The integration of all the priority maps in ArcGIS environment has been used to segregate the critical priority areas based on their ranking. A flow chart showing the proposed methodology is given in Fig. 2 . 3.1 Landslide Inventory A landslide database has been prepared through compilation of data from multiple sources (GSI Legacy data, District Disaster Management Authority, Border Road Organization, Public Works Department, Media, etc.). A total of 1857 landslide data has been compiled from various sources and 128 nos. of debris flows have been prepared using the google earth imagery data. All landslides, including the source areas of debris flows, were converted to point data for analysis. Using these data a landslide density map (number of landslides per square km) has been deduced in ArcGIS environment (Fig. 3 a). The high landslide density has been observed in and around the Nichar village and along NH5. 3.2 Demography As per the 2011 Census the total population of the Kinnaur district was 84,121 of which male and female were 46,249 and 37,872 respectively. Compared to 2001 census, there was 7.39% increase in the total population. This increase is often associated with land uses changes in terms of urbanization, deforestation, transportation network can lead to exacerbate of landslide risk. The interplay between population density and landslides in any area is crucial for assessing risks and implementing effective land use planning. Many researchers have conducted the estimation of landslide risk with the population density (Park et al, 2016 ; Althuwaynee and Pradhan, 2016; Kubwimana et al, 2021 ; Shah et al, 2023 ; Biswakarma et al, 2023). A population density map i.e. denoting the number of people per square kilometre area was developed using the population data and the same is given in Fig. 3 b. This map has been grouped in ten classes from 1–10 (Table 1 , Fig. 3 b). Class 1 represents population density up to 10 persons per square kilometre and Class 10 having density value more than 1000 persons per square kilometre. In between intermediate classes are grouped (Fig. 3 b). The spatial distribution of the various population density classes given in Table 1 and shown in Fig. 3 b depicts that Class 1 having the lower density value occupies the most part of the study area (Approximately 91%). The high population density areas located along the road corridors. If these areas are affected by the landslides, then it requires major resource allocation to address the situation. Table 1 Population density classes of the study area Class Density (Number of Population per square km) Area Percentage 1 10 90.93 2 50 1.61 3 100 1.05 4 200 1.62 5 300 1.78 6 400 1.58 7 500 0.57 8 600 0.24 9 1000 0.42 10 > 1000 0.19 3.3 Settlement/Building Footprint Settlement density refers to the degree of concentration of human habitation and infrastructure within a given area, which can be impacted by landslides. The relationship between settlement density and landslide risk is evident across multiple studies (Althuwaynee and Pradhan, 2016; Priyano et al, 2020, Alam et al, 2021 ; Biswakarma et al, 2023) showing that higher densities can exacerbate risks associated with landslides. The building footprint layer has been obtained from the google open building data set ( https://sites.research.google/gr/open-buildings/ ). A density map (number of buildings per square kilometre) has been prepared (Fig. 3 c) and it shows that higher settlement density is located along the major road corridors. The settlement density map was also grouped into ten classes with value ranges from 10 settlement per square kilometres (Class 1) to > 400 settlements per square kilometres (Class 10) given in Table 2 . Table 2 Settlement Density Classes in the study area Class Density (Number of settlements per square km) Area Percentage 1 10 90.93 2 50 1.61 3 100 1.05 4 200 1.62 5 300 1.78 6 400 1.58 7 500 0.57 8 600 0.24 9 1000 0.42 10 > 1000 0.19 3.4 Road In hilly regions, road closures caused by landslides are frequent during the monsoon season, disrupting the mobility and transportation of the local population and making it challenging to reach remote areas. The assessment of road network vulnerability to landslides is an important domain area of research that combines various methodologies and technologies towards development of strategies in reducing the risk (Jaiswal and van Westen, 2013 ; Nelson et al, 2019 , Yao et al, 2023 ; Zhou et al, 2024 ). The road network of the study area also frequently affected by the landslides (Mainly NH5, secondary road). A road network map of the study area with an aggregate length of around 1272.5 km is given in Fig. 3 d. The road network has been classified as National Highway (NH05 and NH505), Major District Road (MDR), Village Road (VR), Other District Road (ODR), Footway/Bridleway given in Table 3 and Fig. 3 d. Table 3 Distribution of road types in the study area Road Type Road Length in km Percentage share of road length by road types National Highway (NH) 221 17 Major District Road (MDR) 74.5 6 Other District Road (ODR) 1.5 0.11 Village Road (VR) 943.5 74 Footway/Bridleway 32 2.5 Major part of the study area is connected through village roads followed by National Highways. 3.5 Agriculture Agriculture is the major source of economy in the study area (Singh et al, 1996 ; Sharma and Negi, 2020 ). Kinnaur is renowned for its production of high-quality apples, which are exported across India (Sharma and Mohan 2024 ). Like other parts of Himalayas, the area having low cultivable land and people are forced to cultivate on steep slopes which are prone to landslides. There are records of damages to agricultural lands due to landslides throughout the world (Tiwari et al, 1986 ; Haigh and Rawat, 2011 ; Thoha et al, 2021 ; Shrestha et al, 2025 ). The losses may be in terms of loss of standing crops, low yield, or loss of productivity and all these reasonably impacts the economic condition of the farmers/cultivators and also result in loss of revenue to the state exchequer. Thus, it is necessary to demarcate the vulnerable area so that necessary precautionary measures can be taken to minimize the risk associated with the landslides. In this study, a Land Use/Land Cover (LULC) map on a 1:10,000 scale was obtained from the Bhuvan portal of the National Remote Sensing Centre (NRSC). In this map, agricultural land has been categorized into three major classes: Plantation, Single Crop Land, and Multiple Crop Land (Fig. 4 a). The plantation area contains the apple orchard of Kinnaur and it covers 95% of the agricultural land. The single and multiple crop land cover 4% and 1% of the agricultural land respectively (Fig. 4 a). It clearly states the heavy dependency of cash crop income both for the farmers and the state government. 3.6 Landslide Susceptibility Landslide affected area encompasses not only the zones where landslides are initiated but also the areas that could be impacted by the movement of the mobilized material (Melo & Zêzere, 2017 ). Therefore, both the evaluation of initiation susceptibility and the assessment of runout have been modelled by numerous researchers (Montgomery & Dietrich, 1994 ; Dai et al., 2002 ; Chung & Fabbri, 2003 ; Corominas et al., 2003 ; Hurlimann et al., 2006). In the present work, landslide initiation and runout susceptibility have been dealt separately and later combined to know the vulnerable area. 3.6.1 Landslide Initiation Susceptibility The initiation susceptibility map in 1:50000 scale as developed under the National Landslide Susceptibility Mapping programme of GSI and downloaded from the Bhukosh portal ( https://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx ). The Weighted Multi-class Index Overlay Method was used for landslide susceptibility mapping (Van Westen et al., 2006 , Ghosh, 2011 ). According to this study, the Kinnaur district has been grouped into 40% high, 23% moderate and 37% low susceptible area excluding the permafrost area (Fig. 4 b). The cut-off boundaries for each class are taken based on cumulative distribution of landslide percentage such that 70% of the cumulative landslides are falling within high susceptibility and 10% in low susceptibility. 3.6.2 Debris Flow Susceptibility A conceptual model developed by Mergilli et al. (2015) using the r.randomwalk algorithm, which is based on source area definition and the break criteria proposed by Zimmerman et al. (1997), has been employed to create an impact probability map for runout susceptibility. Initially, using the debris flow inventory data, the source area probability map of the entire area was prepared. After that, using the Monte Carlo simulation technique (Random Walk, Pearson, 1905 ), impact probability of the derived source area was deduced. The impact probability map has been classified into three classes low, moderate, and high based on natural breaks. About 19%, 26%, and 24% area have been grouped into low, moderate, and high classes respectively (Fig. 4 c). 3.6.3 Combined Susceptibility The concept of combined susceptibility initiation and runout zone (Rahman et al, 2017 , Mergili et al, 2019 ) refers to the integration of areas that are not only prone to the initiation of landslides or debris flows but also to their subsequent movement or runout. This is crucial for effective hazard assessment and risk management in regions susceptible to such geological events. The combined susceptibility has been prepared using the cell statistics of ArcGIS environment. It has been further reclassified based on the presence or absence of runout or the initiation susceptibility classes. The schema developed for classification of the combined susceptibility is shown in table-1 where no data defines the permafrost region where snow avalanche may occur or it indicates the absence of the runout impact area. Table 1 Classification schema of combined susceptibility Sr No Initiation Susceptibility Runout Susceptibility Combined Susceptibility Class 1 No Data No Data No Data 2 Low No Data Low 3 No Data Low Low 4 Low Low Low 5 No Data Moderate Moderate 6 Low Moderate Moderate 7 Moderate No Data Moderate 8 Moderate Low Moderate 9 Moderate Moderate Moderate 10 High No Data High 11 High Low High 12 High Moderate High 13 No Data High High 14 Low High High 15 Moderate High High 16 High High Very High The reclassified combined susceptibility map shows that very high, high, moderate, and low susceptible area encompasses about 8%, 33%, 25%, and 18% respectively (Fig. 4 d). 4 Results 4.1 Exposures of EatR The assessment of exposure is essential which gives the idea of the location of EatR with respect to the landslide initiation and runout path. The derived combined susceptibility map has been integrated with all the EatR (Fig. 4 ) to define the exposures. The exposure maps were qualitatively classified as no data, low, moderate, high, and very high through overlay analysis of the combined susceptibility and EatR (Fig. 5 ). 4.2 Priority Ranking The focal point of this study is prioritizing the critical areas through priority ranking in the landslide management which entails categorizing areas according to their urgency enables more efficient resource allocation and targeted mitigation efforts. Using the concept of priority ranking proposed by Wong, 1998 and matrix approach for qualitative estimation of landslide risk (Chowdhury & Flentje, 2003 ), a priority score with value ranges from 0 (low priority) to 1 (high priority) scale has been proposed to individual classes of the EatR considering its exposures to landslides and importance on the socio-economic point of view. The assigned priority ranking of the EatR and the landslide density classes are given in Table 2 . Table 2 Assigned priority score of the element at risk Element at Risk and Landslide Classes Priority Score Exposure No Data Low Moderate High Very High Population Density 1 0 0.2 0.3 0.4 0.5 2 0 0.2 0.4 0.6 0.7 3 0 0.2 0.4 0.7 0.8 4 - 0.2 0.4 0.7 0.8 5 0 0.3 0.5 0.9 1 6 - 0.3 0.5 0.9 1 7 0 0.3 0.5 0.9 1 8 - 0.3 0.5 0.9 1 9 - 0.3 0.5 0.9 1 10 - 0.3 0.5 0.9 1 Settlement/ Building Footprint Density 1 0 0.2 0.3 0.4 0.5 2 0 0.2 0.4 0.6 0.7 3 0 0.2 0.4 0.7 0.9 4 0 0.2 0.4 0.7 0.9 5 0 0.3 0.5 0.8 1 6 0 0.3 0.5 0.8 1 7 0 0.3 0.5 0.8 1 8 0 0.3 0.5 0.8 1 9 0 0.3 0.5 0.8 1 10 0 0.3 0.5 0.8 1 Road National Highway (NH) 0 0.4 0.7 1 1 Major District Road (MDR) - 0.3 0.5 0.8 0.9 Other District Road (ODR) - - - 0.5 - Village Road (VR) 0 0.2 0.4 0.7 0.8 Footway/Bridleway 0 0.1 0.3 0.4 0.5 Agriculture Plantation (Apple Orchard) 0 0.4 0.5 1 1 Single crop 0 0.2 0.3 0.6 0.7 Multiple crop - 0.3 0.4 0.8 0.9 Landslide Density 1 - 0.4 - - - 2 - - 0.5 - - 3 - - - 0.8 - 4 - - - 0.8 - 5 - - - 0.8 - 6 - - - - 1 7 - - - - 1 8 - - - - 1 9 - - - - 1 10 - - - - 1 Based on the priority score, priority score maps were deduced for the individual EatR (Fig. 6 ). The landslide priority score map has also been prepared having the higher priority for the high-density classes (Fig. 6 ). The settlement located in the critical areas mainly in and around Nigulsari, Reckong Peo, Pangi, Sangla, Chitkul, Asrang, Spillow,Pooh, Ropa, Charang, Chango, Nako whereas the agricultural land mostly in Nigulsari, Reckong Peo, Sangla, Asrang, Spillow, Ropa, Pooh with few areas in Chango and Nako areas (Fig. 6 ). The road stretches along the NH5 are critical around Nigulsari, NH5 Sangla road junction, Reckong Peo, NH Charang road junction, Ropa road junction, Spillow, Pooh, Chango (Fig. 6 ). The integration of all the maps has been done to get the final priority map of the study area. Based on the priority score, it has been classified into four classes namely low (0-0.4), moderate (> 0.4–0.6), high (> 0.6–0.8), and very high (> 0.8). The high to very high priority area located along the NH5, Sangla-Chitkul, and other NW-SE trending valleys (Fig. 7 ). 4.3 Validation Validation is a crucial step for any model performance evaluation. In this work, area under the receiver operating curve (AUROC) was used to check the efficacy of the model. The AUROC of the susceptibility model depicting 0.73 (Fig. 8 ) which is a good performance (Özay and Orhan, 2023) and the impact probability model also shows a good performance with AUROC of 0.75 (Fig. 8 ). The AUROC of priority rank deduced as 0.93 (Fig. 8 ) revealing a very good performance (Özay and Orhan, 2023). 5 Discussions Generally, the landslide risk was estimated using the landslide hazard, element at risk and their vulnerability. However, the impact area has not been considered in the hazard evaluation. In the present study, the exposure assessment of the EatR with respect to landslide initiation provides that the about 25% each of settlement, population, agricultural land, and 49% of road network comes within the high category whereas with respect to combined susceptibility it is about 41%, 42%, 59%, and 72% of settlement, population, agricultural land, and road network respectively. Considering these consequences, the priority score maps of the individual element at risk with respect to combined susceptibility condition were evaluation The study suggests that the critical areas are located along the major trunk road (NH5), western part of the study area and along the NW-SE trending valleys (Fig. 6 ). The integration of all the priority maps clearly depicts the which vulnerable locality needs to be addressed with greater importance (Fig. 7 ). The availability of safe settlement in hilly terrain is very restricted. According to Zimmermann, 2004 in the European Alps, earlier church, village recreational centre are located in safer place and with growing urbanization new settlements are located in proximity to probable hazard affected area. Thus, strict land-use planning and enforcement of zoning laws are essential, including restricting construction in high-risk areas and considering the relocation of vulnerable communities to achieve sustainable mountain development. In this context, the output maps of the present work will play pivotal role in prioritizing areas for future development, land-use planning. Further, the classified priority map indicates that only 5% of the total area to be given more preference in allocating the resource during the time of disaster. This spatial distribution of prioritized also help in the early warning system. The high to very high priority areas located along the NH5 covering major habitation like Nigulsari, Spillow, Pooh, Nako; Sangla – Chitkul sector; and other NW-SE trending valleys (Fig. 7 ) needs to be monitored remotely during the rainy season with installation of AWS and ARG coupled with ground-based sensor like seepage measuring devices, inclinometers, settlement measuring devices and implementation of early warning system. Slope stability can be improved by adopting some general measures like drainage arrangements, retaining structures and adopting bio-restoration measures along with redesigning roads for resilience while long term and permanent stabilisation/support systems can be implemented after the site-specific detailed geological and geotechnical investigations. Community awareness, people centric early warning systems, and emergency preparedness—including evacuation plans and training—must be prioritized. Coordination among departments, regular infrastructure maintenance, and securing funding for mitigation are key to building long-term resilience against landslides disaster. The future work may be conducted in a more constructive way through incorporation of the rockfall impact area, multi-hazard scenario, using of machine learning method which are the limitation of present work. 6. Conclusion Landslide susceptibility in the Himalayas is shaped by a combination of geological, topoclimatic factors which are specific to a particular sub basin or physiographic subdivision which can later be altered by human-induced factors such as slope modification through new road cuts/widening of existing road benches and construction of other infrastructural facilities. The Kinnaur region in Himachal Pradesh is especially prone to landslides due to its fragile geology, rugged topography, active tectonic environment, steep slopes, and frequent seismic activity, all of which contribute to the high occurrence of landslides while rainfall, snow melt, and seismic tremors act as trigger. Prioritizing areas based on their susceptibility to landslides becomes the cornerstone of effective risk mitigation strategies. The results of this study have demonstrated a noble approach in defining the element at risk and exposure to landslides considering both the source and runout zone in identifying the key localities through utilization of readily available geo-spatial data which will significantly enhance the disaster risk reduction efforts in vulnerable regions. Further, with the proposed priority ranking system the study helps in prioritizing the areas where disaster risk reduction (DRR) strategies can be implemented effectively through targeted interventions that enhance community resilience and reduce vulnerability. This study will greatly aid in land use planning, resource allocation during emergency, landslide forecasting system. This work can be easily replicated on a regional scale in landslide prone areas in Himalayas in order to plan and execute the focused disaster risk reduction and build disaster resilience amongst the local communities. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Rabisankar Karmakar, Ankur Kumar Srivastava, and Qurban Ali Tariq. The first draft of the manuscript was written by Rabisankar Karmakar and Ankur Kumar Srivastava and all authors commented on previous versions of the manuscript. Writing - review and editing done by Harish Bahuguna. All authors read and approved the final manuscript. References Alam, Sk & Mandal, Sujit & Maiti, Ramkrishna. (2021). A Probabilistic Landslide Risk Assessment (LRA) on NH31A and Settlement in Rorachu Watershed, East Sikkim, India by using Bivariate Models and Geospatial Techniques. 10.21203/rs.3.rs-1019380/v1. Althuwaynee, Omar F., Pradhan,B. (2017). Semi-quantitative landslide risk assessment using GIS-based exposure analysis in Kuala Lumpur City, Geomatics,Natural Hazards and Risk, 8:2, 706-732, DOI: 10.1080/19475705.2016.1255670. Andersson-Sköld, Y., Nyberg, L. 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04:18:12","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":187442,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7918993/v1/a0cc6bc4c4d516e3055c8647.html"},{"id":94160429,"identity":"5c8b45f1-74ec-4f96-9957-ccdde06253ee","added_by":"auto","created_at":"2025-10-23 04:26:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":365348,"visible":true,"origin":"","legend":"\u003cp\u003eThe location map showing the spatial distribution of element at risk (EatR)\u003c/p\u003e","description":"","filename":"image1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7918993/v1/ad62198ed3a87ff9773f0613.jpg"},{"id":94159991,"identity":"0e81f2d7-b3e7-4e82-8fb1-fe66c5b38a26","added_by":"auto","created_at":"2025-10-23 04:18:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":479389,"visible":true,"origin":"","legend":"\u003cp\u003eA flow diagram depicting the proposed methodology\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7918993/v1/e43959a78cd5d42bccec1b08.png"},{"id":94160974,"identity":"f6bf4616-dd0b-4332-ba66-8c6d2afbd1b0","added_by":"auto","created_at":"2025-10-23 04:42:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":911671,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of Landslide density (a), Element at Risk represented by Population density (b) Settlement density (c), and Road network (d) in the study area\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7918993/v1/d9b8443d0009604b0801622d.png"},{"id":94160430,"identity":"f3bbb80a-63cb-4420-839b-1e1b27bf27bd","added_by":"auto","created_at":"2025-10-23 04:26:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1037199,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of Element at Risk represented Agricultural land (a), initiation susceptibility (b), runout susceptibility (c), and combined susceptibility (d) of the study area\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7918993/v1/e2a7bcc764b09265b464da77.png"},{"id":94160576,"identity":"07151f98-60ef-42ef-a0dc-a63d6f66224e","added_by":"auto","created_at":"2025-10-23 04:34:12","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":141565,"visible":true,"origin":"","legend":"\u003cp\u003eElement at risk exposures to landslide (a) Population, (b) Settlement, (c) Road, and (d) Agricultural land\u003c/p\u003e","description":"","filename":"image5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7918993/v1/7e144a618c106b6ff08ba192.jpg"},{"id":94160433,"identity":"6490b97f-a984-4b99-aadb-6078385bee4e","added_by":"auto","created_at":"2025-10-23 04:26:12","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":185232,"visible":true,"origin":"","legend":"\u003cp\u003ePriority score map with respect to (a) landslide, element at risk in the area-(b) Population, (c) Settlement, (d) Road, and (e) Agricultural land\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7918993/v1/a34ef8a0909a29d08f33c1c2.jpeg"},{"id":94159994,"identity":"a4dac5fd-7af6-4abc-8c28-da70d1efdd87","added_by":"auto","created_at":"2025-10-23 04:18:12","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":223084,"visible":true,"origin":"","legend":"\u003cp\u003ePriority ranking of the Kinnaur district\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7918993/v1/72df8ce806258cc7f0a75a34.jpeg"},{"id":94160002,"identity":"f63db58f-ab8e-40ab-9b90-dca2d1065327","added_by":"auto","created_at":"2025-10-23 04:18:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":152657,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve and AUC values showing performance of the models-Landslide (top left), Impact Probability (top right), and Priority map (bottom)\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7918993/v1/65e72b1aea7a7f467712e850.png"},{"id":94161066,"identity":"126c9300-b910-4b61-82f2-8492c27306db","added_by":"auto","created_at":"2025-10-23 04:50:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4329854,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7918993/v1/7e1f941e-3248-415b-9e38-87f066c0f4db.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEnhancing Decision Support System by Prioritizing Critical Areas in Landslide Disaster Management of Kinnaur District, Himachal Pradesh\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eLandslides refers to as mass movement of rock, earth or debris material down the slope under the influence of gravity (Varnes, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e1978\u003c/span\u003e), and it is one of the major concern for the people living in the Himalayan region as it causes loss of life, damages to households, roads, agricultural lands, infrastructures, electric supply, water supply, etc (Schuster and Flemming, 1986; Oberoi and Thakur, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Chandel and Brar, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Petley, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kahlon et al, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Froude and Petley, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Singh et al, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shrestha et al, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This disaster will increase in future due to increasing trend of urbanization, deforestation as well as climatic change (Poussin et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; IPCC 2013; Sk\u0026ouml;ld and Nyberg, 2016). Thus, it is necessary to take proactive measures to minimize the consequences of the landslides (Dai et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Brooks \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Sarewitz et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). It can be done with effective planning, mobilization and allocation of the resources in the pre, post and during the time of disaster (Bahuguna et al. 2022; Khan et al. 2024). In this context, identifying and prioritizing critical areas exposed to landslides will help in planning the disaster management strategies and facilitate faster decision making while prioritizing target areas and resource allocation in the management of disaster. With the advancement of geographic information systems (GIS) technology, utilization of GIS in assessment of disaster risk to provide critical information that enhances decision-making and the effectiveness of relief efforts in disaster management has been done throughout the world (Herold and Sawada \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Schmidt et al, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Keskin et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rezvani et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yang, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the present study the landslide risk has been determined by combining hazard with vulnerability and exposure i.e. elements at risk, utilizing the Varnes equation (Varnes, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), however, due to non-availability of temporal landslide inventory data in the data scarce area, the risk assessment has been performed using the landslide susceptibility map (Brabb, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Kornejady et al, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bicer and Ercanoglu, 2020; Psomiadis et al \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shah et al, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Besides, incorporating the landslide initiation susceptibility several workers have estimated the landslide risk using the debris flow data (Jaiswal et al \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Liu et al, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Han et al, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Marchesini et al, 2021), yet combining the susceptibility with respect to landslide initiation zone and run out zone in demarcating the risk of an area is rare (Liu et al, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Rahman et al, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Singh et al, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBridging this gap, our study identifies the capability of damage due to landslide initiation and its runout path, the combined susceptibility i.e. initiation susceptibility and debris flow susceptibility, have been considered to locate the vulnerable areas. We propose an expert rating system for the exposures of elements at risks to landslides for prioritizing the area for effective landslide risk reduction. This rating system can be repeated and replicated on a regional or national scale in landslide prone areas.\u003c/p\u003e\u003cp\u003eThe proposed methodology has been applied in Kinnaur district of Himachal Pradesh which is a one of the major landslide prone districts in Himachal Pradesh. The area experienced disastrous landslide events in the recent past which takes life, damages property, create blockage of National Highway (NH5, Hindustan-Tibetan road), the main road corridor connecting Kinnaur with the rest of the country, etc. (Memorandum of Damages Due to Flash Floods, Floods, Cloudbursts, and Landslides During Monsoon Season, Government of Himachal Pradesh Revenue Department \u0026minus;\u0026thinsp;2021, 2023, 2024). In the year 2021, a major landslide took place along the NH-5, the life line of Kinnaur district near Nigulsari village on 11th August (Mahapatra et al, 2021). This tragic incident has taken the lives of 28 nos. of people, while 13 others were injured, and 4 vehicles were buried under the debris, and the road was blocked for few days (Mahapatra et al, 2021; Times of India, 17 August, 2021). The stretch of NH05 near the Nigulsari village has been damaged, blocked in subsequent years in the monsoon season of 2023, 2024 causing delays in the cash crops transportation to the market (Times of India, 18 September, 2023; Bhandari, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). An incidence of rockfall on 25th July, 2021 was reported from Batseri village of Kinnaur district which took 9 lives (Kumar et al, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Further, during July, 2000 and June, 2005 due to the landslide lake outburst flood (LLOF) occurred in the Trans-Himalayan region of Satluj and Spiti valley causing downstream cascading effect in parts of Kinnaur district located along the Satluj and Spiti river (Gupta and Sah, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Total loss estimated due the LLOF for about US\u003cspan\u003e$\u003c/span\u003e 222 m and US\u003cspan\u003e$\u003c/span\u003e 177 m in 2000 and 2005 respectively (Gupta and Sah, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The above disastrous events warrant to identify the probable risk zones with respect to the landslides. The resultant prioritized map will help the government agencies as well as private sectors in constructive planning for the disaster risk reduction and prevention in line with the priority action of Sendai Framework for Disaster Risk Reduction 2015\u0026ndash;2030 (UNISDR, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Study area","content":"\u003cp\u003eKinnaur district covering an area of about 6401 sq. km, located in the Indian state of Himachal Pradesh (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), is known for its rugged topography and mountainous terrain, making it prone to landslides, especially during the monsoon season. Most of the district is entirely mountainous with a few deep, and incised valleys in between. The district is bounded by Lahaul \u0026amp; Spiti district in the north, Tibet (China) in the east, Uttarakhand state in the south, Shimla in the southwestern part, and Kullu in the northwest. The altitudes in the district are ranging from 1,500 m to 6,500 m asl, with a general increase in elevation from west to east and from south to north. The district experiences moderate to heavy rainfall during the monsoon season (July-September), with an annual average of 816 mm (District Irrigation Plan, 2015\u0026ndash;2020) which makes certain areas vulnerable to landslides. Kinnaur located in the Higher Himalayas towards the northwestern part of the Himalayan Fold-Thrust-Belt (FTB) is seismically active and falls in seismic zone IV (BIS,2002), and high damage risk zone (MSK VIII, Source: BMTPC Vulnerability Atlas for Himachal Pradesh). According to Geological Survey of India (GSI), approximately 40% area of Kinnaur district is highly susceptible to landslides (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx\u003c/span\u003e\u003cspan address=\"https://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The area has witnessed several major landslide events over the years, particularly during the monsoon (Gupta and Sah, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Chandel and Brar, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kahlon et al, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yadav, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jamwal and Sharma, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The landslides had a significant impact on infrastructure, leading to damage or destruction of roads, buildings, and agricultural lands. Local communities have been severely affected by the landslides, with many villages being cut off from the rest of the district headquarter and the state capital. The district\u0026rsquo;s roads and the National Highway 5 (connecting Kinnaur to Shimla and further to Tibet), have been frequently impacted by landslides, leading to road blockages, infrastructural damage, and at times, fatalities. In addition, landslides on local roads have isolated entire villages, delayed relief efforts and affecting the local economy (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ddmakinnaur.hp.gov.in/DDMA-kinnaur/page/\u003c/span\u003e\u003cspan address=\"https://ddmakinnaur.hp.gov.in/DDMA-kinnaur/page/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Landslides.aspx, Chand and Gupta, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"3. Data and Methodology","content":"\u003cp\u003eThe landslide risk assessment includes the evaluation of landslide probability, how often it can occur and their consequences i.e. damages to the elements at risk like people, infrastructure, economy, etc (Nasa, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Corominas et al, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The quantitative risk analysis requires the information on potential loss due to landslides (Einstein, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Corominas et al, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, due to the lack of availability on the detailed value of the vulnerable elements, the risk can be deduced in the GIS environment using the spatial association of landslide susceptibility and exposure of elements at risk (Leone et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). In order to prioritize area for the follow up action, a ranking system has been introduced to different slopes based on their propensity to fail (Wong, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). In the present study, to identify the vulnerable areas towards supporting the stakeholders in their effecting planning and execution in order to tackle the disaster judiciously, a priority score ranking has been assessed to the individual elements at risk based on their strategic importance. Here, open-source data has been utilized either directly or the data prepared using the freely available data like landslide susceptibility (GSI, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx\u003c/span\u003e\u003cspan address=\"https://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), landslide (GSI, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx\u003c/span\u003e\u003cspan address=\"https://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), earth observation data, report, media, census data, etc. The objective of utilization of the open-source data is to ensure that the proposed methodology can be easily replicated in any given terrain for evolving a landslide decision support system. The landslide susceptibility status of the study area, i.e. landslide and susceptibility map has been taken from the Bhukosh\u0026rsquo; portal of the Geological Survey of India (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx\u003c/span\u003e\u003cspan address=\"https://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The debris flow susceptibility has been prepared using the Aster DEM of 30 m resolution along with debris flow inventory data prepared from multitemporal google earth observatory data and converted into 50 m resolution map. The initiation and runout susceptibility maps have been combined for better depiction of the comprehensive landslide susceptible condition of the study area. After that individual element at risk (EatR) exposure to landslide has been prepared. All these maps have been converted into priority maps based on their degree of importance. The integration of all the priority maps in ArcGIS environment has been used to segregate the critical priority areas based on their ranking. A flow chart showing the proposed methodology is given in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Landslide Inventory\u003c/h2\u003e\u003cp\u003eA landslide database has been prepared through compilation of data from multiple sources (GSI Legacy data, District Disaster Management Authority, Border Road Organization, Public Works Department, Media, etc.). A total of 1857 landslide data has been compiled from various sources and 128 nos. of debris flows have been prepared using the google earth imagery data. All landslides, including the source areas of debris flows, were converted to point data for analysis. Using these data a landslide density map (number of landslides per square km) has been deduced in ArcGIS environment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The high landslide density has been observed in and around the Nichar village and along NH5.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Demography\u003c/h2\u003e\u003cp\u003eAs per the 2011 Census the total population of the Kinnaur district was 84,121 of which male and female were 46,249 and 37,872 respectively. Compared to 2001 census, there was 7.39% increase in the total population. This increase is often associated with land uses changes in terms of urbanization, deforestation, transportation network can lead to exacerbate of landslide risk. The interplay between population density and landslides in any area is crucial for assessing risks and implementing effective land use planning. Many researchers have conducted the estimation of landslide risk with the population density (Park et al, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Althuwaynee and Pradhan, 2016; Kubwimana et al, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shah et al, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Biswakarma et al, 2023). A population density map i.e. denoting the number of people per square kilometre area was developed using the population data and the same is given in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb. This map has been grouped in ten classes from 1\u0026ndash;10 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Class 1 represents population density up to 10 persons per square kilometre and Class 10 having density value more than 1000 persons per square kilometre. In between intermediate classes are grouped (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The spatial distribution of the various population density classes given in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e1\u003c/span\u003e and shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb depicts that Class 1 having the lower density value occupies the most part of the study area (Approximately 91%). The high population density areas located along the road corridors. If these areas are affected by the landslides, then it requires major resource allocation to address the situation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePopulation density classes of the study area\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDensity\u003c/p\u003e\u003cp\u003e(Number of Population per square km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eArea Percentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Settlement/Building Footprint\u003c/h2\u003e\u003cp\u003eSettlement density refers to the degree of concentration of human habitation and infrastructure within a given area, which can be impacted by landslides. The relationship between settlement density and landslide risk is evident across multiple studies (Althuwaynee and Pradhan, 2016; Priyano et al, 2020, Alam et al, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Biswakarma et al, 2023) showing that higher densities can exacerbate risks associated with landslides. The building footprint layer has been obtained from the google open building data set (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sites.research.google/gr/open-buildings/\u003c/span\u003e\u003cspan address=\"https://sites.research.google/gr/open-buildings/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A density map (number of buildings per square kilometre) has been prepared (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) and it shows that higher settlement density is located along the major road corridors. The settlement density map was also grouped into ten classes with value ranges from 10 settlement per square kilometres (Class 1) to \u0026gt;\u0026thinsp;400 settlements per square kilometres (Class 10) given in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSettlement Density Classes in the study area\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDensity (Number of settlements per square km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eArea Percentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Road\u003c/h2\u003e\u003cp\u003eIn hilly regions, road closures caused by landslides are frequent during the monsoon season, disrupting the mobility and transportation of the local population and making it challenging to reach remote areas. The assessment of road network vulnerability to landslides is an important domain area of research that combines various methodologies and technologies towards development of strategies in reducing the risk (Jaiswal and van Westen, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nelson et al, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Yao et al, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhou et al, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The road network of the study area also frequently affected by the landslides (Mainly NH5, secondary road). A road network map of the study area with an aggregate length of around 1272.5 km is given in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed. The road network has been classified as National Highway (NH05 and NH505), Major District Road (MDR), Village Road (VR), Other District Road (ODR), Footway/Bridleway given in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of road types in the study area\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRoad Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRoad Length\u003c/p\u003e\u003cp\u003ein km\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage share of road length by road types\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNational Highway (NH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMajor District Road (MDR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther District Road (ODR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVillage Road (VR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e943.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFootway/Bridleway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.5\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\u003eMajor part of the study area is connected through village roads followed by National Highways.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Agriculture\u003c/h2\u003e\u003cp\u003eAgriculture is the major source of economy in the study area (Singh et al, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Sharma and Negi, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Kinnaur is renowned for its production of high-quality apples, which are exported across India (Sharma and Mohan \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Like other parts of Himalayas, the area having low cultivable land and people are forced to cultivate on steep slopes which are prone to landslides. There are records of damages to agricultural lands due to landslides throughout the world (Tiwari et al, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Haigh and Rawat, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Thoha et al, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shrestha et al, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The losses may be in terms of loss of standing crops, low yield, or loss of productivity and all these reasonably impacts the economic condition of the farmers/cultivators and also result in loss of revenue to the state exchequer. Thus, it is necessary to demarcate the vulnerable area so that necessary precautionary measures can be taken to minimize the risk associated with the landslides. In this study, a Land Use/Land Cover (LULC) map on a 1:10,000 scale was obtained from the Bhuvan portal of the National Remote Sensing Centre (NRSC). In this map, agricultural land has been categorized into three major classes: Plantation, Single Crop Land, and Multiple Crop Land (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The plantation area contains the apple orchard of Kinnaur and it covers 95% of the agricultural land. The single and multiple crop land cover 4% and 1% of the agricultural land respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). It clearly states the heavy dependency of cash crop income both for the farmers and the state government.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Landslide Susceptibility\u003c/h2\u003e\u003cp\u003eLandslide affected area encompasses not only the zones where landslides are initiated but also the areas that could be impacted by the movement of the mobilized material (Melo \u0026amp; Z\u0026ecirc;zere, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, both the evaluation of initiation susceptibility and the assessment of runout have been modelled by numerous researchers (Montgomery \u0026amp; Dietrich, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Dai et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Chung \u0026amp; Fabbri, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Corominas et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Hurlimann et al., 2006). In the present work, landslide initiation and runout susceptibility have been dealt separately and later combined to know the vulnerable area.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.6.1 Landslide Initiation Susceptibility\u003c/h2\u003e\u003cp\u003eThe initiation susceptibility map in 1:50000 scale as developed under the National Landslide Susceptibility Mapping programme of GSI and downloaded from the Bhukosh portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx\u003c/span\u003e\u003cspan address=\"https://bhukosh.gsi.gov.in/Bhukosh/MapViewer.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Weighted Multi-class Index Overlay Method was used for landslide susceptibility mapping (Van Westen et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Ghosh, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). According to this study, the Kinnaur district has been grouped into 40% high, 23% moderate and 37% low susceptible area excluding the permafrost area (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The cut-off boundaries for each class are taken based on cumulative distribution of landslide percentage such that 70% of the cumulative landslides are falling within high susceptibility and 10% in low susceptibility.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.6.2 Debris Flow Susceptibility\u003c/h2\u003e\u003cp\u003eA conceptual model developed by Mergilli et al. (2015) using the r.randomwalk algorithm, which is based on source area definition and the break criteria proposed by Zimmerman et al. (1997), has been employed to create an impact probability map for runout susceptibility. Initially, using the debris flow inventory data, the source area probability map of the entire area was prepared. After that, using the Monte Carlo simulation technique (Random Walk, Pearson, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1905\u003c/span\u003e), impact probability of the derived source area was deduced. The impact probability map has been classified into three classes low, moderate, and high based on natural breaks. About 19%, 26%, and 24% area have been grouped into low, moderate, and high classes respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.6.3 Combined Susceptibility\u003c/h2\u003e\u003cp\u003eThe concept of combined susceptibility initiation and runout zone (Rahman et al, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Mergili et al, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) refers to the integration of areas that are not only prone to the initiation of landslides or debris flows but also to their subsequent movement or runout. This is crucial for effective hazard assessment and risk management in regions susceptible to such geological events. The combined susceptibility has been prepared using the cell statistics of ArcGIS environment. It has been further reclassified based on the presence or absence of runout or the initiation susceptibility classes. The schema developed for classification of the combined susceptibility is shown in table-1 where no data defines the permafrost region where snow avalanche may occur or it indicates the absence of the runout impact area.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClassification schema of combined susceptibility\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\u003eSr No\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInitiation Susceptibility\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRunout Susceptibility\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCombined Susceptibility Class\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo Data\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVery High\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\u003eThe reclassified combined susceptibility map shows that very high, high, moderate, and low susceptible area encompasses about 8%, 33%, 25%, and 18% respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Exposures of EatR\u003c/h2\u003e\u003cp\u003eThe assessment of exposure is essential which gives the idea of the location of EatR with respect to the landslide initiation and runout path. The derived combined susceptibility map has been integrated with all the EatR (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) to define the exposures. The exposure maps were qualitatively classified as no data, low, moderate, high, and very high through overlay analysis of the combined susceptibility and EatR (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Priority Ranking\u003c/h2\u003e\u003cp\u003eThe focal point of this study is prioritizing the critical areas through priority ranking in the landslide management which entails categorizing areas according to their urgency enables more efficient resource allocation and targeted mitigation efforts. Using the concept of priority ranking proposed by Wong, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e1998\u003c/span\u003e and matrix approach for qualitative estimation of landslide risk (Chowdhury \u0026amp; Flentje, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), a priority score with value ranges from 0 (low priority) to 1 (high priority) scale has been proposed to individual classes of the EatR considering its exposures to landslides and importance on the socio-economic point of view. The assigned priority ranking of the EatR and the landslide density classes are given in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssigned priority score of the element at risk\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eElement at Risk and Landslide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eClasses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u003cp\u003ePriority Score\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVery High\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003cp\u003eDensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eSettlement/\u003c/p\u003e\u003cp\u003eBuilding Footprint\u003c/p\u003e\u003cp\u003eDensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eRoad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNational Highway (NH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMajor District Road (MDR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther District Road (ODR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVillage Road (VR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFootway/Bridleway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAgriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePlantation (Apple Orchard)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle crop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultiple crop\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eLandslide\u003c/p\u003e\u003cp\u003eDensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\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\u003eBased on the priority score, priority score maps were deduced for the individual EatR (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The landslide priority score map has also been prepared having the higher priority for the high-density classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The settlement located in the critical areas mainly in and around Nigulsari, Reckong Peo, Pangi, Sangla, Chitkul, Asrang, Spillow,Pooh, Ropa, Charang, Chango, Nako whereas the agricultural land mostly in Nigulsari, Reckong Peo, Sangla, Asrang, Spillow, Ropa, Pooh with few areas in Chango and Nako areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The road stretches along the NH5 are critical around Nigulsari, NH5 Sangla road junction, Reckong Peo, NH Charang road junction, Ropa road junction, Spillow, Pooh, Chango (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The integration of all the maps has been done to get the final priority map of the study area. Based on the priority score, it has been classified into four classes namely low (0-0.4), moderate (\u0026gt;\u0026thinsp;0.4\u0026ndash;0.6), high (\u0026gt;\u0026thinsp;0.6\u0026ndash;0.8), and very high (\u0026gt;\u0026thinsp;0.8). The high to very high priority area located along the NH5, Sangla-Chitkul, and other NW-SE trending valleys (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Validation\u003c/h2\u003e\u003cp\u003eValidation is a crucial step for any model performance evaluation. In this work, area under the receiver operating curve (AUROC) was used to check the efficacy of the model. The AUROC of the susceptibility model depicting 0.73 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) which is a good performance (\u0026Ouml;zay and Orhan, 2023) and the impact probability model also shows a good performance with AUROC of 0.75 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The AUROC of priority rank deduced as 0.93 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) revealing a very good performance (\u0026Ouml;zay and Orhan, 2023).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Discussions","content":"\u003cp\u003eGenerally, the landslide risk was estimated using the landslide hazard, element at risk and their vulnerability. However, the impact area has not been considered in the hazard evaluation. In the present study, the exposure assessment of the EatR with respect to landslide initiation provides that the about 25% each of settlement, population, agricultural land, and 49% of road network comes within the high category whereas with respect to combined susceptibility it is about 41%, 42%, 59%, and 72% of settlement, population, agricultural land, and road network respectively. Considering these consequences, the priority score maps of the individual element at risk with respect to combined susceptibility condition were evaluation The study suggests that the critical areas are located along the major trunk road (NH5), western part of the study area and along the NW-SE trending valleys (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The integration of all the priority maps clearly depicts the which vulnerable locality needs to be addressed with greater importance (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The availability of safe settlement in hilly terrain is very restricted. According to Zimmermann, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2004\u003c/span\u003e in the European Alps, earlier church, village recreational centre are located in safer place and with growing urbanization new settlements are located in proximity to probable hazard affected area. Thus, strict land-use planning and enforcement of zoning laws are essential, including restricting construction in high-risk areas and considering the relocation of vulnerable communities to achieve sustainable mountain development. In this context, the output maps of the present work will play pivotal role in prioritizing areas for future development, land-use planning. Further, the classified priority map indicates that only 5% of the total area to be given more preference in allocating the resource during the time of disaster. This spatial distribution of prioritized also help in the early warning system. The high to very high priority areas located along the NH5 covering major habitation like Nigulsari, Spillow, Pooh, Nako; Sangla \u0026ndash; Chitkul sector; and other NW-SE trending valleys (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) needs to be monitored remotely during the rainy season with installation of AWS and ARG coupled with ground-based sensor like seepage measuring devices, inclinometers, settlement measuring devices and implementation of early warning system. Slope stability can be improved by adopting some general measures like drainage arrangements, retaining structures and adopting bio-restoration measures along with redesigning roads for resilience while long term and permanent stabilisation/support systems can be implemented after the site-specific detailed geological and geotechnical investigations. Community awareness, people centric early warning systems, and emergency preparedness\u0026mdash;including evacuation plans and training\u0026mdash;must be prioritized. Coordination among departments, regular infrastructure maintenance, and securing funding for mitigation are key to building long-term resilience against landslides disaster.\u003c/p\u003e\u003cp\u003eThe future work may be conducted in a more constructive way through incorporation of the rockfall impact area, multi-hazard scenario, using of machine learning method which are the limitation of present work.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eLandslide susceptibility in the Himalayas is shaped by a combination of geological, topoclimatic factors which are specific to a particular sub basin or physiographic subdivision which can later be altered by human-induced factors such as slope modification through new road cuts/widening of existing road benches and construction of other infrastructural facilities. The Kinnaur region in Himachal Pradesh is especially prone to landslides due to its fragile geology, rugged topography, active tectonic environment, steep slopes, and frequent seismic activity, all of which contribute to the high occurrence of landslides while rainfall, snow melt, and seismic tremors act as trigger. Prioritizing areas based on their susceptibility to landslides becomes the cornerstone of effective risk mitigation strategies.\u003c/p\u003e\u003cp\u003eThe results of this study have demonstrated a noble approach in defining the element at risk and exposure to landslides considering both the source and runout zone in identifying the key localities through utilization of readily available geo-spatial data which will significantly enhance the disaster risk reduction efforts in vulnerable regions. Further, with the proposed priority ranking system the study helps in prioritizing the areas where disaster risk reduction (DRR) strategies can be implemented effectively through targeted interventions that enhance community resilience and reduce vulnerability. This study will greatly aid in land use planning, resource allocation during emergency, landslide forecasting system.\u003c/p\u003e\u003cp\u003eThis work can be easily replicated on a regional scale in landslide prone areas in Himalayas in order to plan and execute the focused disaster risk reduction and build disaster resilience amongst the local communities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Rabisankar Karmakar, Ankur Kumar Srivastava, and Qurban Ali Tariq. The first draft of the manuscript was written by Rabisankar Karmakar and Ankur Kumar Srivastava and all authors commented on previous versions of the manuscript. Writing - review and editing done by Harish Bahuguna. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlam, Sk \u0026amp; Mandal, Sujit \u0026amp; Maiti, Ramkrishna. (2021). A Probabilistic Landslide Risk Assessment (LRA) on NH31A and Settlement in Rorachu Watershed, East Sikkim, India by using Bivariate Models and Geospatial Techniques. 10.21203/rs.3.rs-1019380/v1.\u003c/li\u003e\n \u003cli\u003eAlthuwaynee, Omar F., Pradhan,B. (2017). 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Mountain Research and Development 24(11):19\u0026ndash;23.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Geological Survey of India","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Priority ranking, exposure, landslide initiation, runout, decision support system","lastPublishedDoi":"10.21203/rs.3.rs-7918993/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7918993/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Himalayan region is highly susceptible to landslides and has witnessed several fatal incidents in recent years. Implementing a priority-based ranking system for landslide management is essential to help decision-makers identify the most vulnerable or high-risk zones. This approach ensures that limited resources\u0026mdash;such as funding, manpower, and technical expertise\u0026mdash;are allocated efficiently. The prioritization process involves systematically evaluating and categorizing areas or projects based on the severity of risk, potential impact, and urgency of required interventions. By focusing on mitigation efforts, emergency preparedness, and infrastructure enhancement in the most at-risk zones, authorities can significantly reduce the likelihood of landslide-related disasters and strengthen community resilience. In this study, we propose a priority score ranking system for the Kinnaur district of Himachal Pradesh, based on the assessment of exposure levels of individual elements at risk, both in terms of landslide initiation and runout paths, along with their strategic importance. The resulting individual priority score maps and the integrated priority map are intended to support decision-making for a landslide early warning system and the effective allocation of resources for disaster risk reduction. Our findings indicate that approximately 5% area of the Kinnaur district should be prioritized for resource allocation to implement targeted mitigation strategies. This framework can be readily adapted on a regional scale for use in other landslide-prone regions to support focused planning and execution of disaster risk reduction initiatives.\u003c/p\u003e","manuscriptTitle":"Enhancing Decision Support System by Prioritizing Critical Areas in Landslide Disaster Management of Kinnaur District, Himachal Pradesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 04:18:07","doi":"10.21203/rs.3.rs-7918993/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9d131a13-0254-4252-9647-716e81fc7fc0","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56689052,"name":"Geology"}],"tags":[],"updatedAt":"2025-10-23T04:18:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 04:18:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7918993","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7918993","identity":"rs-7918993","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}