Flood Susceptibility Mapping to Identify the Vulnerable Areas in the Adayar River Basin at Chennai, Tamil Nadu

Flood Susceptibility Mapping to Identify the Vulnerable Areas in the Adayar River Basin at Chennai, Tamil Nadu | 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 Flood Susceptibility Mapping to Identify the Vulnerable Areas in the Adayar River Basin at Chennai, Tamil Nadu MANIMARAN ASAITHAMBI, Aritra Poddar, Gayathri Varatharajan, Aditya Aryan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4180384/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 Adayar River Basin in Chennai, Tamil Nadu, is plagued by recurring inundation events, posing substantial hazards to human settlements and critical infrastructure. In response, this research endeavors to develop a flood susceptibility map to pinpoint regions within the basin prone to flooding. Leveraging Geographic Information Systems (GIS) and employing the Analytical Hierarchy Process (AHP) methodology via GIS software, an array of spatial and non-spatial variables influencing flood susceptibility were meticulously examined and weighted. By integrating diverse hydrological, geological, and meteorological parameters and applying AHP's pairwise comparison, a holistic understanding of flood susceptibility was attained. The GIS approach enables visualizing spatial patterns and identifying high-risk flood areas. In this paper, the flood susceptibility map has been characterized into five different classes which include Very High region, High region, Moderate region, Low region, and Very Low region, based on this characterization a total of 40 vulnerable areas have been identified with 10 very high susceptible areas followed by 16 highly susceptible areas and 14 moderately susceptible areas. Flood Susceptibility Map Geographic Information System (GIS) Analytical Hierarchical Process (AHP) Pairwise Comparison Adayar River Basin Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1 Overview 1.1 Introduction Flooding denotes normally dry land's temporary submergence from overflowing waters of rivers, lakes, or other water bodies. (WMO and UNDRR). Floods are also responsible for approximately 5,000 deaths per year globally (WHO) and are the leading cause of internal displacement globally, affecting millions of people each year (IDMC). Floods also cost the global economy an average of 104 billion dollars annually (World Bank) and can result in epidemics of cholera, typhoid fever, and hepatitis A (WHO). India due to its unique geo-political and socio-economic conditions is vulnerable to floods (Annual Report NDMA GOI 2022–2023). In India, there are the availability of almost 329 million hectors (mha) of geographical area, among this more than 40 mha is flood-prone areas, which results in 75 lakh hectares of affected land, and 1600 deaths on an average year at the cost of 1805 crore rupees (NDMA GOI). India's complex flood situation renders flood management an arduous undertaking (Mohanty et al. 2020 ). Chennai is the capital of Tamil Nadu one of the important southern states of India. It consists of the Adayar River, Cooum River, Palar River, and Kosasthalaiyar River, among the following rivers Adayar River receives the maximum amount of rainfall throughout the year and is prone to flooding (Faiz Ahmed and Kranthi 2018 ; NRSC/ISRO 2015 ; Sharif et al. 2020 ). The Basin situated at Chennai is a part of the Tamil Nadu Coastal region and is in a moderately vulnerable area (Priya Rajan et al. 2020 ). In the past years, there has been a major involvement of Multi-Criteria Decision Analysis (MCDA) (Mateo 2012 ) along with GIS for mapping the vulnerability of any region against any natural disaster (Beheshtifar 2023 ; Sahnoun et al. 2012 ). The several methods that are available for MCDA include Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS), VlseKriteri-jumska Optimizacija I Kompromisno Resenje (VIKOR) in Serbian meaning multi-criteria optimization and compromise solution, Complex Proportional Assessment (COPRAS), Multi-objective Optimization by Ratio Analysis (MULTIMOORA), Preference Ranking Organization Method for the Enrichment of Evaluations and Graphical Analysis for Interactive Aid (PROMETHEE-GAIA), Multi-Attribute Utility Theory (MAUT), and AHP method. Each method has its special significance if the comparison is required AHP, MULTIMOORA, and MAUT are preferred; to find the best alternative from the provided options AHP, TOPSIS, VIKOR, and COPRAS are suitable and PROMETHEE-GAIA is based on pairwise comparison and confirmatory assessment for the desired purpose (Zlaugotne et al. 2020 ). Hence as per the requirement, this paper uses the AHP method because of its greater reliability and easy-to-use characteristics. 1.2 Scope of the Research 1.2.1 Understanding Vulnerability. The paper can delve into the factors that contribute to vulnerability to floods in the Adayar River Basin, such as topography, land use, urbanization, infrastructure, and climate change impacts. It can explore how these factors interact and contribute to the susceptibility of different areas to flooding. 1.2.2 Methodology Development. It can detail the methodology used for developing the flood susceptibility map, which may include data collection, remote sensing techniques, GIS (Geographic Information System) analysis, hydrological modeling, and statistical analysis. This section can contribute to the scientific literature by proposing innovative or improved methodologies for assessing flood susceptibility. 1.2.3 Mapping Techniques. It can discuss the specific mapping techniques employed, such as susceptibility index mapping, multi-criteria decision analysis, or machine learning algorithms. It can evaluate the effectiveness of different techniques in accurately identifying vulnerable areas and compare their results. 1.3 Methodology The project's main outcome is to develop a Flood Susceptible Map (FSM). FSM is a process used in hydrology and geography to assess the likelihood of an area being affected by flooding. It involves analyzing various factors such as topography, land use, soil type, rainfall patterns, or infrastructure to pinpoint regions that have a higher susceptibility to inundation. By integrating these factors into GIS, experts can create maps that highlight areas with different levels of susceptibility to flooding, ranging from low to high risk. It is valuable for disaster management, urban planning, and risk mitigation efforts. So, it is widely advocated to incorporate more than five factors to prevent biased weighting, which could otherwise lead to overemphasis on certain factors, potentially skewing the overall assessment (Kaya and Derin 2023 ). For the following paper, nine criteria have been selected, those are: 1.3.1 Elevation refers to the height of a point or surface above a reference point, usually sea level (Selvam and Antony Jebamalai 2023 ). It is commonly measured in meters or feet and is a critical factor in determining topographical features and landforms. Choosing elevation over height for the paper offers a more consistent and standardized measure referenced to sea level, facilitating accurate assessment of terrain features and water flow patterns 1.3.2 Slope represents the steepness or incline of a surface, usually expressed as a percentage or angle. It indicates how much a surface rises or falls over a certain horizontal distance and is essential in understanding drainage patterns and surface runoff (Dung et al. 2020 ). 1.3.3 Lithology refers to the physical characteristics and composition of rocks or sediments within the Earth's crust (Vojtek and Vojteková 2019 ). It encompasses properties such as rock type, mineral composition, texture, and structure and is essential for understanding groundwater Lithology flow, erosion, and geological processes. 1.3.4 Soil a natural blend of minerals, organic matter, water, air, and living organisms that make up the Earth's surface layer, serves as a growth medium for plants. It regulates water flow, and its water retention capacity plays a crucial role in identifying areas susceptible to flooding. (Chifflard et al. 2018 ). 1.3.5 Land Use Land Cover refers to the human activities and purposes for which land is utilized, such as residential, commercial, agricultural, or industrial purposes. Land cover, on the other hand, refers to the physical and biological coverage of the Earth's surface, including vegetation, water bodies, built-up areas, and bare soil (Brody et al. 2014 ; Kassaye et al. 2024 ; Sundaram et al. 2021 ). Both land use and land cover are essential for understanding landscape changes, environmental impacts, and socio-economic dynamics. 1.3.6 Topographical Wetness Index soil wetness spatially, computing upslope area-slope gradient ratio, pinpointing flood-prone, waterlogged zones.(Selvam and Antony Jebamalai 2023 ). It is calculated based on the ratio of the upslope contributing area to the slope gradient and is useful for identifying areas prone to waterlogging, saturation, and potential flooding. 1.3.7 Distance to Stream refers to the proximity of a location to a watercourse, such as a river, stream, or creek (Mehravar et al. 2023 ). It is measured in linear distance and is an essential factor in flood risk assessment, as areas closer to streams are more susceptible to flooding during high-flow events. 1.3.8 Drainage Density is a measure of the total length of stream channels within a given area (Rimba et al. 2017 ). It quantifies the degree of branching and interconnectedness of the stream network and is indicative of the efficiency of surface water drainage. Higher drainage density implies better natural drainage and reduced flood risk in the area. 1.3.9 Annual Rainfall Distribution refers to the spatial and temporal patterns of precipitation over a specific geographic area during a given year (WMO). It includes information on the amount, intensity, frequency, and duration of rainfall events, which influence hydrological processes, water availability, and flood risk (Breinl et al. 2021 ). 2 Planning and Methods 2.1 Prelude Inundations of typically dry areas by overflowing water characterize the natural disasters known as floods. They can arise from diverse causes like excessive precipitation, rapid snow thawing, coastal storm surges, or the failure of dams (Merz et al. 2021 ). Floods can occur gradually or suddenly, causing widespread damage to infrastructure, homes (Nadal et al. 2010 ), agriculture (Kumar et al. 2022 ), and posing risks to human life (Jonkman 2014 ). They can have devastating impacts on communities, ecosystems, and economies (Parida et al. 2022 ), making flood preparedness and management crucial for minimizing their effects. 2.2 Study Area The Adyar River originates as a stream from the Malaipattu tank situated near Manimangalam village in the Sriperumbudur taluk, which is approximately 15 km west of Tambaram in South Chennai. It gains significant water flow from the point where the release from a lake joins the river at Thiruneermalai. The river traverses through the districts of Kancheepuram, Tiruvallur, and Chennai over a distance of about 42.5 km before emptying into the Bay of Bengal at Adyar, Chennai. The study area under consideration lies geographically between the northern latitudes of 10°15' to 10°25' and eastern longitudes of 79°20' and 79°55', spanning an area of 750 square kilometers. (Arivazhagan et al. 2019 ). The depth of the Adyar River varies, with an approximate depth of 0.75 meters in its upper reaches and 0.5 meters in its lower reaches. The river's catchment area spans 530 square kilometers. The width of its bed ranges from 10.5 meters to 200 meters. Within the Chennai Metropolitan area, the river flows for a distance of 24 kilometers, with around 15 kilometers of its course lying within the Chennai district before it drains into the sea. Annually, the Adyar River discharges between 190 to 940 million cubic meters of water into the Bay of Bengal. This discharge is seasonal, with the flow being 7 to 33 times higher than the annual average during the Northeast monsoon season, which occurs between September and December. The study area map is in Fig. 1 . 2.3 Data Collection The collection of data follows the procedure as shown in Fig. 2 . The collection of the data includes the following sources HydroSHEDS Data ( https://www.hydrosheds.org/ ), FAO Data ( https://www.fao.org/soils-portal/en/ ), USGS Data ( https://www.usgs.gov/ ), BHUKOSH Data ( https://bhukosh.gsi.gov.in/Bhukosh/Public ), SOI Data ( https://onlinemaps.surveyofindia.gov.in/Home.aspx ), ( https://www.climateurope.eu/datasets-climatic-research-unit-cru/ ) CRU Data, and ( https://sedac.ciesin.columbia.edu/data/set/india-india-village-level-geospatial-socio-econ-1991-2001 ) SEDAC. Furthermore, the Land use land Cover map is developed from the 2023 data, and the Annual Rainfall Distribution Map was developed on the data available from the year 2012 to 2022 that is past consecutive 10 years data. Digital Elevation Model (DEM) data as shown in Fig. 3 plays a crucial role in assessing flood susceptibility in any urban area (Huang et al. 2023 ) by providing detailed information about the topography of an area, some of its uses are: Terrain Analysis : By analyzing the slope and aspect of the terrain derived from DEMs, experts can identify areas prone to flooding, such as low-lying areas or regions with steep slopes where water is likely to accumulate. Floodplain Mapping : DEMs are used to demarcate floodplains - areas flanking rivers, and streams susceptible to inundation during high flow episodes. Risk Assessment and Planning : DEM-derived flood susceptibility maps provide valuable information for land use planning, emergency preparedness, and risk mitigation. 2.4 Criteria Explanation The above-stated nine factors have been generated as a raster grid of 30 m × 30 m in ArcMap 9.2 for the application of the AHP method and the significance of each of the developed criteria maps based on their segregated classes has been elaborated below. 2.4.1 Development of Elevation Map . Low-lying areas, such as floodplains, are more susceptible to flooding because water naturally accumulates in these areas. Understanding elevation variations helps identify areas prone to inundation during heavy rainfall or river overflow. In Fig. 4 , the elevation map of the Adayar River Basin is divided into five classes based on the percentage rise which ranges from 0 to 25 is the first class with the least elevation, and a gradual increase in elevation followed by 25.1 to 72 being the second class, 72.1 to 115 being third class, 116 to 154 being fourth class, 155 to 223 finally being the fifth class with the highest elevation value. 2.4.2 Development of Slope Map Steep slopes accelerate runoff, increasing the likelihood of flash floods. Conversely, flat areas might retain water longer, leading to prolonged inundation. By analyzing slope gradients, predictions can be made of how water will flow across the landscape during rainfall events. In Fig. 5 , the elevation map of the Adayar River Basin is divided into five classes which range from 0 to 1.61 m the first class with the least slope, and the gradual increase of the slope followed by 1.62 to 4.51 m is the second class, 4.52 to 12.6 m being third class, 12.7 to 28.4 m being fourth class, 28.5 to 82.2 m finally being fifth class with the highest slope value. 2.4.3 Development of Lithology Map Different rock types have varying permeability rates, which can lead to increased or decreased surface runoff. Understanding the composition of the terrain helps assess how water will interact with the landscape. In Fig. 6 the lithology map of the Adayar River Basin the region consists of four different classes of rocks those are Charnockite Gneiss rock (30%), which is a type of crystalline rock formation that tends to have low porosity and permeability, meaning it doesn't hold or transmit water well; Upper Gondwana (27%), these rocks are often composed of sandstones and shales, which can have moderate water-holding capacity compared to crystalline rocks but may not be as effective as fluvial sediments; Lower Gondwana (10%), Similar to Upper Gondwana, these rocks are sedimentary but they may have slightly higher water-holding capacity due to different depositional environments; Fluvial (33%), these rocks deposited by rivers, tend to have the highest water-holding capacity amongst all types and often consist of well-sorted and well-rounded particles with good porosity and permeability, making them efficient at storing and transmitting water. Thus, based on the characteristics of the prevailing rocks the region consisting of Fluvial rocks is most susceptible to flood flowed by Upper Gondwana, Lower Gondwana, and least in the Charnockite Gneiss rock region. 2.4.4 Development of Soil Map Soil characteristics influence infiltration rates and water retention capacity. By examining soil properties, anticipation can be performed on how quickly water will move through the ground. In Fig. 7 the soil map of the Adayar River Basin the region consists of three different classes of soils those are, Dystric Regosols (6%), soils are often characterized by low organic matter content and coarse texture, which typically results in lower water holding capacity compared to soils with more organic matter and finer texture; Orthoc Luvisols (92%), these soils have some characteristics that improve water retention compared to Dystric Regosols, generally have less clay content and lower organic matter content than Cromic Luvisols, resulting in a lower overall water holding capacity; Cromic Luvisols (2%), these soils tend to have a higher clay content and more organic matter, which both contribute to their greater water holding capacity compared to the other two soil types mentioned. The presence of clay particles allows them to retain more water, and higher organic matter content enhances soil structure, improving water retention further. Thus, based on the characteristics of the prevailing soils the region consisting of Dystric Regosols is most susceptible to flood flowed by Orthoc Luvisols and least in Chromic Luvisols region. 2.4.5 Development of Land Use Land Cover Map Urbanization and deforestation reduce natural infiltration and increase surface runoff, elevating flood risk. Conversely, areas with vegetation cover can help absorb and retain water, mitigating flood impacts. Understanding land use and land cover changes enables us to anticipate shifts in flood susceptibility. In Fig. 8 the land uses a land cover map of the Adayar River Basin the region consists of five different classes those are Water Bodies (4%), High-Density Settlement (34%), Low-Density Settlement (33%), Vegetation (27%) and Barren Land (2%). Naturally, water bodies are already flooded areas, so they are the most susceptible to flooding; Vegetated areas can absorb some water and reduce surface runoff, making them less susceptible to flooding compared to built-up areas; density settlements may have some permeable surfaces and green spaces, which can help mitigate flooding to some extent; High density settlements often have large areas of impermeable surfaces like concrete and asphalt, which increase surface runoff and make them more susceptible to flooding; Barren land typically lacks vegetation and has little to no soil cover to absorb or slow down water, so it can be highly susceptible to flooding. 2.4.6 Development of Topographical Wetness Index Map This index integrates various topographic factors to quantify the propensity of a location to accumulate water. This index is developed with the help of the f ormula (Beven and Kirkby 1979 ) TWI = ln (α / tan β) (1) where α is the local upslope area draining through a certain point per unit concentration length and tan β is the local slope. In Fig. 9 the topographical wetness index map of the Adayar River Basin the region consists of five different levels ranging from − 9.08 to − 4.33 being first class, − 4.32 to -2.25 being second class, − 2.24 to − 0.168 being third class, 0.169 to 3.42 being fourth class, 3.43 to 12.2 being fifth class. The ranges can be categorized respectively as Extremely Dry areas with very low wetness, likely corresponding to high points or steep slopes where water runoff is rapid, and little water accumulates; Relatively Dry areas with low wetness include moderately sloped terrain where water runoff occurs more slowly compared to the previous category, Moderately Wet areas with some potential for water accumulation and could encompass gently sloping terrain or areas with moderate upslope contributing areas; Higher Wetness area with increased potential for water accumulation, likely include flat or low-lying areas where water tends to pool and saturate the soil; Very Wet areas with high potential for water accumulation correspond to floodplains or other prolonged inundation areas. 2.4.7 Development of Distance to Stream Map. Proximity to rivers and streams influences flood risk, as areas adjacent to water bodies are more susceptible to inundation during periods of high flow. Mapping distances to waterways helps identify areas at heightened risk of flooding. In the Fig. 10 distance to stream map of the Adayar River Basin the region consists of five different classes consisting of 500 m being first class with the highest prone to flooding, and gradually decreasing by 750 m being second class, 1000 m being third class, 1250 m being fourth class, and finally 2500 m being the fifth class with least flooding. 2.4.8 Development of Drainage Density Map. In Fig. 11 elaborates on the drainage density map of the Adayar River Basin. The drainage density of 336.422 m 3 /km suggests that the area has a dense network of streams and rivers, hence efficient water drainage and less susceptible to flooding. A moderately high drainage density of 252.317 m 3 /km indicates that the area still has a considerable number of streams and rivers, but perhaps not as dense as the previous category. The moderate drainage density of 168.211 m 3 /km suggests a somewhat less dense network of streams and rivers. A moderately low drainage density of 84.1055 m 3 /km indicates slower drainage, leading to increased flood risk during heavy rainfall. A drainage density of 0 m 3 /km indicates that there are no streams or rivers present in the area increasing flood susceptibility. 2.4.9 Development of Annual Rainfall Distribution Map Intense rainfall over short periods can overwhelm drainage systems, leading to localized flooding. By analyzing historical rainfall patterns and future climate projections, we can assess the frequency and intensity of rainfall events, aiding in flood risk assessment. In the formation of this map, the Inverse Distance Weighted average (IDW) method has been used as it has the lowest error and maximum model efficiency (Knight et al.). The IDW method assumes that values closer to the location being estimated have more influence than those farther away. The influence of each sampled point on the estimation at an unsampled location is weighted based on its distance from that location. The weights assigned to each sampled point are inversely proportional to their distance from the estimation location. This means that closer points have higher weights, while points farther away have lower weights. The formula for calculating the weight of each sample point is given by w i = 1/d i p (2) Where w i is the weight, d i is the distance between the estimation location, i is the sample point, and p is a power parameter that controls the rate at which the weights decrease the distance. Once the weights are calculated for each sampled point, the estimated value at the unsampled location is obtained by averaging the measured values at the sampled locations, weighted by their respective weights. The formula for the estimated value (Z) at a given location is typically Z = \(\frac{\sum _{i=1}^{n}{w}_{i}{Z}_{i}}{\sum _{i=1}^{n}{w}_{i}}\) (3) Where Z i is the measured rainfall value at sampled point I, n is the total number of sampled points. Finally in ArcGIS, the study area is typically divided into a grid of cells, and the IDW method is applied to estimate rainfall values at each cell center. Finally, the result in a raster layer representing the interpolated annual rainfall distribution across the study area. In Fig. 12 the rainfall distribution map of the Adayar River Basin the region has been classified into five different sectors ranging from 3.994 to 3.999 mm/year as the first sector, 4 to 4.004 mm/year as the second sector, 4.005 to 4.01 mm/year as the third sector, 4.011 to 4.015 mm/year as a fourth sector, 4.016 to 4.02 mm/year as a fifth sector. Thus, the regions experiencing maximum rainfall are prone to flood. . Figure 12. Rainfall Distribution Map of Adayar River Basin 3 Analysis and Result For assessing flood-prone areas in the Adayar River Basin, the AHP process was utilized (Ghosh and Kar 2018 ; Mitra et al. 2022 ; Princecharles Chukwuemeka Anyadiegwu et al. 2021 ). AHP, a decision-making framework by Thomas L. Saaty in 1988, evaluates and prioritizes multiple criteria through pairwise comparisons, aiding complex decisions systematically. AHP analysis procedure involves: (Ali et al. 2019 ; Bagyaraj et al. 2023 ; Chakraborty and Mukhopadhyay 2019 ; Mukherjee and Deb 2023 ; Selvam and Antony Jebamalai 2023 ): First of all, the formation of a pairwise comparison matrix based on the scale of relative importance as per Satty tabulation (1980), is shown in Table 1 . Table 1 Fundamental Scale of pair-wise comparison matrix (Satty 1980 ) Intensities of importance Definition 1 Equal Importance 3 Low importance of one over another 5 Strong or essential importance 7 Established importance 9 Absolute or high importance 2, 4, 6, 8 Intermediate values between the two adjacent importance or judgments Reciprocals Reciprocals if criteria i has one of the above numbers designated to it when compared with criteria j , then j has the reciprocal value when compared with i . Then transferring to the normalized pairwise matrix by dividing all the elements of the column by the sum of all the elements of the column and hence, calculating the consistency matrix to find the Consistency Index (CI) with the formula CI = \(\frac{{\lambda }\text{max}- n}{n-1}\) (4) Where λ max refers to the maximum eigenvalue of the pairwise comparison matrix and n refers to the number of compared elements. After that Random Index is calculated from Table 2 and finally, the Consistency Ratio (CR) is calculated as the ratio of CI to RI. If the value of CR is less than 0.1 the obtained criteria weights are correct. Table 2 Random Inconsistency Indices for n = 10 (Satty 1980 ) n 1 2 3 4 5 6 7 8 9 10 RI 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 Based on the selected criteria, with the help of Table 1 and Table 2 , the consistency matrix as shown in Table 3 is developed. After the preparation of the consistency matrix since the value of CR came to be less than 1 the assumed criteria weighted correctly and based on this Percentage Criteria Weight the FSM is prepared as shown in Fig. 13 and the Susceptibility Class Ratings and the Percentage Criteria Weights are mentioned in Table 4 . Table 3. Consistency Matrix for Analytical Hierarchical Process % Criteri Weights 2 3 4 5 7 12 15 22 30 CR = 0.041 < 0.100 (Hence correct) 9.203 9.109 9.108 9.243 9.420 9.607 9.791 9.894 9.855 Criteria Weights 0.019 0.026 0.037 0.053 0.076 0.115 0.154 0.217 0.304 Weighted Sum Value 0.175 0.237 0.337 0.490 0.716 1.105 1.508 2.147 2.996 RI = 1.45 Rainfall Distribution 0.034 0.038 0.043 0.051 0.061 0.101 0.101 0.152 0.304 Drainage Density 0.027 0.031 0.036 0.043 0.054 0.109 0.109 0.217 0.434 CI = 0.059 Distance to Stream 0.022 0.026 0.031 0.039 0.051 0.077 0.154 0.308 0.462 TWI 0.019 0.023 0.029 0.038 0.058 0.012 0.230 0.345 0.460 λ max = 9.470 LULC 0.015 0.019 0.025 0.038 0.076 0.152 0.228 0.304 0.380 Soil 0.013 0.018 0.027 0.053 0.106 0.159 0.212 0.265 0.318 Lithology 0.012 0.019 0.037 0.074 0.011 0.148 0.185 0.222 0.259 Slope 0.013 0.026 0.052 0.078 0.104 0.130 0.156 0.182 0.208 Elevation 0.019 0.038 0.057 0.076 0.095 0.114 0.133 0.152 0.171 Factors Elevation Slope Lithology Soil LULC TWI Distance to Stream Drainage Density Rainfall Distribution Table 4. Parameter Rank and Percentage Criteria Weights Flood Causative Criteria Unit Class Susceptibility Class Ratings % Criteria Weights Elevation m 0 to 25 5 2 25.1 to 72 4 72.1 to 115 3 116 to 154 2 155 to 223 1 Slope % 0 to 1.61 5 3 1.62 to 4.51 4 4.52 to 12.6 3 12.7 to 28.4 2 28.5 to 82.2 1 Lithology level Fluvial 4 4 Upper Gondwana 3 Lower Gondwana 2 Charmockite 1 Soil level Chromic Luvisols 3 5 Orthic Luvisols 2 Dystric Regosols 1 LULC level Barren Land 1 7 Vegetation 2 Water Bodies 3 Low Density Pop. 4 High Density Pop. 5 TWI level - 9.08 to - 4.33 1 12 - 4.32 to - 2.25 2 - 2.24 to - 0.168 3 0.169 to 3.42 4 3.43 to 12.2 5 Distance to Stream m 500 5 15 750 4 1000 3 1250 2 2500 1 Drainage Density m 3 /km 336.422 to 252.317 1 22 252.3147 to 168.211 2 168.211 to 84.1055 3 84.1055 to 0.00 4 Annual Rainfall Distribution mm/year 3.994 to 3.999 1 30 4.00 to 4.004 2 4.005 to 4.010 3 4.011 to 4.015 4 4.016 to 4.020 5 As per the obtained FSM, it is observed that the maximum percentage criteria weight is experienced by Annual Rainfall Distribution with a value of 30% followed by Drainage Density at 22%, Distance to stream at 15%, TWI at 12%, LULC at 7%, Soil at 5%, Lithology at 4%, Slope at 3%, Elevation at 2% hence it is conclusive that the Annual Rainfall Distribution plays the vital role in determining the flood-prone area in Adayar River Basin and Elevation affects the least. Furthermore, the FSM is divided into five different classes viz. Very Low flood-prone area, Low flood-prone area, Moderate flood-prone area, High flood-prone area, and Very High flood-prone area with a percentage distribution of 10%, 22%, 29%, 25%, and 13% respectively. It is also evident from the FSM that the headwaters are the main regions that are susceptible to flood as they experience maximum annual rainfall, low drainage density, very wet areas, covered by mostly orthic luvisols, and moderately sloped and moderately elevated. Based on the following characteristics the installation areas are selected emphasizing mainly the three regions of FSM that are a very highly susceptible region (10 areas), high susceptible region (16 areas), an intermediate susceptible region (14 areas), and those of total 40 regions are mentioned in Table 5 and the pictorial presentation of the areas are mentioned in Fig. 14 . Table 5 Categories of the selected areas Susceptibility ratings Name of the Areas Very High Susceptible Guduperembedu, Karanaipuducheri, Karasangal, Keelakalani, Korukkanthangal, Padappai, Valathancheri, Vandalur, Vellarai, Venjuvancheri High Susceptible Adhanur, Erumaiyur R.F., Irumbedu, Irumngattukottai, Kaduvancheri, Katrambakkam, Kiloy, Nallur R.F., Nemam, Nemili, Pondur, Sembarambakkam, Sethupattu, Sirumathur, Sriperumbadur, Varadharajapuram Intermediate Susceptible Chennai, Gudupakkam, Kattupakkam, Kavam, Kilmanambedu, Malayambakkam, Manapakkam, Mangadu, Mevalurkuppam, Mugalivakkam, Poprur, Ramapuram, Thodukadu, Valasaravakkam 4 Conclusion In conclusion, the research work represents a significant step towards mitigating the impacts of recurrent flooding in the region. By integrating spatial and non-spatial factors and employing a robust decision-making framework, this study has provided valuable insights into understanding the complex dynamics of flood vulnerability within the basin. The findings of this research highlight the importance of considering factors such as topography, land use land cover, soil type, topographical wetness index, annual rainfall distribution, and proximity to water bodies in assessing flood susceptibility. Through the visualization of spatial patterns and identifying high-risk areas, decision-makers are empowered to prioritize mitigation strategies and allocate resources effectively. Furthermore, the development of the flood susceptibility map serves as a crucial tool for enhancing disaster management and urban planning efforts in the Adayar River Basin. By identifying vulnerable areas and informing proactive measures, such as infrastructure improvements, land use planning, and early warning systems, the resilience of communities and infrastructure can be significantly enhanced. Ultimately, this research highlights the significance of utilizing geospatial tools and decision support systems to tackle the issues presented by natural hazards, thereby fostering sustainable development and enhancing the resilience of the communities residing within the Adayar River Basin region. Further Research Prospects Refinement of Methodologies. Continuously improving and refining the methodologies used for flood susceptibility mapping can enhance the accuracy and reliability of the results. Future research could explore advanced remote sensing techniques, machine learning algorithms, and high-resolution modeling approaches to better capture the complex interactions of factors influencing flood vulnerability. Dynamic Mapping. Approaches. Developing dynamic flood susceptibility maps that can adapt to changing environmental conditions and human activities could be an area of future research. Incorporating real-time data streams, such as rainfall forecasts, river flow monitoring, and land use changes, can improve the timeliness and responsiveness of flood risk assessments. Community-Based Monitoring and Early Warning Systems. Engaging local communities in flood monitoring and early warning systems can enhance resilience and preparedness at the grassroots level. Future research could explore participatory approaches for collecting and disseminating flood-related information, leveraging community networks and indigenous knowledge systems. Cross-Sectoral Collaboration. Promoting interdisciplinary collaboration between researchers, policymakers, urban planners, engineers, and community stakeholders is crucial for addressing complex flood vulnerability issues. Future studies could explore innovative frameworks for cross-sectoral knowledge exchange and collaboration to facilitate integrated flood risk management. Ecosystem-Based Approaches. Investigating the role of natural ecosystems, such as wetlands, mangroves, and floodplains, in mitigating flood risk could be an emerging area of research. Understanding the ecosystem services provided by these habitats and incorporating them into flood susceptibility mapping can lead to nature-based solutions for reducing vulnerability. Policy Implementation and Evaluation. Assessing the effectiveness of policies and interventions based on flood susceptibility mapping is essential for informed decision-making. Future research could focus on evaluating the impact of land use regulations, infrastructure investments, and disaster risk reduction measures on reducing flood risk and enhancing community resilience. 4.1 Limitations Data Limitations. Availability and quality of data, including topographic data, land use/land cover data, rainfall data, and historical flood data, can significantly impact the accuracy of the susceptibility map. Limited or outdated data may result in inaccuracies in identifying vulnerable areas. Scale and Resolution. The scale and resolution of data used in the study can affect the precision of the susceptibility map. Fine-scale data may not be available or feasible to obtain, particularly in densely populated or remote areas, leading to coarse-resolution maps that may overlook localized flood risks. Model Uncertainty. Flood susceptibility mapping involves the use of various modeling techniques and assumptions, leading to inherent uncertainties in the results. Uncertainties may arise from model parameters, input data, and modeling algorithms, which can affect the reliability and interpretation of the susceptibility map. Complexity of Flood Processes. Flood susceptibility mapping often simplifies the complex interactions between natural and anthropogenic factors influencing flood risk. It may overlook localized phenomena such as urban drainage systems, floodplain dynamics, and hydraulic infrastructure, which can significantly influence flood susceptibility. Temporal Dynamics. Flood susceptibility is dynamic and can change over time due to land use changes, urbanization, climate variability, and adaptation measures. A static susceptibility map may become outdated quickly, necessitating periodic updates and revisions to reflect evolving flood risk scenarios. Declarations Acknowledgment. The author expresses their gratitude to the SRM Institute of Science and Technology, Kattankulathur for their assistance and exceptional laboratory resources in facilitating the study. The provision of complementary data dissemination by the Survey of India, United States Geological Survey (USGS), BHUKOSH, HydroSHEDS, Climatic Research Unit (CRU), and Food and Agriculture Organization of the United Nations (FAO) is highly valued in fulfillment of the manuscript. Authors' Contribution. The conceptualization; methodology; investigation and design of the study involved contributions from all the authors. Aritra Poddar, Gayathri Varatharanjan, and Aditya Aryan were responsible for material preparation, data collection, and analysis. Manimaran Asaithambi wrote the Original draft of the manuscript; validated; supervised; wrote – reviewed and editing of the final manuscript. Data Availability. Data will be shared on request. 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. References Ali S A, Khatun R, Ahmad A, Ahmad (2019) Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India. Modeling Earth Systems and Environment, 5(3), 1083–1102 https://doi.org/10.1007/s40808-019-00593-z Annual Report NDMA GOI (2022-2023) Arivazhagan R, Sureshraj R I, Thenmozhi E, Bhuvaneshwari B, Vishnu N (2019) Numerical modelling of adyar river. International Journal of Recent Technology and Engineering, 8(3), 6965–6970 https://doi.org/10.35940/ijrte.C6012.098319 Bagyaraj M, Senapathi V, Chung S Y, Gopalakrishnan G, Xiao Y, Karthikeyan S, Nadiri A, Barzegar R (2023) A geospatial approach for assessing urban flood risk zones in Chennai, Tamil Nadu, India. Environmental Science and Pollution Research, 30(45), 100562–100575 https://doi.org/10.1007/s11356-023-29132-1 Beheshtifar S (2023) Identification of landslide-prone zones using a GIS-based multi-criteria decision analysis and region-growing algorithm in uncertain conditions. Natural Hazards, 115(2), 1475–1497 https://doi.org/10.1007/s11069-022-05603-5 Beven K J, Kirkby M J (1979) A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin, 24(1), 43–69 https://doi.org/10.1080/02626667909491834 Breinl K, Lun D, Müller Thomy H, Blöschl G (2021) Understanding the relationship between rainfall and flood probabilities through combined intensity-duration-frequency analysis. Journal of Hydrology, 602 https://doi.org/10.1016/j.jhydrol.2021.126759 Brody S, Blessing R, Sebastian A, Bedient P (2014) Examining the impact of land use/land cover characteristics on flood losses. Journal of Environmental Planning and Management, 57(8) https://doi.org/10.1080/09640568.2013.802228 Chakraborty S, Mukhopadhyay S (2019) Assessing flood risk using analytical hierarchy process (AHP) and geographical information system (GIS): application in Coochbehar district of West Bengal, India. Natural Hazards, 99(1), 247–274 https://doi.org/10.1007/s11069-019-03737-7 Chifflard P, Kranl J, Strassen G Zur, Zepp H (2018) The significance of soil moisture in forecasting characteristics of flood events. A statistical analysis in two nested catchments. Journal of Hydrology and Hydromechanics, 66(1) https://doi.org/10.1515/johh-2017-0037 Dung N B, Minh D T, Ahmad A, Long N Q (2020) The role of relative slope length in flood hazard mapping using ahp and GIS (Case study: Lam river basin, Vietnam). Geography, Environment, Sustainability, 13(2) https://doi.org/10.24057/2071-9388-2020-48 Faiz Ahmed C, Kranthi N (2018) Flood Vulnerability Assessment using Geospatial Techniques: Chennai, India. Indian Journal of Science and Technology, 11(6), 1–13 https://doi.org/10.17485/ijst/2018/v11i6/110831 Ghosh A, Kar S K (2018) Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal, India. Natural Hazards, 94(1) https://doi.org/10.1007/s11069-018-3392-y Huang H, Liao W, Lei X, Wang C, Cai Z, Wang H (2023) An urban DEM reconstruction method based on multisource data fusion for urban pluvial flooding simulation. Journal of Hydrology, 617 https://doi.org/10.1016/j.jhydrol.2022.128825 Jonkman S (2014) Loss of Life Due to Floods: General Overview. In Drowning https://doi.org/10.1007/978-3-642-04253-9_148 Kassaye S M, Tadesse T, Tegegne G, Hordofa A T (2024) Integrated impact of land use/cover and topography on hydrological extremes in the Baro River Basin. Environmental Earth Sciences, 83(2) https://doi.org/10.1007/s12665-023-11378-0 Kaya C M, Derin L (2023) Parameters and methods used in flood susceptibility mapping: a review. In Journal of Water and Climate Change (Vol. 14, Issue 6, pp. 1935–1960), IWA Publishing https://doi.org/10.2166/wcc.2023.035 Knight Y, Yu B, Jenkins G, Morris K, Comparing rainfall interpolation techniques for small subtropical urban catchments. Kumar H, Karwariya S K, Kumar R (2022) Google Earth Engine-Based Identification of Flood Extent and Flood-Affected Paddy Rice Fields Using Sentinel-2 MSI and Sentinel-1 SAR Data in Bihar State, India. Journal of the Indian Society of Remote Sensing, 50(5) https://doi.org/10.1007/s12524-021-01487-3 Mateo J R S C (2012) Multi-criteria analysis. Green Energy and Technology, 83, 7–10 https://doi.org/10.1007/978-1-4471-2346-0_2 Mehravar S, Razavi-Termeh S V, Moghimi A, Ranjgar B, Foroughnia F, Amani M (2023). Flood susceptibility mapping using multi-temporal SAR imagery and novel integration of nature-inspired algorithms into support vector regression. Journal of Hydrology, 617 https://doi.org/10.1016/j.jhydrol.2023.129100 Merz B, Blöschl G, Vorogushyn S, Dottori F, Aerts J C J H, Bates P, Bertola M, Kemter M, Kreibich H, Lall U, Macdonald E (2021) Causes, impacts and patterns of disastrous river floods. In Nature Reviews Earth and Environment (Vol. 2, Issue 9, pp. 592–609). Springer Nature https://doi.org/10.1038/s43017-021-00195-3 Mitra R, Saha P, Das J (2022) Assessment of the performance of GIS-based analytical hierarchical process (AHP) approach for flood modeling in Uttar Dinajpur district of West Bengal, India. Geomatics, Natural Hazards and Risk, 13(1) https://doi.org/10.1080/19475705.2022.2112094 Mohanty M P, Mudgil S, Karmakar S (2020) Flood management in India: A focussed review on the current status and future challenges. In International Journal of Disaster Risk Reduction (Vol. 49) Elsevier Ltd. https://doi.org/10.1016/j.ijdrr.2020.101660 Mukherjee R, Deb P (2023) Application of GIS-based analytical hierarchy process for assessment and mapping of flood risk zone in the lower Ramganga River Basin, Western Gangetic Plain, India. Environment, Development and Sustainability https://doi.org/10.1007/s10668-023-02957-z Nadal N C, Zapata R E, Pagán I, López R, Agudelo J (2010) Building Damage due to Riverine and Coastal Floods. Journal of Water Resources Planning and Management, 136(3) https://doi.org/10.1061/(ASCE)wr.1943-5452.0000036 NRSC/ISRO (2015) Hydrological Simulation Study of Flood Disaster in Adyar and Cooum Rivers, Tamilnadu. Parida Y, Roy Chowdhury J, Saini S, Dash D P (2022) Role of income and government responsiveness in reducing the death toll from floods in Indian states. Scientific Reports, 12(1) https://doi.org/10.1038/s41598-022-21334-w Princecharles Chukwuemeka Anyadiegwu, Joel Izuchukwu Igbokwe, Adeyemi James Adeboboye (2021) Assessment of flood hazard in Owerri West, Imo State, Nigeria using Analytical Hierarchical Process and Index-Based Approach. World Journal of Advanced Research and Reviews, 12(2) https://doi.org/10.30574/wjarr.2021.12.2.0517 Priya Rajan S M, Nellayaputhenpeedika M, Tiwari S P, Vengadasalam R (2020) Mapping and analysis of the physical vulnerability of coastal Tamil Nadu. Human and Ecological Risk Assessment, 26(7), 1879–1895 https://doi.org/10.1080/10807039.2019.1602752 Rimba A, Setiawati M, Sambah A, Miura F (2017) Physical Flood Vulnerability Mapping Applying Geospatial Techniques in Okazaki City, Aichi Prefecture, Japan. Urban Science, 1(1) https://doi.org/10.3390/urbansci1010007 Sahnoun H, Serbaji M, Karray B, Medhioub K (2012) GIS and multi-criteria analysis to select potential sites of agro-industrial complex. Environmental Earth Sciences, 66(8) https://doi.org/10.1007/s12665-011-1471-4 Selvam R A, Antony Jebamalai A R (2023) Application of the analytical hierarchy process (AHP) for flood susceptibility mapping using GIS techniques in Thamirabarani river basin, Srivaikundam region, Southern India. Natural Hazards, 118(2), 1065–1083 https://doi.org/10.1007/s11069-023-06037-3 Sharif M, Dhillon M S, Chandra S, Kumar M, Vasanthakumar V (2020) Urban Flooding—A Case Study of Chennai Floods of 2015. In Lecture Notes in Civil Engineering (Vol. 58) https://doi.org/10.1007/978-981-15-2545-2_64 Sundaram S, Devaraj S, Yarrakula K (2021) Modeling, mapping, and analysis of urban floods in India—a review on geospatial methodologies. In Environmental Science and Pollution Research (Vol. 28, Issue 48) https://doi.org/10.1007/s11356-021-16747-5 Satty T L (1980) The Analytical Hierarchy Process, McGraw–Hill, New York Vojtek, M, Vojteková J (2019) Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water (Switzerland), 11(2) https://doi.org/10.3390/w11020364 Zlaugotne B, Zihare L, Balode L, Kalnbalkite A, Khabdullin A, Blumberga D (2020) Multi-Criteria Decision Analysis Methods Comparison. Environmental and Climate Technologies, 24(1) https://doi.org/10.2478/rtuect-2020-0028 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4180384","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":285862693,"identity":"ad48be76-b938-4938-a534-84ae15ae11b7","order_by":0,"name":"MANIMARAN ASAITHAMBI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie2OMQtBURiGP8uxqLuy8AvUp1s2+SvfSbEcUlaD6VrE6meQkvFwy3RlPRYxMNmUqJuciw33shnOM7zf8j69H4DB8KfIIKwgCJ5J3yip1r2M3yl3UD4ORPaz7cV0pprrjL1c7AYb3wcrLhC2k89K3quRK+aN3FhV8oo7CKnOAYF7IYoU6ApGsbFiTPEWAiq9wp0QZXnQypWKo96cKfIRipGKLrhVh/gAylpheiUZqeiVapdKfaUV7tiJpLevy/DHhH0UJyr09GOrs59OW+3ScHsJUV5IBCF/EAwGg8HwhhtGjlfQd4slQQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8600-666X","institution":"SRM Institute of Science and Technology (Deemed to be University)","correspondingAuthor":true,"prefix":"","firstName":"MANIMARAN","middleName":"","lastName":"ASAITHAMBI","suffix":""},{"id":285862694,"identity":"1f12127f-ecd1-4269-a716-20051ec1e32a","order_by":1,"name":"Aritra Poddar","email":"","orcid":"","institution":"SRM Institute of Science and Technology (Deemed to be University)","correspondingAuthor":false,"prefix":"","firstName":"Aritra","middleName":"","lastName":"Poddar","suffix":""},{"id":285862695,"identity":"16e61c27-e688-433b-9a71-482df9c48f72","order_by":2,"name":"Gayathri Varatharajan","email":"","orcid":"","institution":"SRM Institute of Science and Technology (Deemed to be University)","correspondingAuthor":false,"prefix":"","firstName":"Gayathri","middleName":"","lastName":"Varatharajan","suffix":""},{"id":285862696,"identity":"039c1f51-2840-4830-af09-5c3f44e0c94b","order_by":3,"name":"Aditya Aryan","email":"","orcid":"","institution":"SRM Institute of Science and Technology (Deemed to be University)","correspondingAuthor":false,"prefix":"","firstName":"Aditya","middleName":"","lastName":"Aryan","suffix":""}],"badges":[],"createdAt":"2024-03-28 07:40:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4180384/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4180384/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54003154,"identity":"0bf0e96d-ee84-4419-a83e-ca5213e11b31","added_by":"auto","created_at":"2024-04-03 08:52:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":325883,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area map of Adayar River Basin\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4180384/v1/e3c41fc0a25943225ab00811.png"},{"id":54003155,"identity":"3343eba7-0d9a-401f-93a8-92dea76d59b7","added_by":"auto","created_at":"2024-04-03 08:52:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28398,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart followed in Data Collection\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4180384/v1/553821f76b860b4e81ad5bdf.png"},{"id":54003728,"identity":"c4b7eab4-a618-430b-97a1-9709909d45d1","added_by":"auto","created_at":"2024-04-03 09:00:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":135331,"visible":true,"origin":"","legend":"\u003cp\u003eDigital Elevation Model Data of Adayar River Basin\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4180384/v1/e1726f7433c5493497028dfa.png"},{"id":54003143,"identity":"7dedb9e2-a408-4513-99cc-6536b5cc9c01","added_by":"auto","created_at":"2024-04-03 08:52:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":547467,"visible":true,"origin":"","legend":"\u003cp\u003eElevation Map of Adayar River Basin\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4180384/v1/a47b563a2d005b4bb8c7ca4c.png"},{"id":54003727,"identity":"abc2501e-5853-48d3-929b-f76d486e7641","added_by":"auto","created_at":"2024-04-03 09:00:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":530414,"visible":true,"origin":"","legend":"\u003cp\u003eSlope Map of Adayar River Basin\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4180384/v1/7df99b8c6af27e787945b3a3.png"},{"id":54003162,"identity":"d883e263-b215-4c25-a6d3-c40fa78f0ba7","added_by":"auto","created_at":"2024-04-03 08:52:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":238312,"visible":true,"origin":"","legend":"\u003cp\u003eLithology Map of Adayar River Basin\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4180384/v1/6c8cb47e6a026d33c2528e2c.png"},{"id":54003144,"identity":"2f5e0d65-9a1c-4d6e-9945-507395925725","added_by":"auto","created_at":"2024-04-03 08:52:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":197779,"visible":true,"origin":"","legend":"\u003cp\u003eSoil Map of Adayar River Basin\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4180384/v1/d9bba91089eeb17587644df5.png"},{"id":54003159,"identity":"efdf0794-210c-4da9-823a-1361dbc54389","added_by":"auto","created_at":"2024-04-03 08:52:21","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":544541,"visible":true,"origin":"","legend":"\u003cp\u003eLand Use Land Cover Map of Adaya River Basin\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4180384/v1/5967030b97d6b217eaf3fba7.png"},{"id":54003161,"identity":"dfd6017a-4639-41ba-bc25-39622cd55b41","added_by":"auto","created_at":"2024-04-03 08:52:22","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":547366,"visible":true,"origin":"","legend":"\u003cp\u003eTopographical Wetness Index Map of Adayar River Basin\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4180384/v1/35cce8945327c4f51636ba18.png"},{"id":54003158,"identity":"cb016a59-f8eb-4665-a04b-8bf70b96c1bf","added_by":"auto","created_at":"2024-04-03 08:52:21","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":519052,"visible":true,"origin":"","legend":"\u003cp\u003eDistance to Stream Map of Adayar River Basin\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-4180384/v1/b3849c1ed561ca4b16d6b537.png"},{"id":54003153,"identity":"98dee847-15a8-4a76-9c88-ab51a2992e7d","added_by":"auto","created_at":"2024-04-03 08:52:20","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":403357,"visible":true,"origin":"","legend":"\u003cp\u003eDrainage Density Map of Adayar River Basin\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-4180384/v1/b25b934d560e71ebdd07ca60.png"},{"id":54003163,"identity":"3d441163-38b9-4374-a844-61a76762b1f5","added_by":"auto","created_at":"2024-04-03 08:52:22","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":219644,"visible":true,"origin":"","legend":"\u003cp\u003eRainfall Distribution Map of Adayar River Basin\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-4180384/v1/391f2c7c090f9cf6a3a0a804.png"},{"id":54003160,"identity":"68c03b6e-11e9-4a18-91cc-2314fe43f25c","added_by":"auto","created_at":"2024-04-03 08:52:21","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":481620,"visible":true,"origin":"","legend":"\u003cp\u003eFlood Susceptibility Map of Adayar River Basin\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-4180384/v1/5d114c8d90f11b44291d2a2b.png"},{"id":54003141,"identity":"237de8db-8237-4780-80e4-f3d677fbc6ce","added_by":"auto","created_at":"2024-04-03 08:52:18","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":504537,"visible":true,"origin":"","legend":"\u003cp\u003eLocations of the Vulnerable Areas at Adayar River Basin\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-4180384/v1/9c67dd5b8a2432527e868db0.png"},{"id":64411141,"identity":"9ef34798-4f59-493b-b457-d3dd7a4d4b51","added_by":"auto","created_at":"2024-09-12 20:33:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7192907,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4180384/v1/961fda3b-5229-4d13-ad7b-2be0e279ea19.pdf"}],"financialInterests":"","formattedTitle":"Flood Susceptibility Mapping to Identify the Vulnerable Areas in the Adayar River Basin at Chennai, Tamil Nadu","fulltext":[{"header":"1\tOverview","content":"\u003ch3\u003e1.1 Introduction\u003c/h3\u003e\n\u003cp\u003eFlooding denotes normally dry land\u0026apos;s temporary submergence from overflowing waters of rivers, lakes, or other water bodies. (WMO and UNDRR). Floods are also responsible for approximately 5,000 deaths per year globally (WHO) and are the leading cause of internal displacement globally, affecting millions of people each year (IDMC). Floods also cost the global economy an average of 104\u0026nbsp;billion dollars annually (World Bank) and can result in epidemics of cholera, typhoid fever, and hepatitis A (WHO).\u003c/p\u003e\n\u003cp\u003eIndia due to its unique geo-political and socio-economic conditions is vulnerable to floods (Annual Report NDMA GOI 2022\u0026ndash;2023). In India, there are the availability of almost 329 million hectors (mha) of geographical area, among this more than 40 mha is flood-prone areas, which results in 75 lakh hectares of affected land, and 1600 deaths on an average year at the cost of 1805 crore rupees (NDMA GOI). India\u0026apos;s complex flood situation renders flood management an arduous undertaking (Mohanty et al. \u003cspan\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eChennai is the capital of Tamil Nadu one of the important southern states of India. It consists of the Adayar River, Cooum River, Palar River, and Kosasthalaiyar River, among the following rivers Adayar River receives the maximum amount of rainfall throughout the year and is prone to flooding (Faiz Ahmed and Kranthi \u003cspan\u003e2018\u003c/span\u003e; NRSC/ISRO \u003cspan\u003e2015\u003c/span\u003e; Sharif et al. \u003cspan\u003e2020\u003c/span\u003e). The Basin situated at Chennai is a part of the Tamil Nadu Coastal region and is in a moderately vulnerable area (Priya Rajan et al. \u003cspan\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn the past years, there has been a major involvement of Multi-Criteria Decision Analysis (MCDA) (Mateo \u003cspan\u003e2012\u003c/span\u003e) along with GIS for mapping the vulnerability of any region against any natural disaster (Beheshtifar \u003cspan\u003e2023\u003c/span\u003e; Sahnoun et al. \u003cspan\u003e2012\u003c/span\u003e). The several methods that are available for MCDA include Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS), VlseKriteri-jumska Optimizacija I Kompromisno Resenje (VIKOR) in Serbian meaning multi-criteria optimization and compromise solution, Complex Proportional Assessment (COPRAS), Multi-objective Optimization by Ratio Analysis (MULTIMOORA), Preference Ranking Organization Method for the Enrichment of Evaluations and Graphical Analysis for Interactive Aid (PROMETHEE-GAIA), Multi-Attribute Utility Theory (MAUT), and AHP method. Each method has its special significance if the comparison is required AHP, MULTIMOORA, and MAUT are preferred; to find the best alternative from the provided options AHP, TOPSIS, VIKOR, and COPRAS are suitable and PROMETHEE-GAIA is based on pairwise comparison and confirmatory assessment for the desired purpose (Zlaugotne et al. \u003cspan\u003e2020\u003c/span\u003e). Hence as per the requirement, this paper uses the AHP method because of its greater reliability and easy-to-use characteristics.\u003c/p\u003e\n\u003cdiv id=\"Sec2\"\u003e\n \u003ch2\u003e1.2 Scope of the Research\u003c/h2\u003e\u003cspan\u003e\u003cstrong\u003e1.2.1 Understanding Vulnerability.\u003c/strong\u003e The paper can delve into the factors that contribute to vulnerability to floods in the Adayar River Basin, such as topography, land use, urbanization, infrastructure, and climate change impacts. It can explore how these factors interact and contribute to the susceptibility of different areas to flooding.\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e\u003cstrong\u003e1.2.2 Methodology Development.\u003c/strong\u003e It can detail the methodology used for developing the flood susceptibility map, which may include data collection, remote sensing techniques, GIS (Geographic Information System) analysis, hydrological modeling, and statistical analysis. This section can contribute to the scientific literature by proposing innovative or improved methodologies for assessing flood susceptibility.\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e\u003cstrong\u003e1.2.3 Mapping Techniques.\u003c/strong\u003e It can discuss the specific mapping techniques employed, such as susceptibility index mapping, multi-criteria decision analysis, or machine learning algorithms. It can evaluate the effectiveness of different techniques in accurately identifying vulnerable areas and compare their results.\u003cbr\u003e\u003c/span\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e1.3 Methodology\u003c/h2\u003e\n \u003cp\u003eThe project\u0026apos;s main outcome is to develop a Flood Susceptible Map (FSM). FSM is a process used in hydrology and geography to assess the likelihood of an area being affected by flooding. It involves analyzing various factors such as topography, land use, soil type, rainfall patterns, or infrastructure to pinpoint regions that have a higher susceptibility to inundation. By integrating these factors into GIS, experts can create maps that highlight areas with different levels of susceptibility to flooding, ranging from low to high risk. It is valuable for disaster management, urban planning, and risk mitigation efforts. So, it is widely advocated to incorporate more than five factors to prevent biased weighting, which could otherwise lead to overemphasis on certain factors, potentially skewing the overall assessment (Kaya and Derin \u003cspan\u003e2023\u003c/span\u003e). For the following paper, nine criteria have been selected, those are:\u003c/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e1.3.1 Elevation\u003c/strong\u003e refers to the height of a point or surface above a reference point, usually sea level (Selvam and Antony Jebamalai \u003cspan\u003e2023\u003c/span\u003e). It is commonly measured in meters or feet and is a critical factor in determining topographical features and landforms. Choosing elevation over height for the paper offers a more consistent and standardized measure referenced to sea level, facilitating accurate assessment of terrain features and water flow patterns\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e1.3.2 Slope\u003c/strong\u003e represents the steepness or incline of a surface, usually expressed as a percentage or angle. It indicates how much a surface rises or falls over a certain horizontal distance and is essential in understanding drainage patterns and surface runoff (Dung et al. \u003cspan\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e1.3.3 Lithology\u003c/strong\u003e refers to the physical characteristics and composition of rocks or sediments within the Earth\u0026apos;s crust (Vojtek and Vojtekov\u0026aacute; \u003cspan\u003e2019\u003c/span\u003e). It encompasses properties such as rock type, mineral composition, texture, and structure and is essential for understanding groundwater Lithology flow, erosion, and geological processes.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e1.3.4 Soil\u003c/strong\u003e a natural blend of minerals, organic matter, water, air, and living organisms that make up the Earth\u0026apos;s surface layer, serves as a growth medium for plants. It regulates water flow, and its water retention capacity plays a crucial role in identifying areas susceptible to flooding. (Chifflard et al. \u003cspan\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e1.3.5 Land Use Land Cover\u003c/strong\u003e refers to the human activities and purposes for which land is utilized, such as residential, commercial, agricultural, or industrial purposes. Land cover, on the other hand, refers to the physical and biological coverage of the Earth\u0026apos;s surface, including vegetation, water bodies, built-up areas, and bare soil (Brody et al. \u003cspan\u003e2014\u003c/span\u003e; Kassaye et al. \u003cspan\u003e2024\u003c/span\u003e; Sundaram et al. \u003cspan\u003e2021\u003c/span\u003e). Both land use and land cover are essential for understanding landscape changes, environmental impacts, and socio-economic dynamics.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e1.3.6 Topographical Wetness Index\u003c/strong\u003e soil wetness spatially, computing upslope area-slope gradient ratio, pinpointing flood-prone, waterlogged zones.(Selvam and Antony Jebamalai \u003cspan\u003e2023\u003c/span\u003e). It is calculated based on the ratio of the upslope contributing area to the slope gradient and is useful for identifying areas prone to waterlogging, saturation, and potential flooding.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e1.3.7 Distance to Stream\u003c/strong\u003e refers to the proximity of a location to a watercourse, such as a river, stream, or creek (Mehravar et al. \u003cspan\u003e2023\u003c/span\u003e). It is measured in linear distance and is an essential factor in flood risk assessment, as areas closer to streams are more susceptible to flooding during high-flow events.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e1.3.8 Drainage Density\u003c/strong\u003e is a measure of the total length of stream channels within a given area (Rimba et al. \u003cspan\u003e2017\u003c/span\u003e). It quantifies the degree of branching and interconnectedness of the stream network and is indicative of the efficiency of surface water drainage. Higher drainage density implies better natural drainage and reduced flood risk in the area.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e1.3.9 Annual Rainfall Distribution\u003c/strong\u003e refers to the spatial and temporal patterns of precipitation over a specific geographic area during a given year (WMO). It includes information on the amount, intensity, frequency, and duration of rainfall events, which influence hydrological processes, water availability, and flood risk (Breinl et al. \u003cspan\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003c/span\u003e\n\u003c/div\u003e"},{"header":"2 Planning and Methods","content":"\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.1 Prelude\u003c/h2\u003e\n \u003cp\u003eInundations of typically dry areas by overflowing water characterize the natural disasters known as floods. They can arise from diverse causes like excessive precipitation, rapid snow thawing, coastal storm surges, or the failure of dams (Merz et al. \u003cspan\u003e2021\u003c/span\u003e). Floods can occur gradually or suddenly, causing widespread damage to infrastructure, homes (Nadal et al. \u003cspan\u003e2010\u003c/span\u003e), agriculture (Kumar et al. \u003cspan\u003e2022\u003c/span\u003e), and posing risks to human life (Jonkman \u003cspan\u003e2014\u003c/span\u003e). They can have devastating impacts on communities, ecosystems, and economies (Parida et al. \u003cspan\u003e2022\u003c/span\u003e), making flood preparedness and management crucial for minimizing their effects.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.2 Study Area\u003c/h2\u003e\n \u003cp\u003eThe Adyar River originates as a stream from the Malaipattu tank situated near Manimangalam village in the Sriperumbudur taluk, which is approximately 15 km west of Tambaram in South Chennai. It gains significant water flow from the point where the release from a lake joins the river at Thiruneermalai. The river traverses through the districts of Kancheepuram, Tiruvallur, and Chennai over a distance of about 42.5 km before emptying into the Bay of Bengal at Adyar, Chennai. The study area under consideration lies geographically between the northern latitudes of 10\u0026deg;15\u0026apos; to 10\u0026deg;25\u0026apos; and eastern longitudes of 79\u0026deg;20\u0026apos; and 79\u0026deg;55\u0026apos;, spanning an area of 750 square kilometers. (Arivazhagan et al. \u003cspan\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe depth of the Adyar River varies, with an approximate depth of 0.75 meters in its upper reaches and 0.5 meters in its lower reaches. The river\u0026apos;s catchment area spans 530 square kilometers. The width of its bed ranges from 10.5 meters to 200 meters. Within the Chennai Metropolitan area, the river flows for a distance of 24 kilometers, with around 15 kilometers of its course lying within the Chennai district before it drains into the sea. Annually, the Adyar River discharges between 190 to 940\u0026nbsp;million cubic meters of water into the Bay of Bengal. This discharge is seasonal, with the flow being 7 to 33 times higher than the annual average during the Northeast monsoon season, which occurs between September and December. The study area map is in Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.3 Data Collection\u003c/h2\u003e\n \u003cp\u003eThe collection of data follows the procedure as shown in Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe collection of the data includes the following sources HydroSHEDS Data (\u003cspan\u003e\u003cspan\u003ehttps://www.hydrosheds.org/\u003c/span\u003e\u003c/span\u003e), FAO Data (\u003cspan\u003e\u003cspan\u003ehttps://www.fao.org/soils-portal/en/\u003c/span\u003e\u003c/span\u003e), USGS Data (\u003cspan\u003e\u003cspan\u003ehttps://www.usgs.gov/\u003c/span\u003e\u003c/span\u003e), BHUKOSH Data (\u003cspan\u003e\u003cspan\u003ehttps://bhukosh.gsi.gov.in/Bhukosh/Public\u003c/span\u003e\u003c/span\u003e), SOI Data (\u003cspan\u003e\u003cspan\u003ehttps://onlinemaps.surveyofindia.gov.in/Home.aspx\u003c/span\u003e\u003c/span\u003e), (\u003cspan\u003e\u003cspan\u003ehttps://www.climateurope.eu/datasets-climatic-research-unit-cru/\u003c/span\u003e\u003c/span\u003e) CRU Data, and (\u003cspan\u003e\u003cspan\u003ehttps://sedac.ciesin.columbia.edu/data/set/india-india-village-level-geospatial-socio-econ-1991-2001\u003c/span\u003e\u003c/span\u003e) SEDAC.\u003c/p\u003e\n \u003cp\u003eFurthermore, the Land use land Cover map is developed from the 2023 data, and the Annual Rainfall Distribution Map was developed on the data available from the year 2012 to 2022 that is past consecutive 10 years data.\u003c/p\u003e\n \u003cp\u003eDigital Elevation Model (DEM) data as shown in Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e plays a crucial role in assessing flood susceptibility in any urban area (Huang et al. \u003cspan\u003e2023\u003c/span\u003e) by providing detailed information about the topography of an area, some of its uses are:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003eTerrain Analysis\u003c/em\u003e: By analyzing the slope and aspect of the terrain derived from DEMs, experts can identify areas prone to flooding, such as low-lying areas or regions with steep slopes where water is likely to accumulate.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003eFloodplain Mapping\u003c/em\u003e: DEMs are used to demarcate floodplains - areas flanking rivers, and streams susceptible to inundation during high flow episodes.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cem\u003eRisk Assessment and Planning\u003c/em\u003e: DEM-derived flood susceptibility maps provide valuable information for land use planning, emergency preparedness, and risk mitigation.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.4 Criteria Explanation\u003c/h2\u003e\n \u003cp\u003eThe above-stated nine factors have been generated as a raster grid of 30 m \u0026times; 30 m in ArcMap 9.2 for the application of the AHP method and the significance of each of the developed criteria maps based on their segregated classes has been elaborated below.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.4.1 Development of Elevation Map\u003c/strong\u003e. Low-lying areas, such as floodplains, are more susceptible to flooding because water naturally accumulates in these areas. Understanding elevation variations helps identify areas prone to inundation during heavy rainfall or river overflow. In Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e, the elevation map of the Adayar River Basin is divided into five classes based on the percentage rise which ranges from 0 to 25 is the first class with the least elevation, and a gradual increase in elevation followed by 25.1 to 72 being the second class, 72.1 to 115 being third class, 116 to 154 being fourth class, 155 to 223 finally being the fifth class with the highest elevation value.\u003c/p\u003e\u003cbr\u003e\n \u003cp\u003e\u003cstrong\u003e2.4.2 Development of Slope Map\u003c/strong\u003e Steep slopes accelerate runoff, increasing the likelihood of flash floods. Conversely, flat areas might retain water longer, leading to prolonged inundation. By analyzing slope gradients, predictions can be made of how water will flow across the landscape during rainfall events. In Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e, the elevation map of the Adayar River Basin is divided into five classes which range from 0 to 1.61 m the first class with the least slope, and the gradual increase of the slope followed by 1.62 to 4.51 m is the second class, 4.52 to 12.6 m being third class, 12.7 to 28.4 m being fourth class, 28.5 to 82.2 m finally being fifth class with the highest slope value.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e2.4.3 Development of Lithology Map\u003c/strong\u003e Different rock types have varying permeability rates, which can lead to increased or decreased surface runoff. Understanding the composition of the terrain helps assess how water will interact with the landscape. In Fig. \u003cspan\u003e6\u003c/span\u003e the lithology map of the Adayar River Basin the region consists of four different classes of rocks those are Charnockite Gneiss rock (30%), which is a type of crystalline rock formation that tends to have low porosity and permeability, meaning it doesn\u0026apos;t hold or transmit water well; Upper Gondwana (27%), these rocks are often composed of sandstones and shales, which can have moderate water-holding capacity compared to crystalline rocks but may not be as effective as fluvial sediments; Lower Gondwana (10%), Similar to Upper Gondwana, these rocks are sedimentary but they may have slightly higher water-holding capacity due to different depositional environments; Fluvial (33%), these rocks deposited by rivers, tend to have the highest water-holding capacity amongst all types and often consist of well-sorted and well-rounded particles with good porosity and permeability, making them efficient at storing and transmitting water. Thus, based on the characteristics of the prevailing rocks the region consisting of Fluvial rocks is most susceptible to flood flowed by Upper Gondwana, Lower Gondwana, and least in the Charnockite Gneiss rock region.\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003e\u003cstrong\u003e2.4.4 Development of Soil Map\u003c/strong\u003e Soil characteristics influence infiltration rates and water retention capacity. By examining soil properties, anticipation can be performed on how quickly water will move through the ground. In Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e the soil map of the Adayar River Basin the region consists of three different classes of soils those are, Dystric Regosols (6%), soils are often characterized by low organic matter content and coarse texture, which typically results in lower water holding capacity compared to soils with more organic matter and finer texture; Orthoc Luvisols (92%), these soils have some characteristics that improve water retention compared to Dystric Regosols, generally have less clay content and lower organic matter content than Cromic Luvisols, resulting in a lower overall water holding capacity; Cromic Luvisols (2%), these soils tend to have a higher clay content and more organic matter, which both contribute to their greater water holding capacity compared to the other two soil types mentioned. The presence of clay particles allows them to retain more water, and higher organic matter content enhances soil structure, improving water retention further. Thus, based on the characteristics of the prevailing soils the region consisting of Dystric Regosols is most susceptible to flood flowed by Orthoc Luvisols and least in Chromic Luvisols region.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.4.5 Development of Land Use Land Cover Map\u003c/strong\u003e Urbanization and deforestation reduce natural infiltration and increase surface runoff, elevating flood risk. Conversely, areas with vegetation cover can help absorb and retain water, mitigating flood impacts. Understanding land use and land cover changes enables us to anticipate shifts in flood susceptibility. In Fig.\u0026nbsp;\u003cspan\u003e8\u003c/span\u003e the land uses a land cover map of the Adayar River Basin the region consists of five different classes those are Water Bodies (4%), High-Density Settlement (34%), Low-Density Settlement (33%), Vegetation (27%) and Barren Land (2%). Naturally, water bodies are already flooded areas, so they are the most susceptible to flooding; Vegetated areas can absorb some water and reduce surface runoff, making them less susceptible to flooding compared to built-up areas; density settlements may have some permeable surfaces and green spaces, which can help mitigate flooding to some extent; High density settlements often have large areas of impermeable surfaces like concrete and asphalt, which increase surface runoff and make them more susceptible to flooding; Barren land typically lacks vegetation and has little to no soil cover to absorb or slow down water, so it can be highly susceptible to flooding.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.4.6 Development of Topographical Wetness Index Map\u003c/strong\u003e This index integrates various topographic factors to quantify the propensity of a location to accumulate water. This index is developed with the help of the \u003cstrong\u003ef\u003c/strong\u003eormula (Beven and Kirkby \u003cspan\u003e1979\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eTWI\u0026thinsp;=\u0026thinsp;ln (\u0026alpha; / tan \u0026beta;) (1)\u003c/p\u003e\n \u003cp\u003ewhere \u0026alpha; is the local upslope area draining through a certain point per unit concentration length and tan \u0026beta; is the local slope. In Fig.\u0026nbsp;\u003cspan\u003e9\u003c/span\u003e the topographical wetness index map of the Adayar River Basin the region consists of five different levels ranging from \u0026minus;\u0026thinsp;9.08 to \u0026minus;\u0026thinsp;4.33 being first class, \u0026minus;\u0026thinsp;4.32 to -2.25 being second class, \u0026minus;\u0026thinsp;2.24 to \u0026minus;\u0026thinsp;0.168 being third class, 0.169 to 3.42 being fourth class, 3.43 to 12.2 being fifth class. The ranges can be categorized respectively as Extremely Dry areas with very low wetness, likely corresponding to high points or steep slopes where water runoff is rapid, and little water accumulates; Relatively Dry areas with low wetness include moderately sloped terrain where water runoff occurs more slowly compared to the previous category, Moderately Wet areas with some potential for water accumulation and could encompass gently sloping terrain or areas with moderate upslope contributing areas; Higher Wetness area with increased potential for water accumulation, likely include flat or low-lying areas where water tends to pool and saturate the soil; Very Wet areas with high potential for water accumulation correspond to floodplains or other prolonged inundation areas.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.4.7 Development of Distance to Stream Map.\u003c/strong\u003e Proximity to rivers and streams influences flood risk, as areas adjacent to water bodies are more susceptible to inundation during periods of high flow. Mapping distances to waterways helps identify areas at heightened risk of flooding. In the Fig.\u0026nbsp;\u003cspan\u003e10\u003c/span\u003e distance to stream map of the Adayar River Basin the region consists of five different classes consisting of 500 m being first class with the highest prone to flooding, and gradually decreasing by 750 m being second class, 1000 m being third class, 1250 m being fourth class, and finally 2500 m being the fifth class with least flooding.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u003cstrong\u003e2.4.8 Development of Drainage Density Map. In\u003c/strong\u003e Fig.\u0026nbsp;\u003cspan\u003e11\u003c/span\u003e elaborates on the drainage density map of the Adayar River Basin. The drainage density of 336.422 m\u003csup\u003e3\u003c/sup\u003e/km suggests that the area has a dense network of streams and rivers, hence efficient water drainage and less susceptible to flooding. A moderately high drainage density of 252.317 m\u003csup\u003e3\u003c/sup\u003e/km indicates that the area still has a considerable number of streams and rivers, but perhaps not as dense as the previous category. The moderate drainage density of 168.211 m\u003csup\u003e3\u003c/sup\u003e/km suggests a somewhat less dense network of streams and rivers. A moderately low drainage density of 84.1055 m\u003csup\u003e3\u003c/sup\u003e/km indicates slower drainage, leading to increased flood risk during heavy rainfall. A drainage density of 0 m\u003csup\u003e3\u003c/sup\u003e/km indicates that there are no streams or rivers present in the area increasing flood susceptibility.\u003cbr\u003e\u003c/span\u003e \u003cspan\u003e\u003cstrong\u003e2.4.9 Development of Annual Rainfall Distribution Map\u003c/strong\u003e Intense rainfall over short periods can overwhelm drainage systems, leading to localized flooding. By analyzing historical rainfall patterns and future climate projections, we can assess the frequency and intensity of rainfall events, aiding in flood risk assessment. In the formation of this map, the Inverse Distance Weighted average (IDW) method has been used as it has the lowest error and maximum model efficiency (Knight et al.). The IDW method assumes that values closer to the location being estimated have more influence than those farther away. The influence of each sampled point on the estimation at an unsampled location is weighted based on its distance from that location. The weights assigned to each sampled point are inversely proportional to their distance from the estimation location. This means that closer points have higher weights, while points farther away have lower weights. The formula for calculating the weight of each sample point is given by\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003ew\u003csub\u003ei\u003c/sub\u003e = 1/d\u003csub\u003ei\u003c/sub\u003e\u003csup\u003ep\u003c/sup\u003e (2)\u003c/p\u003e\n \u003cp\u003eWhere w\u003csub\u003ei\u003c/sub\u003e is the weight, d\u003csub\u003ei\u003c/sub\u003e is the distance between the estimation location, i is the sample point, and p is a power parameter that controls the rate at which the weights decrease the distance. Once the weights are calculated for each sampled point, the estimated value at the unsampled location is obtained by averaging the measured values at the sampled locations, weighted by their respective weights. The formula for the estimated value (Z) at a given location is typically\u003c/p\u003e\n \u003cp\u003eZ = \u003cspan\u003e\u003cspan\u003e\\(\\frac{\\sum _{i=1}^{n}{w}_{i}{Z}_{i}}{\\sum _{i=1}^{n}{w}_{i}}\\)\u003c/span\u003e\u003c/span\u003e (3)\u003c/p\u003e\n \u003cp\u003eWhere Z\u003csub\u003ei\u003c/sub\u003e is the measured rainfall value at sampled point I, n is the total number of sampled points. Finally in ArcGIS, the study area is typically divided into a grid of cells, and the IDW method is applied to estimate rainfall values at each cell center. Finally, the result in a raster layer representing the interpolated annual rainfall distribution across the study area.\u003c/p\u003e\n \u003cp\u003eIn \u003cstrong\u003eFig.\u0026nbsp;12\u003c/strong\u003e the rainfall distribution map of the Adayar River Basin the region has been classified into five different sectors ranging from 3.994 to 3.999 mm/year as the first sector, 4 to 4.004 mm/year as the second sector, 4.005 to 4.01 mm/year as the third sector, 4.011 to 4.015 mm/year as a fourth sector, 4.016 to 4.02 mm/year as a fifth sector. Thus, the regions experiencing maximum rainfall are prone to flood.\u003c/p\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;12.\u003c/strong\u003e Rainfall Distribution Map of Adayar River Basin\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Analysis and Result","content":"\u003cp\u003eFor assessing flood-prone areas in the Adayar River Basin, the AHP process was utilized (Ghosh and Kar \u003cspan\u003e2018\u003c/span\u003e; Mitra et al. \u003cspan\u003e2022\u003c/span\u003e; Princecharles Chukwuemeka Anyadiegwu et al. \u003cspan\u003e2021\u003c/span\u003e). AHP, a decision-making framework by Thomas L. Saaty in 1988, evaluates and prioritizes multiple criteria through pairwise comparisons, aiding complex decisions systematically. AHP analysis procedure involves: (Ali et al. \u003cspan\u003e2019\u003c/span\u003e; Bagyaraj et al. \u003cspan\u003e2023\u003c/span\u003e; Chakraborty and Mukhopadhyay \u003cspan\u003e2019\u003c/span\u003e; Mukherjee and Deb \u003cspan\u003e2023\u003c/span\u003e; Selvam and Antony Jebamalai \u003cspan\u003e2023\u003c/span\u003e):\u003c/p\u003e\n\u003cp\u003eFirst of all, the formation of a pairwise comparison matrix based on the scale of relative importance as per Satty tabulation (1980), is shown in Table \u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFundamental Scale of pair-wise comparison matrix (Satty \u003cspan\u003e1980\u003c/span\u003e)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIntensities of importance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEqual Importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow importance of one over another\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStrong or essential importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstablished importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsolute or high importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2, 4, 6, 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermediate values between the two adjacent importance or judgments\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReciprocals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReciprocals if criteria \u003cem\u003ei\u003c/em\u003e has one of the above numbers designated to it when compared with criteria \u003cem\u003ej\u003c/em\u003e, then \u003cem\u003ej\u003c/em\u003e has the reciprocal value when compared with \u003cem\u003ei\u003c/em\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThen transferring to the normalized pairwise matrix by dividing all the elements of the column by the sum of all the elements of the column and hence, calculating the consistency matrix to find the Consistency Index (CI) with the formula\u003c/p\u003e\n\u003cp\u003eCI = \u003cspan\u003e\u003cspan\u003e\\(\\frac{{\\lambda }\\text{max}- n}{n-1}\\)\u003c/span\u003e\u003c/span\u003e (4)\u003c/p\u003e\u003cp\u003eWhere \u0026lambda;\u003csub\u003emax\u003c/sub\u003e refers to the maximum eigenvalue of the pairwise comparison matrix and n refers to the number of compared elements. After that Random Index is calculated from Table \u003cspan\u003e2\u003c/span\u003eand finally, the Consistency Ratio (CR) is calculated as the ratio of CI to RI. If the value of CR is less than 0.1 the obtained criteria weights are correct.\u003c/p\u003e\u003cdiv align=\"left\"\u003e\u003cbr\u003e\u003c/div\u003e\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv\u003eTable 2\u003c/div\u003e\u003cdiv\u003e\u003cp\u003eRandom Inconsistency Indices for n\u0026thinsp;=\u0026thinsp;10 (Satty \u003cspan\u003e1980\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eRI\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on the selected criteria, with the help of Table \u003cspan\u003e1\u003c/span\u003e and Table \u003cspan\u003e2\u003c/span\u003e, the consistency matrix as shown in Table\u0026nbsp;3 is developed.\u003c/p\u003e\u003cp\u003eAfter the preparation of the consistency matrix since the value of CR came to be less than 1 the assumed criteria weighted correctly and based on this Percentage Criteria Weight the FSM is prepared as shown in Fig. \u003cspan\u003e13\u003c/span\u003e and the Susceptibility Class Ratings and the Percentage Criteria Weights are mentioned in Table \u003cspan\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Consistency Matrix for Analytical Hierarchical Process\u003c/p\u003e\u003cdiv align=\"center\"\u003e\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd width=\"13.449023861171366%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCriteri Weights\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"6.724511930585683%\" style=\"width: 10.4265%;\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"6.724511930585683%\" style=\"width: 6.0032%;\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"6.941431670281996%\" style=\"width: 10.5845%;\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"6.724511930585683%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"6.724511930585683%\" style=\"width: 7.109%;\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"6.724511930585683%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"9.11062906724512%\" style=\"width: 9.4787%;\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.195227765726681%\" style=\"width: 10.1106%;\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"9.761388286334057%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.195227765726681%\" rowspan=\"3\" style=\"width: 8.0569%;\"\u003e\u003cp\u003eCR = 0.041 \u0026lt; 0.100\u003c/p\u003e\u003cp\u003e(Hence correct)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"16.18798955613577%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 10.4265%;\"\u003e\u003cp\u003e9.203\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 6.0032%;\"\u003e\u003cp\u003e9.109\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.355091383812011%\" style=\"width: 10.5845%;\"\u003e\u003cp\u003e9.108\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e9.243\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 7.109%;\"\u003e\u003cp\u003e9.420\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e9.607\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.966057441253264%\" style=\"width: 9.4787%;\"\u003e\u003cp\u003e9.791\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"12.27154046997389%\" style=\"width: 10.1106%;\"\u003e\u003cp\u003e9.894\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"11.74934725848564%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e9.855\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"16.18798955613577%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e\u003cstrong\u003eCriteria Weights\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 10.4265%;\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 6.0032%;\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.355091383812011%\" style=\"width: 10.5845%;\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 7.109%;\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.966057441253264%\" style=\"width: 9.4787%;\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"12.27154046997389%\" style=\"width: 10.1106%;\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"11.74934725848564%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"14.418604651162791%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e\u003cstrong\u003eWeighted Sum Value\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 10.4265%;\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 6.0032%;\"\u003e\u003cp\u003e0.237\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.441860465116279%\" style=\"width: 10.5845%;\"\u003e\u003cp\u003e0.337\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.490\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 7.109%;\"\u003e\u003cp\u003e0.716\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e1.105\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"9.767441860465116%\" style=\"width: 9.4787%;\"\u003e\u003cp\u003e1.508\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.930232558139535%\" style=\"width: 10.1106%;\"\u003e\u003cp\u003e2.147\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.465116279069768%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e2.996\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.930232558139535%\" rowspan=\"2\" style=\"width: 8.0569%;\"\u003e\u003cp\u003eRI = 1.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"16.18798955613577%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e\u003cstrong\u003eRainfall Distribution\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 10.4265%;\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 6.0032%;\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.355091383812011%\" style=\"width: 10.5845%;\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 7.109%;\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.966057441253264%\" style=\"width: 9.4787%;\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"12.27154046997389%\" style=\"width: 10.1106%;\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"11.74934725848564%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"14.418604651162791%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e\u003cstrong\u003eDrainage Density\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 10.4265%;\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 6.0032%;\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.441860465116279%\" style=\"width: 10.5845%;\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 7.109%;\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"9.767441860465116%\" style=\"width: 9.4787%;\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.930232558139535%\" style=\"width: 10.1106%;\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.465116279069768%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e0.434\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.930232558139535%\" rowspan=\"2\" style=\"width: 8.0569%;\"\u003e\u003cp\u003eCI = 0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"16.18798955613577%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e\u003cstrong\u003eDistance to Stream\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 10.4265%;\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 6.0032%;\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.355091383812011%\" style=\"width: 10.5845%;\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 7.109%;\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.966057441253264%\" style=\"width: 9.4787%;\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"12.27154046997389%\" style=\"width: 10.1106%;\"\u003e\u003cp\u003e0.308\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"11.74934725848564%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e0.462\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"14.418604651162791%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e\u003cstrong\u003eTWI\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 10.4265%;\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 6.0032%;\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.441860465116279%\" style=\"width: 10.5845%;\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 7.109%;\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"9.767441860465116%\" style=\"width: 9.4787%;\"\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.930232558139535%\" style=\"width: 10.1106%;\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.465116279069768%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e0.460\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.930232558139535%\" rowspan=\"3\" style=\"width: 8.0569%;\"\u003e\u003cp\u003e\u0026lambda;\u003csub\u003emax\u0026nbsp;\u003c/sub\u003e= 9.470\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"16.18798955613577%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e\u003cstrong\u003eLULC\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 10.4265%;\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 6.0032%;\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.355091383812011%\" style=\"width: 10.5845%;\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 7.109%;\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.966057441253264%\" style=\"width: 9.4787%;\"\u003e\u003cp\u003e0.228\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"12.27154046997389%\" style=\"width: 10.1106%;\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"11.74934725848564%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e0.380\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"16.18798955613577%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e\u003cstrong\u003eSoil\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 10.4265%;\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 6.0032%;\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.355091383812011%\" style=\"width: 10.5845%;\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 7.109%;\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.966057441253264%\" style=\"width: 9.4787%;\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"12.27154046997389%\" style=\"width: 10.1106%;\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"11.74934725848564%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e0.318\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"14.418604651162791%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e\u003cstrong\u003eLithology\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 10.4265%;\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 6.0032%;\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.441860465116279%\" style=\"width: 10.5845%;\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 7.109%;\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"7.209302325581396%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"9.767441860465116%\" style=\"width: 9.4787%;\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.930232558139535%\" style=\"width: 10.1106%;\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.465116279069768%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e0.259\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.930232558139535%\" rowspan=\"4\" style=\"width: 8.0569%;\"\u003e\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"16.18798955613577%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 10.4265%;\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 6.0032%;\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.355091383812011%\" style=\"width: 10.5845%;\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 7.109%;\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.966057441253264%\" style=\"width: 9.4787%;\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"12.27154046997389%\" style=\"width: 10.1106%;\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"11.74934725848564%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"16.18798955613577%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e\u003cstrong\u003eElevation\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 10.4265%;\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 6.0032%;\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.355091383812011%\" style=\"width: 10.5845%;\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 7.109%;\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.966057441253264%\" style=\"width: 9.4787%;\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"12.27154046997389%\" style=\"width: 10.1106%;\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"11.74934725848564%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"16.18798955613577%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e\u003cstrong\u003eFactors\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 10.4265%;\"\u003e\u003cp\u003e\u003cstrong\u003eElevation\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 6.0032%;\"\u003e\u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.355091383812011%\" style=\"width: 10.5845%;\"\u003e\u003cp\u003e\u003cstrong\u003eLithology\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e\u003cstrong\u003eSoil\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 7.109%;\"\u003e\u003cp\u003e\u003cstrong\u003eLULC\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"8.093994778067884%\" style=\"width: 5.8452%;\"\u003e\u003cp\u003e\u003cstrong\u003eTWI\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"10.966057441253264%\" style=\"width: 9.4787%;\"\u003e\u003cp\u003e\u003cstrong\u003eDistance to Stream\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"12.27154046997389%\" style=\"width: 10.1106%;\"\u003e\u003cp\u003e\u003cstrong\u003eDrainage Density\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"11.74934725848564%\" style=\"width: 13.2701%;\"\u003e\u003cp\u003e\u003cstrong\u003eRainfall Distribution\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Parameter Rank and Percentage Criteria Weights\u003c/p\u003e\u003cdiv\u003e\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"490\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd width=\"21.224489795918366%\"\u003e\u003cp\u003e\u003cstrong\u003eFlood Causative Criteria\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"13.46938775510204%\"\u003e\u003cp\u003e\u003cstrong\u003eUnit\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"26.122448979591837%\"\u003e\u003cp\u003e\u003cstrong\u003eClass\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"20.20408163265306%\"\u003e\u003cp\u003e\u003cstrong\u003eSusceptibility Class Ratings\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"18.979591836734695%\"\u003e\u003cp\u003e\u003cstrong\u003e% Criteria Weights\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"21.224489795918366%\" rowspan=\"5\" valign=\"top\"\u003e\u003cp\u003e\u003cstrong\u003eElevation\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"13.46938775510204%\" rowspan=\"5\" valign=\"top\"\u003e\u003cp\u003em\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"26.122448979591837%\"\u003e\u003cp\u003e0 to 25\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"20.20408163265306%\" valign=\"top\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"18.979591836734695%\" rowspan=\"5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e25.1 to 72\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e72.1 to 115\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e116 to 154\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e155 to 223\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"21.224489795918366%\" rowspan=\"5\" valign=\"top\"\u003e\u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"13.46938775510204%\" rowspan=\"5\" valign=\"top\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"26.122448979591837%\"\u003e\u003cp\u003e0 to 1.61\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"20.20408163265306%\" valign=\"top\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"18.979591836734695%\" rowspan=\"5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e1.62 to 4.51\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e4.52 to 12.6\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e12.7 to 28.4\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e28.5 to 82.2\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"21.224489795918366%\" rowspan=\"4\" valign=\"top\"\u003e\u003cp\u003e\u003cstrong\u003eLithology\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"13.46938775510204%\" rowspan=\"4\" valign=\"top\"\u003e\u003cp\u003elevel\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"26.122448979591837%\"\u003e\u003cp\u003eFluvial\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"20.20408163265306%\" valign=\"top\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"18.979591836734695%\" rowspan=\"4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003eUpper Gondwana\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003eLower Gondwana\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003eCharmockite\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"21.224489795918366%\" rowspan=\"3\" valign=\"top\"\u003e\u003cp\u003e\u003cstrong\u003eSoil\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"13.46938775510204%\" rowspan=\"3\" valign=\"top\"\u003e\u003cp\u003elevel\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"26.122448979591837%\"\u003e\u003cp\u003eChromic Luvisols\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"20.20408163265306%\" valign=\"top\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"18.979591836734695%\" rowspan=\"3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003eOrthic Luvisols\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003eDystric Regosols\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"21.224489795918366%\" rowspan=\"5\" valign=\"top\"\u003e\u003cp\u003e\u003cstrong\u003eLULC\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"13.46938775510204%\" rowspan=\"5\" valign=\"top\"\u003e\u003cp\u003elevel\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"26.122448979591837%\"\u003e\u003cp\u003eBarren Land\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"20.20408163265306%\" valign=\"top\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"18.979591836734695%\" rowspan=\"5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003eVegetation\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003eWater Bodies\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003eLow Density Pop.\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003eHigh Density Pop.\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"21.224489795918366%\" rowspan=\"5\" valign=\"top\"\u003e\u003cp\u003e\u003cstrong\u003eTWI\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"13.46938775510204%\" rowspan=\"5\" valign=\"top\"\u003e\u003cp\u003elevel\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"26.122448979591837%\"\u003e\u003cp\u003e- 9.08 to - 4.33\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"20.20408163265306%\" valign=\"top\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"18.979591836734695%\" rowspan=\"5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e- 4.32 to - 2.25\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e- 2.24 to - 0.168\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e0.169 to 3.42\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e3.43 to 12.2\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"21.224489795918366%\" rowspan=\"5\" valign=\"top\"\u003e\u003cp\u003e\u003cstrong\u003eDistance to Stream\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"13.46938775510204%\" rowspan=\"5\" valign=\"top\"\u003e\u003cp\u003em\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"26.122448979591837%\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"20.20408163265306%\" valign=\"top\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"18.979591836734695%\" rowspan=\"5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e750\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e2500\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"21.224489795918366%\" rowspan=\"4\" valign=\"top\"\u003e\u003cp\u003e\u003cstrong\u003eDrainage Density\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"13.46938775510204%\" rowspan=\"4\" valign=\"top\"\u003e\u003cp\u003em\u003csup\u003e3\u003c/sup\u003e/km\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"26.122448979591837%\"\u003e\u003cp\u003e336.422 to 252.317\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"20.20408163265306%\" valign=\"top\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"18.979591836734695%\" rowspan=\"4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e252.3147 to 168.211\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e168.211 to 84.1055\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e84.1055 to 0.00\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"21.224489795918366%\" rowspan=\"5\" valign=\"top\"\u003e\u003cp\u003e\u003cstrong\u003eAnnual Rainfall Distribution\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"13.46938775510204%\" rowspan=\"5\" valign=\"top\"\u003e\u003cp\u003emm/year\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"26.122448979591837%\"\u003e\u003cp\u003e3.994 to 3.999\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"20.20408163265306%\" valign=\"top\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"18.979591836734695%\" rowspan=\"5\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e4.00 to 4.004\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e4.005 to 4.010\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e4.011 to 4.015\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"56.38766519823788%\"\u003e\u003cp\u003e4.016 to 4.020\u003c/p\u003e\u003c/td\u003e\u003ctd width=\"43.61233480176212%\" valign=\"top\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eAs per the obtained FSM, it is observed that the maximum percentage criteria weight is experienced by Annual Rainfall Distribution with a value of 30% followed by Drainage Density at 22%, Distance to stream at 15%, TWI at 12%, LULC at 7%, Soil at 5%, Lithology at 4%, Slope at 3%, Elevation at 2% hence it is conclusive that the Annual Rainfall Distribution plays the vital role in determining the flood-prone area in Adayar River Basin and Elevation affects the least. Furthermore, the FSM is divided into five different classes viz. Very Low flood-prone area, Low flood-prone area, Moderate flood-prone area, High flood-prone area, and Very High flood-prone area with a percentage distribution of 10%, 22%, 29%, 25%, and 13% respectively.\u003c/p\u003e\u003cp\u003eIt is also evident from the FSM that the headwaters are the main regions that are susceptible to flood as they experience maximum annual rainfall, low drainage density, very wet areas, covered by mostly orthic luvisols, and moderately sloped and moderately elevated.\u003c/p\u003e\u003cp\u003eBased on the following characteristics the installation areas are selected emphasizing mainly the three regions of FSM that are a very highly susceptible region (10 areas), high susceptible region (16 areas), an intermediate susceptible region (14 areas), and those of total 40 regions are mentioned in Table \u003cspan\u003e5\u003c/span\u003e and the pictorial presentation of the areas are mentioned in Fig. \u003cspan\u003e14\u003c/span\u003e.\u003c/p\u003e\u003cdiv align=\"left\"\u003e\u003cbr\u003e\u003c/div\u003e\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv\u003eTable 5\u003c/div\u003e\u003cdiv\u003e\u003cp\u003eCategories of the selected areas\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\u003cp\u003eSusceptibility ratings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\"\u003e\u003cp\u003eName of the Areas\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eVery High Susceptible\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eGuduperembedu, Karanaipuducheri, Karasangal, Keelakalani, Korukkanthangal, Padappai, Valathancheri, Vandalur, Vellarai, Venjuvancheri\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eHigh Susceptible\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eAdhanur, Erumaiyur R.F., Irumbedu, Irumngattukottai, Kaduvancheri, Katrambakkam, Kiloy, Nallur R.F., Nemam, Nemili, Pondur, Sembarambakkam, Sethupattu, Sirumathur, Sriperumbadur, Varadharajapuram\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\u003cp\u003e\u003cstrong\u003eIntermediate Susceptible\u003c/strong\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\"\u003e\u003cp\u003eChennai, Gudupakkam, Kattupakkam, Kavam, Kilmanambedu, Malayambakkam, Manapakkam, Mangadu, Mevalurkuppam, Mugalivakkam, Poprur, Ramapuram, Thodukadu, Valasaravakkam\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eIn conclusion, the research work represents a significant step towards mitigating the impacts of recurrent flooding in the region. By integrating spatial and non-spatial factors and employing a robust decision-making framework, this study has provided valuable insights into understanding the complex dynamics of flood vulnerability within the basin.\u003c/p\u003e \u003cp\u003eThe findings of this research highlight the importance of considering factors such as topography, land use land cover, soil type, topographical wetness index, annual rainfall distribution, and proximity to water bodies in assessing flood susceptibility. Through the visualization of spatial patterns and identifying high-risk areas, decision-makers are empowered to prioritize mitigation strategies and allocate resources effectively.\u003c/p\u003e \u003cp\u003eFurthermore, the development of the flood susceptibility map serves as a crucial tool for enhancing disaster management and urban planning efforts in the Adayar River Basin. By identifying vulnerable areas and informing proactive measures, such as infrastructure improvements, land use planning, and early warning systems, the resilience of communities and infrastructure can be significantly enhanced.\u003c/p\u003e \u003cp\u003eUltimately, this research highlights the significance of utilizing geospatial tools and decision support systems to tackle the issues presented by natural hazards, thereby fostering sustainable development and enhancing the resilience of the communities residing within the Adayar River Basin region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e "},{"header":"Further Research Prospects","content":"\u003cp\u003e\u003cstrong\u003eRefinement of Methodologies.\u003c/strong\u003e Continuously improving and refining the methodologies used for flood susceptibility mapping can enhance the accuracy and reliability of the results. Future research could explore advanced remote sensing techniques, machine learning algorithms, and high-resolution modeling approaches to better capture the complex interactions of factors influencing flood vulnerability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDynamic Mapping. Approaches.\u003c/strong\u003e Developing dynamic flood susceptibility maps that can adapt to changing environmental conditions and human activities could be an area of future research. Incorporating real-time data streams, such as rainfall forecasts, river flow monitoring, and land use changes, can improve the timeliness and responsiveness of flood risk assessments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCommunity-Based Monitoring and Early Warning Systems.\u003c/strong\u003e Engaging local communities in flood monitoring and early warning systems can enhance resilience and preparedness at the grassroots level. Future research could explore participatory approaches for collecting and disseminating flood-related information, leveraging community networks and indigenous knowledge systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross-Sectoral Collaboration.\u003c/strong\u003e Promoting interdisciplinary collaboration between researchers, policymakers, urban planners, engineers, and community stakeholders is crucial for addressing complex flood vulnerability issues. Future studies could explore innovative frameworks for cross-sectoral knowledge exchange and collaboration to facilitate integrated flood risk management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEcosystem-Based Approaches.\u003c/strong\u003e Investigating the role of natural ecosystems, such as wetlands, mangroves, and floodplains, in mitigating flood risk could be an emerging area of research. Understanding the ecosystem services provided by these habitats and incorporating them into flood susceptibility mapping can lead to nature-based solutions for reducing vulnerability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolicy Implementation and Evaluation.\u003c/strong\u003e Assessing the effectiveness of policies and interventions based on flood susceptibility mapping is essential for informed decision-making. Future research could focus on evaluating the impact of land use regulations, infrastructure investments, and disaster risk reduction measures on reducing flood risk and enhancing community resilience.\u003c/p\u003e\n\u003ch2\u003e4.1 Limitations\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eData Limitations.\u003c/strong\u003e Availability and quality of data, including topographic data, land use/land cover data, rainfall data, and historical flood data, can significantly impact the accuracy of the susceptibility map. Limited or outdated data may result in inaccuracies in identifying vulnerable areas.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScale and Resolution.\u003c/strong\u003e The scale and resolution of data used in the study can affect the precision of the susceptibility map. Fine-scale data may not be available or feasible to obtain, particularly in densely populated or remote areas, leading to coarse-resolution maps that may overlook localized flood risks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Uncertainty.\u003c/strong\u003e Flood susceptibility mapping involves the use of various modeling techniques and assumptions, leading to inherent uncertainties in the results. Uncertainties may arise from model parameters, input data, and modeling algorithms, which can affect the reliability and interpretation of the susceptibility map.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComplexity of Flood Processes.\u003c/strong\u003e Flood susceptibility mapping often simplifies the complex interactions between natural and anthropogenic factors influencing flood risk. It may overlook localized phenomena such as urban drainage systems, floodplain dynamics, and hydraulic infrastructure, which can significantly influence flood susceptibility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemporal Dynamics.\u003c/strong\u003e Flood susceptibility is dynamic and can change over time due to land use changes, urbanization, climate variability, and adaptation measures. A static susceptibility map may become outdated quickly, necessitating periodic updates and revisions to reflect evolving flood risk scenarios.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgment. The author expresses their gratitude to the SRM Institute of Science and Technology, Kattankulathur for their assistance and exceptional laboratory resources in facilitating the study. The provision of complementary data dissemination by the Survey of India, United States Geological Survey (USGS), BHUKOSH, HydroSHEDS, Climatic Research Unit (CRU), and Food and Agriculture Organization of the United Nations (FAO) is highly valued in fulfillment of the manuscript.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; Contribution. The conceptualization; methodology; investigation and design of the study involved contributions from all the authors. Aritra Poddar, Gayathri Varatharanjan, and Aditya Aryan were responsible for material preparation, data collection, and analysis. Manimaran Asaithambi wrote the Original draft of the manuscript; validated; supervised; wrote \u0026ndash; reviewed and editing of the final manuscript.\u003c/p\u003e\n\u003cp\u003eData Availability. Data will be shared on request.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding.\u003c/strong\u003e The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests.\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAli S A, Khatun R, Ahmad A, Ahmad (2019) Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India. Modeling Earth Systems and Environment, 5(3), 1083\u0026ndash;1102 https://doi.org/10.1007/s40808-019-00593-z\u003c/li\u003e\n\u003cli\u003eAnnual Report NDMA GOI (2022-2023)\u003c/li\u003e\n\u003cli\u003eArivazhagan R, Sureshraj R I, Thenmozhi E, Bhuvaneshwari B, Vishnu N (2019) Numerical modelling of adyar river. International Journal of Recent Technology and Engineering, 8(3), 6965\u0026ndash;6970 https://doi.org/10.35940/ijrte.C6012.098319\u003c/li\u003e\n\u003cli\u003eBagyaraj M, Senapathi V, Chung S Y, Gopalakrishnan G, Xiao Y, Karthikeyan S, Nadiri A, Barzegar R (2023) A geospatial approach for assessing urban flood risk zones in Chennai, Tamil Nadu, India. Environmental Science and Pollution Research, 30(45), 100562\u0026ndash;100575 https://doi.org/10.1007/s11356-023-29132-1\u003c/li\u003e\n\u003cli\u003eBeheshtifar S (2023) Identification of landslide-prone zones using a GIS-based multi-criteria decision analysis and region-growing algorithm in uncertain conditions. Natural Hazards, 115(2), 1475\u0026ndash;1497 https://doi.org/10.1007/s11069-022-05603-5\u003c/li\u003e\n\u003cli\u003eBeven K J, Kirkby M J (1979) A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin, 24(1), 43\u0026ndash;69 https://doi.org/10.1080/02626667909491834\u003c/li\u003e\n\u003cli\u003eBreinl K, Lun D, M\u0026uuml;ller Thomy H, Bl\u0026ouml;schl G (2021) Understanding the relationship between rainfall and flood probabilities through combined intensity-duration-frequency analysis. Journal of Hydrology, 602 https://doi.org/10.1016/j.jhydrol.2021.126759\u003c/li\u003e\n\u003cli\u003eBrody S, Blessing R, Sebastian A, Bedient P (2014) Examining the impact of land use/land cover characteristics on flood losses. Journal of Environmental Planning and Management, 57(8) https://doi.org/10.1080/09640568.2013.802228\u003c/li\u003e\n\u003cli\u003eChakraborty S, Mukhopadhyay S (2019) Assessing flood risk using analytical hierarchy process (AHP) and geographical information system (GIS): application in Coochbehar district of West Bengal, India. Natural Hazards, 99(1), 247\u0026ndash;274 https://doi.org/10.1007/s11069-019-03737-7\u003c/li\u003e\n\u003cli\u003eChifflard P, Kranl J, Strassen G Zur, Zepp H (2018) The significance of soil moisture in forecasting characteristics of flood events. A statistical analysis in two nested catchments. Journal of Hydrology and Hydromechanics, 66(1) https://doi.org/10.1515/johh-2017-0037\u003c/li\u003e\n\u003cli\u003eDung N B, Minh D T, Ahmad A, Long N Q (2020) The role of relative slope length in flood hazard mapping using ahp and GIS (Case study: Lam river basin, Vietnam). Geography, Environment, Sustainability, 13(2) https://doi.org/10.24057/2071-9388-2020-48\u003c/li\u003e\n\u003cli\u003eFaiz Ahmed C, Kranthi N (2018) Flood Vulnerability Assessment using Geospatial Techniques: Chennai, India. Indian Journal of Science and Technology, 11(6), 1\u0026ndash;13 https://doi.org/10.17485/ijst/2018/v11i6/110831\u003c/li\u003e\n\u003cli\u003eGhosh A, Kar S K (2018) Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal, India. Natural Hazards, 94(1) https://doi.org/10.1007/s11069-018-3392-y\u003c/li\u003e\n\u003cli\u003eHuang H, Liao W, Lei X, Wang C, Cai Z, Wang H (2023) An urban DEM reconstruction method based on multisource data fusion for urban pluvial flooding simulation. Journal of Hydrology, 617 https://doi.org/10.1016/j.jhydrol.2022.128825\u003c/li\u003e\n\u003cli\u003eJonkman S (2014) Loss of Life Due to Floods: General Overview. In Drowning https://doi.org/10.1007/978-3-642-04253-9_148\u003c/li\u003e\n\u003cli\u003eKassaye S M, Tadesse T, Tegegne G, Hordofa A T (2024) Integrated impact of land use/cover and topography on hydrological extremes in the Baro River Basin. Environmental Earth Sciences, 83(2) https://doi.org/10.1007/s12665-023-11378-0\u003c/li\u003e\n\u003cli\u003eKaya C M, Derin L (2023) Parameters and methods used in flood susceptibility mapping: a review. In Journal of Water and Climate Change (Vol. 14, Issue 6, pp. 1935\u0026ndash;1960), IWA Publishing https://doi.org/10.2166/wcc.2023.035\u003c/li\u003e\n\u003cli\u003eKnight Y, Yu B, Jenkins G, Morris K, Comparing rainfall interpolation techniques for small subtropical urban catchments.\u003c/li\u003e\n\u003cli\u003eKumar H, Karwariya S K, Kumar R (2022) Google Earth Engine-Based Identification of Flood Extent and Flood-Affected Paddy Rice Fields Using Sentinel-2 MSI and Sentinel-1 SAR Data in Bihar State, India. Journal of the Indian Society of Remote Sensing, 50(5) https://doi.org/10.1007/s12524-021-01487-3\u003c/li\u003e\n\u003cli\u003eMateo J R S C (2012) Multi-criteria analysis. Green Energy and Technology, 83, 7\u0026ndash;10 https://doi.org/10.1007/978-1-4471-2346-0_2\u003c/li\u003e\n\u003cli\u003eMehravar S, Razavi-Termeh S V, Moghimi A, Ranjgar B, Foroughnia F, Amani M (2023). Flood susceptibility mapping using multi-temporal SAR imagery and novel integration of nature-inspired algorithms into support vector regression. Journal of Hydrology, 617 https://doi.org/10.1016/j.jhydrol.2023.129100\u003c/li\u003e\n\u003cli\u003eMerz B, Bl\u0026ouml;schl G, Vorogushyn S, Dottori F, Aerts J C J H, Bates P, Bertola M, Kemter M, Kreibich H, Lall U, Macdonald E (2021) Causes, impacts and patterns of disastrous river floods. In Nature Reviews Earth and Environment (Vol. 2, Issue 9, pp. 592\u0026ndash;609). Springer Nature https://doi.org/10.1038/s43017-021-00195-3\u003c/li\u003e\n\u003cli\u003eMitra R, Saha P, Das J (2022) Assessment of the performance of GIS-based analytical hierarchical process (AHP) approach for flood modeling in Uttar Dinajpur district of West Bengal, India. Geomatics, Natural Hazards and Risk, 13(1) https://doi.org/10.1080/19475705.2022.2112094\u003c/li\u003e\n\u003cli\u003eMohanty M P, Mudgil S, Karmakar S (2020) Flood management in India: A focussed review on the current status and future challenges. In International Journal of Disaster Risk Reduction (Vol. 49) Elsevier Ltd. https://doi.org/10.1016/j.ijdrr.2020.101660\u003c/li\u003e\n\u003cli\u003eMukherjee R, Deb P (2023) Application of GIS-based analytical hierarchy process for assessment and mapping of flood risk zone in the lower Ramganga River Basin, Western Gangetic Plain, India. Environment, Development and Sustainability https://doi.org/10.1007/s10668-023-02957-z\u003c/li\u003e\n\u003cli\u003eNadal N C, Zapata R E, Pag\u0026aacute;n I, L\u0026oacute;pez R, Agudelo J (2010) Building Damage due to Riverine and Coastal Floods. Journal of Water Resources Planning and Management, 136(3) https://doi.org/10.1061/(ASCE)wr.1943-5452.0000036\u003c/li\u003e\n\u003cli\u003eNRSC/ISRO (2015) Hydrological Simulation Study of Flood Disaster in Adyar and Cooum Rivers, Tamilnadu.\u003c/li\u003e\n\u003cli\u003eParida Y, Roy Chowdhury J, Saini S, Dash D P (2022) Role of income and government responsiveness in reducing the death toll from floods in Indian states. Scientific Reports, 12(1) https://doi.org/10.1038/s41598-022-21334-w\u003c/li\u003e\n\u003cli\u003ePrincecharles Chukwuemeka Anyadiegwu, Joel Izuchukwu Igbokwe, Adeyemi James Adeboboye (2021) Assessment of flood hazard in Owerri West, Imo State, Nigeria using Analytical Hierarchical Process and Index-Based Approach. World Journal of Advanced Research and Reviews, 12(2) https://doi.org/10.30574/wjarr.2021.12.2.0517\u003c/li\u003e\n\u003cli\u003ePriya Rajan S M, Nellayaputhenpeedika M, Tiwari S P, Vengadasalam R (2020) Mapping and analysis of the physical vulnerability of coastal Tamil Nadu. Human and Ecological Risk Assessment, 26(7), 1879\u0026ndash;1895 https://doi.org/10.1080/10807039.2019.1602752\u003c/li\u003e\n\u003cli\u003eRimba A, Setiawati M, Sambah A, Miura F (2017) Physical Flood Vulnerability Mapping Applying Geospatial Techniques in Okazaki City, Aichi Prefecture, Japan. Urban Science, 1(1) https://doi.org/10.3390/urbansci1010007\u003c/li\u003e\n\u003cli\u003eSahnoun H, Serbaji M, Karray B, Medhioub K (2012) GIS and multi-criteria analysis to select potential sites of agro-industrial complex. Environmental Earth Sciences, 66(8) https://doi.org/10.1007/s12665-011-1471-4\u003c/li\u003e\n\u003cli\u003eSelvam R A, Antony Jebamalai A R (2023) Application of the analytical hierarchy process (AHP) for flood susceptibility mapping using GIS techniques in Thamirabarani river basin, Srivaikundam region, Southern India. Natural Hazards, 118(2), 1065\u0026ndash;1083 https://doi.org/10.1007/s11069-023-06037-3\u003c/li\u003e\n\u003cli\u003eSharif M, Dhillon M S, Chandra S, Kumar M, Vasanthakumar V (2020) Urban Flooding\u0026mdash;A Case Study of Chennai Floods of 2015. In Lecture Notes in Civil Engineering (Vol. 58) https://doi.org/10.1007/978-981-15-2545-2_64\u003c/li\u003e\n\u003cli\u003eSundaram S, Devaraj S, Yarrakula K (2021) Modeling, mapping, and analysis of urban floods in India\u0026mdash;a review on geospatial methodologies. In Environmental Science and Pollution Research (Vol. 28, Issue 48) https://doi.org/10.1007/s11356-021-16747-5\u003c/li\u003e\n\u003cli\u003eSatty T L (1980) The Analytical Hierarchy Process, McGraw\u0026ndash;Hill, New York\u003c/li\u003e\n\u003cli\u003eVojtek, M, Vojtekov\u0026aacute; J (2019) Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water (Switzerland), 11(2) https://doi.org/10.3390/w11020364\u003c/li\u003e\n\u003cli\u003eZlaugotne B, Zihare L, Balode L, Kalnbalkite A, Khabdullin A, Blumberga D (2020) Multi-Criteria Decision Analysis Methods Comparison. Environmental and Climate Technologies, 24(1) https://doi.org/10.2478/rtuect-2020-0028\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Flood Susceptibility Map, Geographic Information System (GIS), Analytical Hierarchical Process (AHP), Pairwise Comparison, Adayar River Basin","lastPublishedDoi":"10.21203/rs.3.rs-4180384/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4180384/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Adayar River Basin in Chennai, Tamil Nadu, is plagued by recurring inundation events, posing substantial hazards to human settlements and critical infrastructure. In response, this research endeavors to develop a flood susceptibility map to pinpoint regions within the basin prone to flooding. Leveraging Geographic Information Systems (GIS) and employing the Analytical Hierarchy Process (AHP) methodology via GIS software, an array of spatial and non-spatial variables influencing flood susceptibility were meticulously examined and weighted. By integrating diverse hydrological, geological, and meteorological parameters and applying AHP's pairwise comparison, a holistic understanding of flood susceptibility was attained. The GIS approach enables visualizing spatial patterns and identifying high-risk flood areas. In this paper, the flood susceptibility map has been characterized into five different classes which include Very High region, High region, Moderate region, Low region, and Very Low region, based on this characterization a total of 40 vulnerable areas have been identified with 10 very high susceptible areas followed by 16 highly susceptible areas and 14 moderately susceptible areas.\u003c/p\u003e","manuscriptTitle":"Flood Susceptibility Mapping to Identify the Vulnerable Areas in the Adayar River Basin at Chennai, Tamil Nadu","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-03 08:52:11","doi":"10.21203/rs.3.rs-4180384/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":"61b94ab4-167e-476b-8d02-7effbb35be3e","owner":[],"postedDate":"April 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-12T20:25:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-03 08:52:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4180384","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4180384","identity":"rs-4180384","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}