Efficiency exploration of frequency ratio, entropy and weights of evidence-information value models in flood vulnerabilityassessment: a study of raiganj subdivision, Eastern India
The primary objective of this research was to assess the efficiency of RS and GIS for predicting the flood risk in Raiganj Sub-division, Eastern India using the Frequency ratio, Entropy index, and Weight of evidence-information Value models. Consequently, for spatial analyses fourteen flood conditio...
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description | The primary objective of this research was to assess the efficiency of RS and GIS for predicting the flood risk in Raiganj Sub-division, Eastern India using the Frequency ratio, Entropy index, and Weight of evidence-information Value models. Consequently, for spatial analyses fourteen flood conditioning variables are constructed. The research region is experiencing floods consequently in the past with moderate to high intensities. The assessment demonstrates that factors such as elevation, LULC, rainfall, distance from rivers, and drainage density contributed significantly to the occurrence of floods. From the estimation, it is found that about 11.02 per cent (Frequency Ratio result), 13.90 per cent (Entropy’s result), and 11.50 per cent (WofE-IV results) of the total area has very high vulnerability status respectively. Around 33 per cent to 47 per cent of the total area of each block in the subdivision is projected to be in danger of floods. The validation of the results indicates that the success rate of the presently constructed maps was 0.933 for the FR model, 0.917 for the SEI model, and 0.907 for the WofE model indicating that the frequency ratio model for mapping flood risk in the study region is more authentic, reliable, and useful for delineating flood vulnerable areas and potential flood risk sites. The findings of the analysis will help planners to develop flood prevention measures as part of regional flood risk management programs, as well as provide a foundation for future research in the study area. |
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Consequently, for spatial analyses fourteen flood conditioning variables are constructed. The research region is experiencing floods consequently in the past with moderate to high intensities. The assessment demonstrates that factors such as elevation, LULC, rainfall, distance from rivers, and drainage density contributed significantly to the occurrence of floods. From the estimation, it is found that about 11.02 per cent (Frequency Ratio result), 13.90 per cent (Entropy’s result), and 11.50 per cent (WofE-IV results) of the total area has very high vulnerability status respectively. Around 33 per cent to 47 per cent of the total area of each block in the subdivision is projected to be in danger of floods. The validation of the results indicates that the success rate of the presently constructed maps was 0.933 for the FR model, 0.917 for the SEI model, and 0.907 for the WofE model indicating that the frequency ratio model for mapping flood risk in the study region is more authentic, reliable, and useful for delineating flood vulnerable areas and potential flood risk sites. The findings of the analysis will help planners to develop flood prevention measures as part of regional flood risk management programs, as well as provide a foundation for future research in the study area.</description><identifier>ISSN: 1436-3240</identifier><identifier>EISSN: 1436-3259</identifier><identifier>DOI: 10.1007/s00477-021-02115-9</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Aquatic Pollution ; Chemistry and Earth Sciences ; Computational Intelligence ; Computer Science ; Drainage density ; Earth and Environmental Science ; Earth Sciences ; Entropy ; Environment ; Environmental risk ; Flood control ; Flood management ; Flood mapping ; Flood predictions ; Floods ; Math. 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Consequently, for spatial analyses fourteen flood conditioning variables are constructed. The research region is experiencing floods consequently in the past with moderate to high intensities. The assessment demonstrates that factors such as elevation, LULC, rainfall, distance from rivers, and drainage density contributed significantly to the occurrence of floods. From the estimation, it is found that about 11.02 per cent (Frequency Ratio result), 13.90 per cent (Entropy’s result), and 11.50 per cent (WofE-IV results) of the total area has very high vulnerability status respectively. Around 33 per cent to 47 per cent of the total area of each block in the subdivision is projected to be in danger of floods. The validation of the results indicates that the success rate of the presently constructed maps was 0.933 for the FR model, 0.917 for the SEI model, and 0.907 for the WofE model indicating that the frequency ratio model for mapping flood risk in the study region is more authentic, reliable, and useful for delineating flood vulnerable areas and potential flood risk sites. The findings of the analysis will help planners to develop flood prevention measures as part of regional flood risk management programs, as well as provide a foundation for future research in the study area.</description><subject>Aquatic Pollution</subject><subject>Chemistry and Earth Sciences</subject><subject>Computational Intelligence</subject><subject>Computer Science</subject><subject>Drainage density</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Entropy</subject><subject>Environment</subject><subject>Environmental risk</subject><subject>Flood control</subject><subject>Flood management</subject><subject>Flood mapping</subject><subject>Flood predictions</subject><subject>Floods</subject><subject>Math. Appl. in Environmental Science</subject><subject>Original Paper</subject><subject>Physics</subject><subject>Probability Theory and Stochastic Processes</subject><subject>Rainfall</subject><subject>Regional development</subject><subject>Risk management</subject><subject>Spatial analysis</subject><subject>Statistics for Engineering</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><issn>1436-3240</issn><issn>1436-3259</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kc1O4zAUhaMRIw0CXmBWlmbbgJ3_sEOoQCUkNrC2buLrjqvU7vgmhbxNn4IH6JON2yDYsbBsX3_nWDonin4Lfik4L6-I86wsY56IwxJ5XP-ITkWWFnGa5PXJ5znjv6ILItMEUZ7WteCn0ftca9MatO3I8G3TOQ-9cZY5zbTHf8Px4TibMbS9d5uRgVXsFc3yb08HDrdGBQxjY7Xz60m_hW7A_W7tFHbEjGW6c06x7dBZ9NCYzvQjECHROtheM2DUD2oMfvudB7MEu9rvaGiU2RoKhjM2B-rRW7awysB59FNDR3jxsZ9FL3fz59uH-PHpfnF78xi3qaj7OM-haFoUUPKsBmwO9wrrEJIqK5GGUVtmKYBIsMUcQjBaixKqShVC6CZJz6I_k-_GuxAG9XLlBm_DlzIpigBVIfpAJRPVekfkUcuNN2vwoxRcHiqSU0Uy1COPFck6iNJJRAG2S_Rf1t-o_gNyGJwo</recordid><startdate>20220601</startdate><enddate>20220601</enddate><creator>Saha, Sunil</creator><creator>Sarkar, Debabrata</creator><creator>Mondal, Prolay</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7XB</scope><scope>88I</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0W</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0003-3263-4377</orcidid></search><sort><creationdate>20220601</creationdate><title>Efficiency exploration of frequency ratio, entropy and weights of evidence-information value models in flood vulnerabilityassessment: a study of raiganj subdivision, Eastern India</title><author>Saha, Sunil ; Sarkar, Debabrata ; Mondal, Prolay</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-55a6bce1a7049aeb55a68e9211d78139aec743aa12ece5ab00ff17a88d611fb23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Aquatic Pollution</topic><topic>Chemistry and Earth Sciences</topic><topic>Computational Intelligence</topic><topic>Computer Science</topic><topic>Drainage density</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Entropy</topic><topic>Environment</topic><topic>Environmental risk</topic><topic>Flood control</topic><topic>Flood management</topic><topic>Flood mapping</topic><topic>Flood predictions</topic><topic>Floods</topic><topic>Math. Appl. in Environmental Science</topic><topic>Original Paper</topic><topic>Physics</topic><topic>Probability Theory and Stochastic Processes</topic><topic>Rainfall</topic><topic>Regional development</topic><topic>Risk management</topic><topic>Spatial analysis</topic><topic>Statistics for Engineering</topic><topic>Waste Water Technology</topic><topic>Water Management</topic><topic>Water Pollution Control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Saha, Sunil</creatorcontrib><creatorcontrib>Sarkar, Debabrata</creatorcontrib><creatorcontrib>Mondal, Prolay</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>DELNET Engineering & Technology Collection</collection><collection>Environment Abstracts</collection><jtitle>Stochastic environmental research and risk assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Saha, Sunil</au><au>Sarkar, Debabrata</au><au>Mondal, Prolay</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Efficiency exploration of frequency ratio, entropy and weights of evidence-information value models in flood vulnerabilityassessment: a study of raiganj subdivision, Eastern India</atitle><jtitle>Stochastic environmental research and risk assessment</jtitle><stitle>Stoch Environ Res Risk Assess</stitle><date>2022-06-01</date><risdate>2022</risdate><volume>36</volume><issue>6</issue><spage>1721</spage><epage>1742</epage><pages>1721-1742</pages><issn>1436-3240</issn><eissn>1436-3259</eissn><abstract>The primary objective of this research was to assess the efficiency of RS and GIS for predicting the flood risk in Raiganj Sub-division, Eastern India using the Frequency ratio, Entropy index, and Weight of evidence-information Value models. Consequently, for spatial analyses fourteen flood conditioning variables are constructed. The research region is experiencing floods consequently in the past with moderate to high intensities. The assessment demonstrates that factors such as elevation, LULC, rainfall, distance from rivers, and drainage density contributed significantly to the occurrence of floods. From the estimation, it is found that about 11.02 per cent (Frequency Ratio result), 13.90 per cent (Entropy’s result), and 11.50 per cent (WofE-IV results) of the total area has very high vulnerability status respectively. Around 33 per cent to 47 per cent of the total area of each block in the subdivision is projected to be in danger of floods. The validation of the results indicates that the success rate of the presently constructed maps was 0.933 for the FR model, 0.917 for the SEI model, and 0.907 for the WofE model indicating that the frequency ratio model for mapping flood risk in the study region is more authentic, reliable, and useful for delineating flood vulnerable areas and potential flood risk sites. The findings of the analysis will help planners to develop flood prevention measures as part of regional flood risk management programs, as well as provide a foundation for future research in the study area.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00477-021-02115-9</doi><tpages>22</tpages><orcidid>https://orcid.org/0000-0003-3263-4377</orcidid></addata></record> |
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subjects | Aquatic Pollution Chemistry and Earth Sciences Computational Intelligence Computer Science Drainage density Earth and Environmental Science Earth Sciences Entropy Environment Environmental risk Flood control Flood management Flood mapping Flood predictions Floods Math. Appl. in Environmental Science Original Paper Physics Probability Theory and Stochastic Processes Rainfall Regional development Risk management Spatial analysis Statistics for Engineering Waste Water Technology Water Management Water Pollution Control |
title | Efficiency exploration of frequency ratio, entropy and weights of evidence-information value models in flood vulnerabilityassessment: a study of raiganj subdivision, Eastern India |
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