Applications of Geoinformatics for Flood Hazard Zonation - A case study of Pozhuthana grama panchayath, Wayanad district, Kerala

Applications of Geoinformatics for Flood Hazard Zonation - A case study of Pozhuthana grama panchayath, Wayanad district, Kerala | 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 Case Report Applications of Geoinformatics for Flood Hazard Zonation - A case study of Pozhuthana grama panchayath, Wayanad district, Kerala Nihala Shirin P, SANJAYAN M S, BRIJESH V K, SWETHA R This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8680245/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Floods are among the most frequent and destructive natural disasters globally, posing significant threats to both human lives and infrastructure. The increasing population density in flood-prone areas, unplanned urban development, and encroachment into natural floodplains have intensified the vulnerability of many communities. This study aims to delineate flood-prone areas within Pozhuthana Grama Panchayath, located in Wayanad district, Kerala, using remote sensing and Geographic Information System (GIS) techniques. The analysis is conducted through a weighted sum overlay method, incorporating multiple thematic layers including slope, elevation, drainage density, rainfall, geology, geomorphology, soil type, and land use land cover. These layers, processed in the ArcGIS environment, were derived from satellite data and secondary sources. The outcome of this analysis is the Flood Hazard Zonation (FHZ) Map, which classifies the region into four distinct flood risk zones: low, moderate, high, and very high. To validate the accuracy of the FHZ map, a comparison was made with past flood events, particularly the 2018 Kerala flood. The comparison indicates that most of the areas falling under the mapped high and very high-risk zones in the map correspond with regions affected during past flood events. A spatial comparison with previous flood events, particularly the major Kerala floods, confirms that most high and very high-risk zones were indeed affected, validating the accuracy of the model. This study can guide land use planning, infrastructure development, and emergency preparedness, contributing to more resilient communities in flood-sensitive regions like Pozhuthana Grama panchayath. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction The word ‘flood’ is derived from the old English term ‘flod’, which is common in Germanic languages. Flood literally means a flowing of water, or an overflowing of land by water, or a deluge. Flood becomes a natural disaster when it poses threat to human life and property. Natural and anthropogenic factors are the main occurrence of the floods. The most flood-affected country in the world is Bangladesh, followed by India, and one-fifth of the global death rate is happening due to floods ( Joy et al., 2019 ). Susceptibility of flash flood has been studied by Chakrabortty et. al., ( 2021 ) by taking a case study of Kangsabati River basin. In the context of the Kangsabati River Basin, a subtropical region in eastern India, flash floods are a common occurrence due to short-duration, high-intensity monsoon precipitation. To address this, researchers have employed a combination of remote sensing, GIS, and machine learning models to develop a flood susceptibility map. Flood vulnerability of urban metropolis, flood risk assesment of various watersheds, and identification and mapping of areas prone to flood has been attempted by different authors (Mann et al, 2023 , Mokhtari et al; 2023 ). These studies have been utilized the capabilities of GIS viz, Analytical Hierarchy Process (AHP), Multi-Criteria Decision Analysis (MCDA) using various geoinfromatics tools, techniques and spatial datasets.Combining various capabilities including AHP, GIS seems to be a potential tool for risk mapping of flood hazards utilizing spatial data. Aichi et al., ( 2024 ) has calculated Flood Risk Index (FHI) making use of a MCDA that combined AHP, GIS and Remote Sensing.The study had identified seven primary flood-causing factors. This method established the significances of each factor’s contribution to flood hazard. Bivariate Statistical Frequency Ratio (FR) models were used by Ashfaq et. al., ( 2025 ) for producing flood in susceptability maps. The Kerala deluge of 2018 stands diffent in terms of the high rainfall occuring from June to August, about 42% above normal and by flooding 13 districts of the state. State's extensive river network, including the Periyar, Pamba, and Chalakudy, has served both as a vital resource and a cause of devastation. Kerala has experienced numerous floods, notably in 1341, 1907, 1924, 1961, 1974, 1992, 2003, 2013, 2018, and 2019. These events clearly demonstrate the remarkable resilience of the affected communities. However, the flood of 2018 stands out as a remarkable moment in the history of Kerala. In Wayanad district, 2018 flood created havocby its wide geographic reach, magnitude of loss and destruction, and also by the effetcs it’s impacted on the population. 2 Materials and Methods 2.1 Study area Pozhuthana Grama Panchayath, the study area ( Fig. 1 ), is located in the Wayanad district. The name "Pozhuthana" is believed to have evolved from the term "Puzha Tanna," referring to the numerous rivers and streams flowing through the grama panchayath. The terrain of Pozhuthana ascends to elevations of up to 2100 meters above sea level. Covering an area of 70.29 square kilometers, the Panchayat includes the villages of Pozhuthana and Achuranam within Vythiri Taluk. The Panchayat is geographically bounded by Tariyod Grama Panchayat to the north, Koyilandy Taluk to the west, Vythiri Grama Panchayath to the south, and Kalpetta Municipality and Vengappally Grama Panchayat to the east. The methodology for assessing flood risk in Pozhuthana Grama Panchayath was based on the Weighted Sum Model (WSM) using ArcGIS 10.8.2. The process began with the selection of the study area, which is prone to frequent flooding due to its undulating topography and high rainfall. Various spatial and non-spatial data were collected from both primary and secondary sources. Physigraphic data including elevation, slope, and drainage density were derived from Digital Elevation Models (DEMs) and Survey of India toposheets. Land use/land cover (LULC) data were obtained from satellite imagery and classified using supervised classification techniques. Geological and geomorphological data were sourced from existing geological maps and refined through field verification. Rainfall distribution map for Pozhuthana Grama Panchayath was proposed using data collected from field monitoring stations All thematic layers were processed and reclassified in ArcGIS based on their influence on flood susceptibility. For instance, low elevation, gentle slope, and high drainage density areas were considered more vulnerable. Weights were assigned to each layer based on expert opinion and literature support. Using the Weighted Overlay tool, these layers were combined using the Weighted Sum Model to generate a flood risk zonation map. The final output categorized the area into low, moderate, and high flood risk zones. The flow chart (Fig no 3.1) shows the step-by-step methodology adopted in the study, from data collection and preprocessing to the preparation of the final flood risk map. 2.2 Field works A detailed field study was conducted in the Pozhuthana Grama Panchayath area. Primary information was collected from 1,120 households, of which 118 houses were directly affected during the 2018 flood event. The remaining sampled houses are located in relatively high-elevation areas. Based on GPS measurements collected during the field survey, the altitude of the study area ranges from 624 m to 1,505 m above mean sea level. 2.3 IDW Inverse Distance Weighted (IDW) interpolation was used to create a spatial rainfall distribution map for Pozhuthana Grama Panchayath using data collected from field monitoring stations. IDW estimates rainfall at unsampled locations by assigning higher weights to nearby stations, assuming that points closer in space are more similar. A power value of 2 was applied to balance the influence of surrounding points, and a fixed search radius ensured consistent interpolation. The resulting raster surface accurately visualizes rainfall variability across the region, aiding in flood risk assessment, water resource planning, and disaster preparedness by identifying high-intensity rainfall zones and vulnerable areas. 3.4 Reclassification R eclassification is a process that modifies attribute values without altering the geometry of spatial features. It simplifies the database by reducing the number of attribute categories, thereby grouping adjacent features with identical values into a single class. Each thematic layer used in the flood susceptibility analysis was reclassified on a common scale of 1 to 5, where rank 5 represents the highest contribution to flood susceptibility and rank 1 represents the lowest contribution. Rank Definitions : Rank 1 – Very Low Influence / Very Low Susceptibility Rank 2 – Low Influence / Low Susceptibility Rank 3 – Moderate Influence / Moderate Susceptibility Rank 4 – High Influence / High Susceptibility Rank 5 – Very High Influence / Very High Susceptibility 2.5 Overlay Analysis Overlay analysis superimposes multiple layers with a common coordinate system to determine spatial relationships and create new geometries. It merges thematic layers, combines attributes, compares variables across coverages, and generates new spatial datasets. Suitability models use overlaid to identify optimal locations for facilities based on criteria such as land use, slope, proximity, and exclusion zones. The steps include selecting criteria, reclassifying data, performing Boolean or map algebra overlay, and extracting suitable sites. Weighted overlay assigns numerical weights to thematic layers based on their relative importance and overlays them for multicriteria evaluation. In this study, thematic layers were generated, rasterized, classified, weighted, ranked using the inverse ranking method, and overlaid to produce and analyze the Flood Hazard Zonation map. 2.6 Preparation of Thematic Maps For this study, eight parameters were used for producing the Flood Hazard map. Shapefile of the initially processed parameters were used to prepare thematic maps. Data were collected from field survey, SRTM DEM, LANDSAT 8 imagery, and the Bhukosh portal. Slope and elevation maps were extracted from DEM, which served as input for GIS analysis. Land Use Land Cover and Geology of the study area were converted to raster using ArcMap 10.8.2 conversion tools. The preparation of a flood hazard zonation map involves integrating thematic layers such as elevation, slope, rainfall, land use/land cover, drainage density, geology, geomorphology, and soil. These parameters are analyzed using GIS-based multi-criteria techniques to identify areas vulnerable to flooding. 2.7 Flood Hazard Index To obtain the Flood Hazard Index, parameters such as slope, elevation, rainfall intensity, drainage density, and land use/land cover were reclassified into five classes using the reclassify tool in ArcMap. These parameters were then overlaid using the weighted sum tool, with each assigned a weight based on its impact ( Table 1 ). Rainfall intensity and elevation received the highest weights, followed by other contributing parameters. The thematic maps were integrated using the Spatial Analyst tool to assess flood risk ( Hagos et al., 2022 ). Weighted overlay analysis allows incorporation of human judgment, with weights representing the relative importance of parameters. Weighted index overlay analysis was used as a simple mathematical model, assigning weights to thematic maps based on influence. Index values, calculated as the product of rank and weightage using the raster calculator, were used to classify flood hazard zones. All thematic layers were overlaid to generate the final flood hazard map, with total weightage summing to 100, producing the flood hazard index. Table 1 Parameters in the rank and weight Parameters Classes Rank Weightage Elevation 624–700 5 20 700–800 4 800–900 3 900–1100 2 1100–1505 1 Slope 0–12.19 5 10 12.19–24.38 4 24.38–36.58 3 36.58–48.77 2 48.77–60.96 1 Rainfall 1638.82–2178.05 1 25 2178.05–2717.28 2 2717.28–3256.51 3 3256.51–3795.74 4 3795.74–4334.98 5 Geomorphology Residual Hill 1 10 Denudational Structural Hills 2 Pediplain 3 Piedmont Zone 4 Water Body 5 Geology Peninsular gneissic complex 3 5 Migmatite complex 2 Charnockite group of rocks 1 High grade metasedimentary 3 Soil Clay 1 10 Gravelly clay 2 Loam 3 Gravelly loam 4 Drainage density 0–0.3 1 10 0.3–0.7 2 0.7–1 3 1–1.4 4 1.4–1.83 5 Land use land cover Water bodies 5 10 Agricultural land 4 Forest 1 Built up 3 Hilly terrain 2 3 RESULTS AND DISCUSSION 3.1 Flood susceptibility map The flood susceptibility map was created using the range of values for individual criterion in five classes. The combined flood susceptibility map for the eight criteria was developed using the sub-criteria classified under each criterion. The primary criteria-based flood susceptibility maps were further used for developing the final flood zoning map. 3.2 Elevation Elevation is a crucial role as one of the primary factors influencing the floods ( Chakrabortty et al. 2021 ). Lower elevation regions are more prone to flooding than higher ones. This is because increased river discharge, leading to high water flow to inundate regions more quickly ( Zzaman et al., 2021 ). In Pozhuthana grama panchayath, the highest elevation value is 1050m and lowest elevation value is 624 m ( Fig. 2 ). 3.3 Slope T he steepness or inclination of a feature from the horizontal plane is represented by its slope ( Hagos et al., 2022 ). When it comes to flooding, flatter surfaces are more susceptible than steeper ones because the water travels more slowly, collects for more extended periods, and gathers there ( Desalegn & Mulu, 2021 ). A high number of floods occur in lower slope area as the water cannot discharge easily (Sarkar, and Mondal. 2019). The slope map of the study area, Pozhuthana Grama Panchayath, reveals that regions with high slopes occupy only a very small portion of the total area. The majority of the terrain is characterized by low to very low slope gradients, indicating a generally gentle topography ( Fig. 3 ). These low slope areas dominate the landscape and are typically more prone to water accumulation, making them more susceptible to flooding. Slope of the area varies from 0° − 60.96° ( Table 2 ). 3.4 Rainfall The amount of rainfall is the most important reason for flooding in any area. Among the different aspects of the climate, rainfall has the most prominent influence on the frequency of flash floods. It is thought to be the main cause of surface runoff (Alkhawaga, and Mohamed. 2025). The inverse Distance Weighted (IDW) interpolation tool is used to prepare the rainfall distribution map ( Fig. 4 ). This map was classified in five categories. 3.5 Geology Geological units the area is comprised of Charnockite group of rocks, High grade meta-sedimentary rocks, migmatite complex, and peninsular gneissic complex. Charnockites, being dense, crystalline igneous or high-grade metamorphic rocks, generally possess very low primary permeability ( Fig. 5 ). This means that water has limited ability to infiltrate directly into the rock matrix through intergranular pores ( Aichi et al., 2024 ; Choudhury et al., 2022 ). When rainfall occurs on charnockite terrains, a substantial amount of water is unable to seep into the ground. 3.6 Geomorphology Geomorphology is considered as a major factor for the occurrence of flood and has an important role. The landform units identified in Pozhuthana Grama panchayath are denudational structural hills, piedmont zone, residual hill, rock exposures, valley fills, and water body ( Fig. 6 ). 3.7 Land use and land cover Land Use and Land Cover is a very important factor in recognizing sensitive regions prone to flooding. Vegetated areas offer levels of protective mechanism, making land less prone to flooding. Therefore, a negative relationship exists between a flood event and vegetation density. The type of soil cover and vegetation can affect rainwater infiltration into the ground, with vegetation providing an advantage through its root system. Manmade structures can decrease water infiltration and increase water flow ( Aichi et al. 2024 ). Thematic map of LULC includes waste lands, forest, built up, agricultural land, and water bodies, etc ( Fig. 7 ). 3.8 Soil Clay soils have very fine particles and high cohesion, allowing them to hold a lot of water. However, their small pore spaces and tendency to swell when wet lead to very low infiltration rates, making them prone to waterlogging and surface flooding (Burros et. al., 1997). The soil map ( Fig. 8 ) of Pozhuthana Grama Panchayath shows that the major soil types are gravelly clay, loam, gravelly loam, and clay. Clay and loam dominate the low-lying areas, contributing to poor drainage and higher flood risk. Gravelly soils are found in slightly elevated regions with better infiltration capacity. 3.9 Drainage Density Aichi et. al., ( 2024 ) states that the drainage network density is another important factor that affects flood risk. It influences the time it takes for water to travel through the watershed and is an indicator of the likelihood of flooding. In the Pozhuthana Grama Panchayath, the Table 2 shows drainage network density did not exceed 1.83 km/km 2 , with a majority of the basin having a density between 0 and 1.83 km/km 2 . The higher values indicating well-drained, dissected terrains and lower values representing poorly drained or flat areas. Areas with high drainage density are more prone to surface runoff and potential flood risk during heavy rainfall events. Table 2 Drainage Density Weightage and Ranking Parameter Range (k m/km 2 ) class Drainage density 0–0.3 1 0.3–0.7 2 0.7–1 3 1–1.4 4 1.4–1.83 5 3.10 Flood Hazard The reclassified parameters are calculated based on the weighted sum. The final output is obtained by overlaying of 8 parameter maps with respect to the weighted sum given. Table 3 show the parameters used in this weighted sum analysis and their corresponding weighted sum given. Rainfall and elevation have been given a highest weightage of 20. Table 3 Parameters of flood hazard map Parameter Weighted sum Land use land cover 10 Elevation 20 Slope 10 Soil 10 Drainage density 10 Rainfall 20 Geology 10 Geomorphology 10 3.11 Flood Hazard Zonation Map Flood hazard assessments and maps typically look at the expected extent and depth of flooding in a given location, based on various parameters. Here the flood hazard zonation map is obtained by overlaying the above-mentioned parameters according to its relation to flood occurrence. The FHZ map ( Fig. 10 ) identifies areas liable to flooding in four classes: (i) Very Low Risk Zone, (ii) Low Risk Zone (iii) Moderate Risk Zone, (iv) High Risk Zone, and (v) Very High-Risk Zone. Spatial analysis reveals that the high and very high-risk zones are predominantly occupying in the northeast part of the study area. In contrast, the south-west region of the Panchayath is primarily characterized by low to moderate flood risk levels. The flood hazard map of Pozhuthana Grama Panchayath shows the distribution of these zones as very low (7.30%), low risk (31.34%), moderate risk (25.05%), high risk (28.86%), and very high risk (7.47%), indicating the overall flooded area pattern ( Table 4 ). Table 4 Class and area in sq.km and % of the FHZ Flood hazard map Flood Hazard Class Area (sq. km) Area (%) Very High-Risk Zone 5.25 7.47% High Risk Zone 20.28 28.86% Moderate Risk Zone 17.61 25.05% Low Risk Zone 22.02 31.34% Very Low Risk Zone 5.13 7.30% This spatial distribution closely correlates with physical and environmental parameters influencing flood susceptibility. The high and very high-risk zones are located in low-lying areas with gentle slopes and soil types such as clay and loam, which have poor infiltration capacity and thus promote surface runoff accumulation. These areas also show higher drainage density, suggesting the presence of more surface channels, which can quickly convey runoff during intense rainfall events. The field survey of 1,120 households, including 118 houses affected during the 2018 flood event, demonstrates strong agreement between observed flood impacts and the mapped flood-prone areas. The elevation of the study area ranges from 624 to 1,505 m above mean sea level, confirming that flood impacts are predominantly concentrated in lower-lying zones, while higher-elevation areas remain largely unaffected. The spatial distribution map shows a high concentration of field-verified households within the Very High Risk and High-Risk zones in the northern part of the study area, indicating a strong correspondence between field observations and model outputs. Notably, the highest flood-prone areas are also found at the lowest elevations, underscoring the strong relationship between elevation and flood hazard in the region. Land use in these flood-prone zones includes dense agricultural activity and human settlements, both of which are particularly vulnerable to waterlogging and flood damage. Conversely, the low and moderate risk zones occupy relatively elevated terrain with well-drained gravelly soils, steeper slopes, and sparser drainage networks, all of which contribute to reduced surface runoff accumulation. 4 Conclusions The primary objective of the study was to identify flood-prone areas within Pozhuthana Grama Panchayath using the weighted sum overlay method in a GIS environment, supported by remote sensing data. The outcome of the analysis, Flood Hazard Zonation (FHZ) Map, classifies the region into four distinct flood risk zones: low, moderate, high, and very high. To validate the accuracy of the FHZ map, a comparison was made with past flood events, particularly the Kerala flood in 2018. The comparison indicates that most of the areas falling under the high and very high-risk zones indicated by the map correspond with regions affected during previous flood events. Combining the high and very high-risk categories reveals that 36.33% of the total area is exposed to flood hazard. The moderate risk zone accounts for 25.05%, while the low and very low risk zones together comprise 38.64% of the study area. According to the flood hazard map, the Pozhuthana and Achooranam areas represent the most critical risk zones within the Pozhuthana Grama Panchayath, falling largely into the 'high' and 'very high' categories. Flood simulation and risk assessments are strategic planning tools for effectively reducing flood risk and damage, despite the fact that they cannot be avoided. A flood management strategy must include the assessment of flood hazard areas combining field study and state-of-the-art technology tools. Declarations The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments Authors express their deep sense gratitude to MES Ponnani College and University of Calicut for the research facilities provided. Authors thank Mr. C K Vishnudas, Executive Director, Hume Center for Ecology and Wildlife Biology, Wayanad for giving valuable guidance and support. Funding No funding, grants or other support was received was received for conducting this study. The authors receive no specific funding for this work Author information Authors and Affiliations Dept. of PG Studies and Research in Geology, MES Ponnani College, Ponnani, Malappuram, Kerala, India. Affiliated to University of Calicut Author contributions P. Nihala Shirin: Conceptualization, Methodology, Formal Analysis, Investigation, Resources, Writing-Original Draft, Data collection, Writing-Review & Editing; M. S. Sanjayan: Formal Analysis, & Writing-Original Draft, & V. K. Brijesh:Validation, Formal Analysis, Data Curation,Review & Editing, Visualization, Supervision; R. Swetha: Formal Analysis, & Data collection Corresponding author Correspondence to M. S. Sanjayan. Clinical Trial Number manuscript Clinical trial number: not applicable. Ethics declarations Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Data availability statement The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Consent to participate Not applicable References Aichi, A., Ikirri, M., Ait Haddou, M., Quesada-Román, A., Sahoo, S., Singha, C., Sajinkumar, K. S., & Abioui, M. (2024). Integrated GIS and analytic hierarchy process for flood risk assessment in the Dades Wadi watershed (Central High Atlas, Morocco). 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Portico. https://doi.org/10.1111/jfr3.12715 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Mar, 2026 Reviews received at journal 13 Mar, 2026 Reviews received at journal 27 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers invited by journal 10 Feb, 2026 Editor assigned by journal 06 Feb, 2026 Submission checks completed at journal 05 Feb, 2026 First submitted to journal 05 Feb, 2026 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. 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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-8680245","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":590424852,"identity":"80a3b253-beab-4dc9-b067-0e3facb3fc81","order_by":0,"name":"Nihala Shirin P","email":"","orcid":"","institution":"MES PONNANI COLLEGE","correspondingAuthor":false,"prefix":"","firstName":"Nihala","middleName":"Shirin","lastName":"P","suffix":""},{"id":590424854,"identity":"2ca1c380-6fa1-43fd-a288-95a8a99fe1d4","order_by":1,"name":"SANJAYAN M 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COLLEGE","correspondingAuthor":false,"prefix":"","firstName":"BRIJESH","middleName":"V","lastName":"K","suffix":""},{"id":590424859,"identity":"87b69ed7-924a-4bdf-aa19-4de2d2443174","order_by":3,"name":"SWETHA R","email":"","orcid":"","institution":"MES PONNANI COLLEGE","correspondingAuthor":false,"prefix":"","firstName":"SWETHA","middleName":"","lastName":"R","suffix":""}],"badges":[],"createdAt":"2026-01-23 14:39:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8680245/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8680245/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102791155,"identity":"759f25da-7bef-472e-b2fe-c39d2118a2bb","added_by":"auto","created_at":"2026-02-16 17:17:53","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":455919,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eStudy area, Pozhuthana Grama Panchayath\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8680245/v1/99bad7c544943e68cbb872d8.jpeg"},{"id":102791153,"identity":"1a99995b-91ca-4871-b53c-26a7fb056112","added_by":"auto","created_at":"2026-02-16 17:17:53","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":133442,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eElevation map of the study area\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8680245/v1/291670bb302178366890bac5.jpeg"},{"id":102962239,"identity":"44f6a0de-6784-4d57-a1c1-36cfe3ac926d","added_by":"auto","created_at":"2026-02-19 04:05:57","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":555408,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSlope map of the study area\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8680245/v1/d39a3fb78756c99736e36a56.jpeg"},{"id":102791156,"identity":"cc633a5e-265d-4936-980b-99e3ce9f770e","added_by":"auto","created_at":"2026-02-16 17:17:53","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":332226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eRainfall map of the study area\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8680245/v1/8e7746a7f70afe3deb4788ab.jpeg"},{"id":102791160,"identity":"fc61b9fb-dd55-4686-aeb5-bb5ba670984e","added_by":"auto","created_at":"2026-02-16 17:17:53","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":173845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGeology map of the study area\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8680245/v1/8782ec27e2adf577949cb61a.jpeg"},{"id":102963011,"identity":"14eebe7b-9ed5-4382-873a-d0dbc9fc3c3f","added_by":"auto","created_at":"2026-02-19 04:12:47","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":203159,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGeomorphology map of the study area\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8680245/v1/0f9f78fe081c1563a83190c9.jpeg"},{"id":102962749,"identity":"7900db81-e248-42a7-a92c-ccb0ff6cf662","added_by":"auto","created_at":"2026-02-19 04:10:56","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":195212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eLand use / land cover map of the study area\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8680245/v1/43a5d8b3c73feffe1257ce15.jpeg"},{"id":102791158,"identity":"206a9ccd-afee-433d-8fac-aa517980b692","added_by":"auto","created_at":"2026-02-16 17:17:53","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":221218,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSoil map of the study area\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8680245/v1/f15f3ac11c7217dbf9c02751.jpeg"},{"id":102791163,"identity":"0627faa4-e6bd-4ff8-bd99-f95785ec2bec","added_by":"auto","created_at":"2026-02-16 17:17:54","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":171979,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDrainage density of the study area\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8680245/v1/8477d3069db5567067508942.jpeg"},{"id":102791162,"identity":"4339e750-d011-49ce-8c6f-38a698fb7a22","added_by":"auto","created_at":"2026-02-16 17:17:53","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":195273,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFlood Hazard Zonation Map of the study area\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8680245/v1/c64e728ac0712a7e73cd44f5.jpeg"},{"id":102965009,"identity":"787b9375-6193-468c-b7db-2c2d0570d428","added_by":"auto","created_at":"2026-02-19 04:29:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3596826,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8680245/v1/0535d076-7837-4ccf-99d6-b51d2d7b7fe3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Applications of Geoinformatics for Flood Hazard Zonation - A case study of Pozhuthana grama panchayath, Wayanad district, Kerala","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003e \u003cem\u003eThe word \u0026lsquo;flood\u0026rsquo; is derived from the old English term \u0026lsquo;flod\u0026rsquo;, which is common in Germanic languages. Flood literally means a flowing of water, or an overflowing of land by water, or a deluge. Flood becomes a natural disaster when it poses threat to human life and property. Natural and anthropogenic factors are the main occurrence of the floods. The most flood-affected country in the world is Bangladesh, followed by India, and one-fifth of the global death rate is happening due to floods (\u003c/em\u003eJoy et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cem\u003eSusceptibility of flash flood has been studied by\u003c/em\u003e Chakrabortty et. al., (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) \u003cem\u003eby taking a case study of Kangsabati River basin. In the context of the Kangsabati River Basin, a subtropical region in eastern India, flash floods are a common occurrence due to short-duration, high-intensity monsoon precipitation. To address this, researchers have employed a combination of remote sensing, GIS, and machine learning models to develop a flood susceptibility map.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eFlood vulnerability of urban metropolis, flood risk assesment of various watersheds, and identification and mapping of areas prone to flood has been attempted by different authors (Mann et al, 2023\u003c/em\u003e, Mokhtari et al; \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eThese studies have been utilized the capabilities of GIS viz, Analytical Hierarchy Process (AHP), Multi-Criteria Decision Analysis (MCDA) using various geoinfromatics tools, techniques and spatial datasets.Combining various capabilities including AHP, GIS seems to be a potential tool for risk mapping of flood hazards utilizing spatial data.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eAichi et al., (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) \u003cem\u003ehas calculated Flood Risk Index (FHI) making use of a MCDA that combined AHP, GIS and Remote Sensing.The study had identified seven primary flood-causing factors. This method established the significances of each factor\u0026rsquo;s contribution to flood hazard. Bivariate Statistical Frequency Ratio (FR) models were used by\u003c/em\u003e Ashfaq et. al., (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) \u003cem\u003efor producing flood in susceptability maps.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe Kerala deluge of 2018 stands diffent in terms of the high rainfall occuring from June to August, about 42% above normal and by flooding 13 districts of the state. State's extensive river network, including the Periyar, Pamba, and Chalakudy, has served both as a vital resource and a cause of devastation. Kerala has experienced numerous floods, notably in 1341, 1907, 1924, 1961, 1974, 1992, 2003, 2013, 2018, and 2019. These events clearly demonstrate the remarkable resilience of the affected communities. However, the flood of 2018 stands out as a remarkable moment in the history of Kerala.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eIn Wayanad district, 2018 flood created havocby its wide geographic reach, magnitude of loss and destruction, and also by the effetcs it\u0026rsquo;s impacted on the population.\u003c/em\u003e \u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003e \u003cem\u003ePozhuthana Grama Panchayath, the study area (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cem\u003e), is located in the Wayanad district. The name \"Pozhuthana\" is believed to have evolved from the term \"Puzha Tanna,\" referring to the numerous rivers and streams flowing through the grama panchayath. The terrain of Pozhuthana ascends to elevations of up to 2100 meters above sea level. Covering an area of 70.29 square kilometers, the Panchayat includes the villages of Pozhuthana and Achuranam within Vythiri Taluk. The Panchayat is geographically bounded by Tariyod Grama Panchayat to the north, Koyilandy Taluk to the west, Vythiri Grama Panchayath to the south, and Kalpetta Municipality and Vengappally Grama Panchayat to the east.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe methodology for assessing flood risk in Pozhuthana Grama Panchayath was based on the Weighted Sum Model (WSM) using ArcGIS 10.8.2. The process began with the selection of the study area, which is prone to frequent flooding due to its undulating topography and high rainfall. Various spatial and non-spatial data were collected from both primary and secondary sources. Physigraphic data including elevation, slope, and drainage density were derived from Digital Elevation Models (DEMs) and Survey of India toposheets. Land use/land cover (LULC) data were obtained from satellite imagery and classified using supervised classification techniques. Geological and geomorphological data were sourced from existing geological maps and refined through field verification. Rainfall distribution map for Pozhuthana Grama Panchayath was proposed using data collected from field monitoring stations\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAll thematic layers were processed and reclassified in ArcGIS based on their influence on flood susceptibility. For instance, low elevation, gentle slope, and high drainage density areas were considered more vulnerable. Weights were assigned to each layer based on expert opinion and literature support. Using the Weighted Overlay tool, these layers were combined using the Weighted Sum Model to generate a flood risk zonation map. The final output categorized the area into low, moderate, and high flood risk zones. The flow chart (Fig no 3.1) shows the step-by-step methodology adopted in the study, from data collection and preprocessing to the preparation of the final flood risk map.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Field works\u003c/h2\u003e \u003cp\u003e \u003cem\u003eA detailed field study was conducted in the Pozhuthana Grama Panchayath area. Primary information was collected from 1,120 households, of which 118 houses were directly affected during the 2018 flood event. The remaining sampled houses are located in relatively high-elevation areas. Based on GPS measurements collected during the field survey, the altitude of the study area ranges from 624 m to 1,505 m above mean sea level.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 IDW\u003c/h2\u003e \u003cp\u003e \u003cem\u003eInverse Distance Weighted (IDW) interpolation was used to create a spatial rainfall distribution map for Pozhuthana Grama Panchayath using data collected from field monitoring stations. IDW estimates rainfall at unsampled locations by assigning higher weights to nearby stations, assuming that points closer in space are more similar. A power value of 2 was applied to balance the influence of surrounding points, and a fixed search radius ensured consistent interpolation. The resulting raster surface accurately visualizes rainfall variability across the region, aiding in flood risk assessment, water resource planning, and disaster preparedness by identifying high-intensity rainfall zones and vulnerable areas.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Reclassification\u003c/h2\u003e \u003cp\u003eR\u003cem\u003eeclassification is a process that modifies attribute values without altering the geometry of spatial features. It simplifies the database by reducing the number of attribute categories, thereby grouping adjacent features with identical values into a single class. Each thematic layer used in the flood susceptibility analysis was reclassified on a common scale of 1 to 5, where rank 5 represents the highest contribution to flood susceptibility and rank 1 represents the lowest contribution.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eRank Definitions\u003c/em\u003e:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eRank 1 \u0026ndash; Very Low Influence / Very Low Susceptibility\u003c/em\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eRank 2 \u0026ndash; Low Influence / Low Susceptibility\u003c/em\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eRank 3 \u0026ndash; Moderate Influence / Moderate Susceptibility\u003c/em\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eRank 4 \u0026ndash; High Influence / High Susceptibility\u003c/em\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eRank 5 \u0026ndash; Very High Influence / Very High Susceptibility\u003c/em\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Overlay Analysis\u003c/h2\u003e \u003cp\u003e \u003cem\u003eOverlay analysis superimposes multiple layers with a common coordinate system to determine spatial relationships and create new geometries. It merges thematic layers, combines attributes, compares variables across coverages, and generates new spatial datasets. Suitability models use overlaid to identify optimal locations for facilities based on criteria such as land use, slope, proximity, and exclusion zones. The steps include selecting criteria, reclassifying data, performing Boolean or map algebra overlay, and extracting suitable sites.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eWeighted overlay assigns numerical weights to thematic layers based on their relative importance and overlays them for multicriteria evaluation. In this study, thematic layers were generated, rasterized, classified, weighted, ranked using the inverse ranking method, and overlaid to produce and analyze the Flood Hazard Zonation map.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Preparation of Thematic Maps\u003c/h2\u003e \u003cp\u003e \u003cem\u003eFor this study, eight parameters were used for producing the Flood Hazard map. Shapefile of the initially processed parameters were used to prepare thematic maps. Data were collected from field survey, SRTM DEM, LANDSAT 8 imagery, and the Bhukosh portal. Slope and elevation maps were extracted from DEM, which served as input for GIS analysis. Land Use Land Cover and Geology of the study area were converted to raster using ArcMap 10.8.2 conversion tools.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe preparation of a flood hazard zonation map involves integrating thematic layers such as elevation, slope, rainfall, land use/land cover, drainage density, geology, geomorphology, and soil. These parameters are analyzed using GIS-based multi-criteria techniques to identify areas vulnerable to flooding.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Flood Hazard Index\u003c/h2\u003e \u003cp\u003e \u003cem\u003eTo obtain the Flood Hazard Index, parameters such as slope, elevation, rainfall intensity, drainage density, and land use/land cover were reclassified into five classes using the reclassify tool in ArcMap. These parameters were then overlaid using the weighted sum tool, with each assigned a weight based on its impact (\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cem\u003e). Rainfall intensity and elevation received the highest weights, followed by other contributing parameters. The thematic maps were integrated using the Spatial Analyst tool to assess flood risk (\u003c/em\u003eHagos et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eWeighted overlay analysis allows incorporation of human judgment, with weights representing the relative importance of parameters. Weighted index overlay analysis was used as a simple mathematical model, assigning weights to thematic maps based on influence. Index values, calculated as the product of rank and weightage using the raster calculator, were used to classify flood hazard zones. All thematic layers were overlaid to generate the final flood hazard map, with total weightage summing to 100, producing the flood hazard index.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters in the rank and weight\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eParameters\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eClasses\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRank\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWeightage\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003eElevation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e624\u0026ndash;700\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003e20\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e700\u0026ndash;800\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e800\u0026ndash;900\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e900\u0026ndash;1100\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1100\u0026ndash;1505\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003eSlope\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0\u0026ndash;12.19\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e12.19\u0026ndash;24.38\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e24.38\u0026ndash;36.58\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e36.58\u0026ndash;48.77\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e48.77\u0026ndash;60.96\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003eRainfall\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1638.82\u0026ndash;2178.05\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003e25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e2178.05\u0026ndash;2717.28\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e2717.28\u0026ndash;3256.51\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e3256.51\u0026ndash;3795.74\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e3795.74\u0026ndash;4334.98\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003eGeomorphology\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eResidual Hill\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDenudational Structural Hills\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePediplain\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePiedmont Zone\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWater Body\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eGeology\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePeninsular gneissic complex\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMigmatite complex\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCharnockite group of rocks\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHigh grade metasedimentary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eSoil\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eClay\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGravelly clay\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLoam\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGravelly loam\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003eDrainage density\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0\u0026ndash;0.3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.3\u0026ndash;0.7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.7\u0026ndash;1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1\u0026ndash;1.4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1.4\u0026ndash;1.83\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003eLand use land cover\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWater bodies\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAgricultural land\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eForest\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBuilt up\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHilly terrain\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Flood susceptibility map\u003c/h2\u003e \u003cp\u003e \u003cem\u003eThe flood susceptibility map was created using the range of values for individual criterion in five classes. The combined flood susceptibility map for the eight criteria was developed using the sub-criteria classified under each criterion. The primary criteria-based flood susceptibility maps were further used for developing the final flood zoning map.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Elevation\u003c/h2\u003e \u003cp\u003e \u003cem\u003eElevation is a crucial role as one of the primary factors influencing the floods (\u003c/em\u003eChakrabortty et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cem\u003eLower elevation regions are more prone to flooding than higher ones. This is because increased river discharge, leading to high water flow to inundate regions more quickly (\u003c/em\u003eZzaman et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cem\u003eIn Pozhuthana grama panchayath, the highest elevation value is 1050m and lowest elevation value is 624 m (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Slope\u003c/h2\u003e \u003cp\u003eT\u003cem\u003ehe steepness or inclination of a feature from the horizontal plane is represented by its slope (\u003c/em\u003eHagos et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eWhen it comes to flooding, flatter surfaces are more susceptible than steeper ones because the water travels more slowly, collects for more extended periods, and gathers there (\u003c/em\u003eDesalegn \u0026amp; Mulu, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003cem\u003e). A high number of floods occur in lower slope area as the water cannot discharge easily (Sarkar, and Mondal. 2019). The slope map of the study area, Pozhuthana Grama Panchayath, reveals that regions with high slopes occupy only a very small portion of the total area. The majority of the terrain is characterized by low to very low slope gradients, indicating a generally gentle topography (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cem\u003e). These low slope areas dominate the landscape and are typically more prone to water accumulation, making them more susceptible to flooding. Slope of the area varies from 0\u0026deg; \u0026minus;\u0026thinsp;60.96\u0026deg; (\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Rainfall\u003c/h2\u003e \u003cp\u003e \u003cem\u003eThe amount of rainfall is the most important reason for flooding in any area. Among the different aspects of the climate, rainfall has the most prominent influence on the frequency of flash floods. It is thought to be the main cause of surface runoff (Alkhawaga, and Mohamed. 2025). The inverse Distance Weighted (IDW) interpolation tool is used to prepare the rainfall distribution map (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cem\u003e). This map was classified in five categories.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Geology\u003c/h2\u003e \u003cp\u003e \u003cem\u003eGeological units the area is comprised of Charnockite group of rocks, High grade meta-sedimentary rocks, migmatite complex, and peninsular gneissic complex. Charnockites, being dense, crystalline igneous or high-grade metamorphic rocks, generally possess very low primary permeability (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cem\u003e). This means that water has limited ability to infiltrate directly into the rock matrix through intergranular pores (\u003c/em\u003eAichi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Choudhury et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eWhen rainfall occurs on charnockite terrains, a substantial amount of water is unable to seep into the ground.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Geomorphology\u003c/h2\u003e \u003cp\u003e \u003cem\u003eGeomorphology is considered as a major factor for the occurrence of flood and has an important role. The landform units identified in Pozhuthana Grama panchayath are denudational structural hills, piedmont zone, residual hill, rock exposures, valley fills, and water body (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Land use and land cover\u003c/h2\u003e \u003cp\u003e \u003cem\u003eLand Use and Land Cover is a very important factor in recognizing sensitive regions prone to flooding. Vegetated areas offer levels of protective mechanism, making land less prone to flooding. Therefore, a negative relationship exists between a flood event and vegetation density. The type of soil cover and vegetation can affect rainwater infiltration into the ground, with vegetation providing an advantage through its root system. Manmade structures can decrease water infiltration and increase water flow (\u003c/em\u003eAichi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). \u003cem\u003eThematic map of LULC includes waste lands, forest, built up, agricultural land, and water bodies, etc (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Soil\u003c/h2\u003e \u003cp\u003e \u003cem\u003eClay soils have very fine particles and high cohesion, allowing them to hold a lot of water. However, their small pore spaces and tendency to swell when wet lead to very low infiltration rates, making them prone to waterlogging and surface flooding (Burros et. al., 1997). The soil map (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u003cem\u003e) of Pozhuthana Grama Panchayath shows that the major soil types are gravelly clay, loam, gravelly loam, and clay. Clay and loam dominate the low-lying areas, contributing to poor drainage and higher flood risk. Gravelly soils are found in slightly elevated regions with better infiltration capacity.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Drainage Density\u003c/h2\u003e \u003cp\u003eAichi et. al., (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) \u003cem\u003estates that the drainage network density is another important factor that affects flood risk. It influences the time it takes for water to travel through the watershed and is an indicator of the likelihood of flooding. In the Pozhuthana Grama Panchayath, the\u003c/em\u003e Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003eshows drainage network density did not exceed 1.83 km/km\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e, \u003cem\u003ewith a majority of the basin having a density between 0 and 1.83 km/km\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e. \u003cem\u003eThe higher values indicating well-drained, dissected terrains and lower values representing poorly drained or flat areas. Areas with high drainage density are more prone to surface runoff and potential flood risk during heavy rainfall events.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDrainage Density Weightage and Ranking\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange (k\u003cem\u003em/km\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eclass\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDrainage density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u0026ndash;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.4\u0026ndash;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Flood Hazard\u003c/h2\u003e \u003cp\u003e \u003cem\u003eThe reclassified parameters are calculated based on the weighted sum. The final output is obtained by overlaying of 8 parameter maps with respect to the weighted sum given.\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cem\u003eshow the parameters used in this weighted sum analysis and their corresponding weighted sum given. Rainfall and elevation have been given a highest weightage of 20.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters of flood hazard map\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eParameter\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWeighted sum\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLand use land cover\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eElevation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e20\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSlope\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSoil\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDrainage density\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRainfall\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e20\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGeology\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGeomorphology\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.11 Flood Hazard Zonation Map\u003c/h2\u003e \u003cp\u003e \u003cem\u003eFlood hazard assessments and maps typically look at the expected extent and depth of flooding in a given location, based on various parameters. Here the flood hazard zonation map is obtained by overlaying the above-mentioned parameters according to its relation to flood occurrence.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe FHZ map (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e\u003cem\u003e) identifies areas liable to flooding in four classes: (i) Very Low Risk Zone, (ii) Low Risk Zone (iii) Moderate Risk Zone, (iv) High Risk Zone, and (v) Very High-Risk Zone. Spatial analysis reveals that the high and very high-risk zones are predominantly occupying in the northeast part of the study area. In contrast, the south-west region of the Panchayath is primarily characterized by low to moderate flood risk levels. The flood hazard map of Pozhuthana Grama Panchayath shows the distribution of these zones as very low (7.30%), low risk (31.34%), moderate risk (25.05%), high risk (28.86%), and very high risk (7.47%), indicating the overall flooded area pattern (\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClass and area in sq.km and % of the FHZ\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cem\u003eFlood hazard map\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFlood Hazard Class\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eArea (sq. km)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eArea (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eVery High-Risk Zone\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5.25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e7.47%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHigh Risk Zone\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e20.28\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e28.86%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eModerate Risk Zone\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e17.61\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e25.05%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLow Risk Zone\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e22.02\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e31.34%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eVery Low Risk Zone\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e5.13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e7.30%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThis spatial distribution closely correlates with physical and environmental parameters influencing flood susceptibility. The high and very high-risk zones are located in low-lying areas with gentle slopes and soil types such as clay and loam, which have poor infiltration capacity and thus promote surface runoff accumulation. These areas also show higher drainage density, suggesting the presence of more surface channels, which can quickly convey runoff during intense rainfall events.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe field survey of 1,120 households, including 118 houses affected during the 2018 flood event, demonstrates strong agreement between observed flood impacts and the mapped flood-prone areas. The elevation of the study area ranges from 624 to 1,505 m above mean sea level, confirming that flood impacts are predominantly concentrated in lower-lying zones, while higher-elevation areas remain largely unaffected. The spatial distribution map shows a high concentration of field-verified households within the Very High Risk and High-Risk zones in the northern part of the study area, indicating a strong correspondence between field observations and model outputs. Notably, the highest flood-prone areas are also found at the lowest elevations, underscoring the strong relationship between elevation and flood hazard in the region.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eLand use in these flood-prone zones includes dense agricultural activity and human settlements, both of which are particularly vulnerable to waterlogging and flood damage. Conversely, the low and moderate risk zones occupy relatively elevated terrain with well-drained gravelly soils, steeper slopes, and sparser drainage networks, all of which contribute to reduced surface runoff accumulation.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003e \u003cem\u003eThe primary objective of the study was to identify flood-prone areas within Pozhuthana Grama Panchayath using the weighted sum overlay method in a GIS environment, supported by remote sensing data. The outcome of the analysis, Flood Hazard Zonation (FHZ) Map, classifies the region into four distinct flood risk zones: low, moderate, high, and very high. To validate the accuracy of the FHZ map, a comparison was made with past flood events, particularly the Kerala flood in 2018. The comparison indicates that most of the areas falling under the high and very high-risk zones indicated by the map correspond with regions affected during previous flood events. Combining the high and very high-risk categories reveals that 36.33% of the total area is exposed to flood hazard. The moderate risk zone accounts for 25.05%, while the low and very low risk zones together comprise 38.64% of the study area. According to the flood hazard map, the Pozhuthana and Achooranam areas represent the most critical risk zones within the Pozhuthana Grama Panchayath, falling largely into the 'high' and 'very high' categories. Flood simulation and risk assessments are strategic planning tools for effectively reducing flood risk and damage, despite the fact that they cannot be avoided. A flood management strategy must include the assessment of flood hazard areas combining field study and state-of-the-art technology tools.\u003c/em\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors express their deep sense gratitude to MES Ponnani College and University of Calicut for the research facilities provided. Authors thank Mr. C K Vishnudas, Executive Director, Hume Center for Ecology and Wildlife Biology, Wayanad for giving valuable guidance and support.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNo funding, grants or other support was received was received for conducting this study. The authors receive no specific funding for this work\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDept. of PG Studies and Research in Geology, MES Ponnani College, Ponnani, Malappuram, Kerala, India. Affiliated to University of Calicut\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eP. Nihala Shirin: Conceptualization, Methodology, Formal Analysis, Investigation, Resources, Writing-Original Draft, Data collection, Writing-Review \u0026amp; Editing; M. S. Sanjayan: Formal Analysis, \u0026amp; Writing-Original Draft, \u0026amp; V. K. Brijesh:Validation, Formal Analysis, Data Curation,Review \u0026amp; Editing, Visualization, Supervision; R. Swetha: Formal Analysis, \u0026amp; Data collection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorrespondence to M. S. Sanjayan.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number manuscript\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClinical trial number: not applicable.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNot applicable.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNot applicable.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors declare no competing interests.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNot applicable\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cem\u003eAichi, A., Ikirri, M., Ait Haddou, M., Quesada-Rom\u0026aacute;n, A., Sahoo, S., Singha, C., Sajinkumar, K. 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Flood hazard assessment and mapping using GIS integrated with multi-criteria decision analysis in upper Awash River basin, Ethiopia. Applied Water Science, 12(7). \u003c/em\u003e\u003cem\u003ehttps://doi.org/10.1007/s13201-022-01674-8\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eJoy, J., Kanga, S., \u0026amp; Singh, S. K. (2019). Kerala flood 2018: flood mapping by participatory GIS approach, Meloor Panchayat. Int J Emerging Techn, 10(1), 197-205.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eMann, R., \u0026amp; Gupta, A. (2023). Mapping flood vulnerability using an analytical hierarchy process (AHP) in the Metropolis of Mumbai. Environmental Monitoring and Assessment, 195(12). \u003c/em\u003e\u003cem\u003ehttps://doi.org/10.1007/s10661-023-12141-5\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eMokhtari, E., Mezali, F., Abdelkebir, B., \u0026amp; Engel, B. (2023). Flood risk assessment using analytical hierarchy process: A case study from the Cheliff-Ghrib watershed, Algeria. Journal of Water and Climate Change, 14(3), 694\u0026ndash;711. \u003c/em\u003e\u003cem\u003ehttps://doi.org/10.2166/wcc.2023.316\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eSarkar, D., \u0026amp; Mondal, P. (2019). Flood vulnerability mapping using frequency ratio (FR) model: a case study on Kulik river basin, Indo-Bangladesh Barind region. Applied Water Science, 10(1). \u003c/em\u003e\u003cem\u003ehttps://doi.org/10.1007/s13201-019-1102-x\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eZzaman, R. U., Nowreen, S., Billah, M., \u0026amp; Islam, A. S. (2021). Flood hazard mapping of Sangu River basin in Bangladesh using multi\u003c/em\u003e\u003cem\u003e‐\u003c/em\u003e\u003cem\u003ecriteria analysis of hydro\u003c/em\u003e\u003cem\u003e‐\u003c/em\u003e\u003cem\u003egeomorphological factors. Journal of Flood Risk Management, 14(3). Portico. \u003c/em\u003e\u003cem\u003ehttps://doi.org/10.1111/jfr3.12715\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8680245/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8680245/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFloods are among the most frequent and destructive natural disasters globally, posing significant threats to both human lives and infrastructure. The increasing population density in flood-prone areas, unplanned urban development, and encroachment into natural floodplains have intensified the vulnerability of many communities. This study aims to delineate flood-prone areas within Pozhuthana Grama Panchayath, located in Wayanad district, Kerala, using remote sensing and Geographic Information System (GIS) techniques. The analysis is conducted through a weighted sum overlay method, incorporating multiple thematic layers including slope, elevation, drainage density, rainfall, geology, geomorphology, soil type, and land use land cover. These layers, processed in the ArcGIS environment, were derived from satellite data and secondary sources.\u003c/p\u003e \u003cp\u003eThe outcome of this analysis is the Flood Hazard Zonation (FHZ) Map, which classifies the region into four distinct flood risk zones: low, moderate, high, and very high. To validate the accuracy of the FHZ map, a comparison was made with past flood events, particularly the 2018 Kerala flood. The comparison indicates that most of the areas falling under the mapped high and very high-risk zones in the map correspond with regions affected during past flood events. A spatial comparison with previous flood events, particularly the major Kerala floods, confirms that most high and very high-risk zones were indeed affected, validating the accuracy of the model. This study can guide land use planning, infrastructure development, and emergency preparedness, contributing to more resilient communities in flood-sensitive regions like Pozhuthana Grama panchayath.\u003c/p\u003e","manuscriptTitle":"Applications of Geoinformatics for Flood Hazard Zonation - A case study of Pozhuthana grama panchayath, Wayanad district, Kerala","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 17:17:45","doi":"10.21203/rs.3.rs-8680245/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-18T09:43:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-13T17:54:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-27T22:47:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200071413670254146880208907975304588769","date":"2026-02-12T16:54:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191995620140616919661276242611221936162","date":"2026-02-11T14:42:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"700560755351568037760096117344730173","date":"2026-02-11T04:12:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-10T20:03:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-06T07:14:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-06T04:14:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Geoscience","date":"2026-02-06T04:08:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aeede273-c302-4d70-863a-3002435ace22","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T06:23:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 17:17:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8680245","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8680245","identity":"rs-8680245","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}