Waterborne diseases vulnerability analysis using fuzzy analytic hierarchy process: a case study of Azamgarh city, India
This study presents the application of fuzzy analytic hierarchy process (FAHP) aided with geospatial analysis for identifying vulnerable zones to waterborne diseases in Azamgarh city, India. It synergistically employs two stages analyses; the first stage analyses eight determining criteria of waterb...
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Veröffentlicht in: | Modeling earth systems and environment 2022-06, Vol.8 (2), p.2687-2713 |
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description | This study presents the application of fuzzy analytic hierarchy process (FAHP) aided with geospatial analysis for identifying vulnerable zones to waterborne diseases in Azamgarh city, India. It synergistically employs two stages analyses; the first stage analyses eight determining criteria of waterborne diseases, five from socio-economic factors, one from criteria from drinking water (WQI) and two criteria from environmental factors to support the second stage analysis using FAHP. Moreover, weighted overlay analysis was utilised to produce final vulnerability map. The study of triangular fuzzy numbers and extent analysis shows that major responsible factors for controlling the distribution of waterborne diseases in the city are water quality index, irregular water supply and improper sanitation with 0.247, 0.204 and 0.194 weights respectively. The result shows that about 1.99% area in the city lies in very high vulnerability zone, 16.48% in high category, 37.11% in medium category, 37.30 in low category and 7.12% in very low category. Similarly, it has been found that most of the area under high vulnerable zones is found near the city centre in the central congested part of the city. Validation of the results with ground data of occurrence of waterborne diseases has shown that high incidence of waterborne diseases were in conformity with the most vulnerable zones of waterborne diseases. Hence, the current model to identify vulnerable zones to waterborne diseases is validated. The study result also suggests that the study approach adopted and its application process can be employed in other studies to identify vulnerable zones for various diseases. |
doi_str_mv | 10.1007/s40808-021-01237-x |
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It synergistically employs two stages analyses; the first stage analyses eight determining criteria of waterborne diseases, five from socio-economic factors, one from criteria from drinking water (WQI) and two criteria from environmental factors to support the second stage analysis using FAHP. Moreover, weighted overlay analysis was utilised to produce final vulnerability map. The study of triangular fuzzy numbers and extent analysis shows that major responsible factors for controlling the distribution of waterborne diseases in the city are water quality index, irregular water supply and improper sanitation with 0.247, 0.204 and 0.194 weights respectively. The result shows that about 1.99% area in the city lies in very high vulnerability zone, 16.48% in high category, 37.11% in medium category, 37.30 in low category and 7.12% in very low category. Similarly, it has been found that most of the area under high vulnerable zones is found near the city centre in the central congested part of the city. Validation of the results with ground data of occurrence of waterborne diseases has shown that high incidence of waterborne diseases were in conformity with the most vulnerable zones of waterborne diseases. Hence, the current model to identify vulnerable zones to waterborne diseases is validated. The study result also suggests that the study approach adopted and its application process can be employed in other studies to identify vulnerable zones for various diseases.</description><identifier>ISSN: 2363-6203</identifier><identifier>EISSN: 2363-6211</identifier><identifier>DOI: 10.1007/s40808-021-01237-x</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Analysis ; Analytic hierarchy process ; Chemistry and Earth Sciences ; City centres ; Computer Science ; Criteria ; Diseases ; Drinking water ; Earth and Environmental Science ; Earth Sciences ; Earth System Sciences ; Economic analysis ; Economic factors ; Economics ; Ecosystems ; Environment ; Environmental factors ; Math. Appl. in Environmental Science ; Mathematical Applications in the Physical Sciences ; Original Article ; Physics ; Sanitation ; Social factors ; Socioeconomic aspects ; Socioeconomic factors ; Socioeconomics ; Spatial analysis ; Statistics for Engineering ; Vulnerability ; Water quality ; Water supply ; Waterborne diseases</subject><ispartof>Modeling earth systems and environment, 2022-06, Vol.8 (2), p.2687-2713</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021</rights><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-fa09ffc5bdce67f3e251044698af3cfdcba465120138cf4d8da82468b963ce923</citedby><cites>FETCH-LOGICAL-c319t-fa09ffc5bdce67f3e251044698af3cfdcba465120138cf4d8da82468b963ce923</cites><orcidid>0000-0002-8815-6661</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s40808-021-01237-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s40808-021-01237-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51298</link.rule.ids></links><search><creatorcontrib>Ajmal, Uzma</creatorcontrib><creatorcontrib>Jamal, Saleha</creatorcontrib><creatorcontrib>Ahmad, Wani Suhail</creatorcontrib><creatorcontrib>Ali, Md Ashif</creatorcontrib><creatorcontrib>Ali, Md Babor</creatorcontrib><title>Waterborne diseases vulnerability analysis using fuzzy analytic hierarchy process: a case study of Azamgarh city, India</title><title>Modeling earth systems and environment</title><addtitle>Model. Earth Syst. Environ</addtitle><description>This study presents the application of fuzzy analytic hierarchy process (FAHP) aided with geospatial analysis for identifying vulnerable zones to waterborne diseases in Azamgarh city, India. It synergistically employs two stages analyses; the first stage analyses eight determining criteria of waterborne diseases, five from socio-economic factors, one from criteria from drinking water (WQI) and two criteria from environmental factors to support the second stage analysis using FAHP. Moreover, weighted overlay analysis was utilised to produce final vulnerability map. The study of triangular fuzzy numbers and extent analysis shows that major responsible factors for controlling the distribution of waterborne diseases in the city are water quality index, irregular water supply and improper sanitation with 0.247, 0.204 and 0.194 weights respectively. The result shows that about 1.99% area in the city lies in very high vulnerability zone, 16.48% in high category, 37.11% in medium category, 37.30 in low category and 7.12% in very low category. Similarly, it has been found that most of the area under high vulnerable zones is found near the city centre in the central congested part of the city. Validation of the results with ground data of occurrence of waterborne diseases has shown that high incidence of waterborne diseases were in conformity with the most vulnerable zones of waterborne diseases. Hence, the current model to identify vulnerable zones to waterborne diseases is validated. The study result also suggests that the study approach adopted and its application process can be employed in other studies to identify vulnerable zones for various diseases.</description><subject>Analysis</subject><subject>Analytic hierarchy process</subject><subject>Chemistry and Earth Sciences</subject><subject>City centres</subject><subject>Computer Science</subject><subject>Criteria</subject><subject>Diseases</subject><subject>Drinking water</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Earth System Sciences</subject><subject>Economic analysis</subject><subject>Economic factors</subject><subject>Economics</subject><subject>Ecosystems</subject><subject>Environment</subject><subject>Environmental factors</subject><subject>Math. 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Appl. in Environmental Science</topic><topic>Mathematical Applications in the Physical Sciences</topic><topic>Original Article</topic><topic>Physics</topic><topic>Sanitation</topic><topic>Social factors</topic><topic>Socioeconomic aspects</topic><topic>Socioeconomic factors</topic><topic>Socioeconomics</topic><topic>Spatial analysis</topic><topic>Statistics for Engineering</topic><topic>Vulnerability</topic><topic>Water quality</topic><topic>Water supply</topic><topic>Waterborne diseases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ajmal, Uzma</creatorcontrib><creatorcontrib>Jamal, Saleha</creatorcontrib><creatorcontrib>Ahmad, Wani Suhail</creatorcontrib><creatorcontrib>Ali, Md Ashif</creatorcontrib><creatorcontrib>Ali, Md Babor</creatorcontrib><collection>CrossRef</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</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>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic 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>ProQuest Central China</collection><collection>Environmental Science Collection</collection><jtitle>Modeling earth systems and environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ajmal, Uzma</au><au>Jamal, Saleha</au><au>Ahmad, Wani Suhail</au><au>Ali, Md Ashif</au><au>Ali, Md Babor</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Waterborne diseases vulnerability analysis using fuzzy analytic hierarchy process: a case study of Azamgarh city, India</atitle><jtitle>Modeling earth systems and environment</jtitle><stitle>Model. Earth Syst. Environ</stitle><date>2022-06-01</date><risdate>2022</risdate><volume>8</volume><issue>2</issue><spage>2687</spage><epage>2713</epage><pages>2687-2713</pages><issn>2363-6203</issn><eissn>2363-6211</eissn><abstract>This study presents the application of fuzzy analytic hierarchy process (FAHP) aided with geospatial analysis for identifying vulnerable zones to waterborne diseases in Azamgarh city, India. It synergistically employs two stages analyses; the first stage analyses eight determining criteria of waterborne diseases, five from socio-economic factors, one from criteria from drinking water (WQI) and two criteria from environmental factors to support the second stage analysis using FAHP. Moreover, weighted overlay analysis was utilised to produce final vulnerability map. The study of triangular fuzzy numbers and extent analysis shows that major responsible factors for controlling the distribution of waterborne diseases in the city are water quality index, irregular water supply and improper sanitation with 0.247, 0.204 and 0.194 weights respectively. The result shows that about 1.99% area in the city lies in very high vulnerability zone, 16.48% in high category, 37.11% in medium category, 37.30 in low category and 7.12% in very low category. Similarly, it has been found that most of the area under high vulnerable zones is found near the city centre in the central congested part of the city. Validation of the results with ground data of occurrence of waterborne diseases has shown that high incidence of waterborne diseases were in conformity with the most vulnerable zones of waterborne diseases. Hence, the current model to identify vulnerable zones to waterborne diseases is validated. The study result also suggests that the study approach adopted and its application process can be employed in other studies to identify vulnerable zones for various diseases.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s40808-021-01237-x</doi><tpages>27</tpages><orcidid>https://orcid.org/0000-0002-8815-6661</orcidid></addata></record> |
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subjects | Analysis Analytic hierarchy process Chemistry and Earth Sciences City centres Computer Science Criteria Diseases Drinking water Earth and Environmental Science Earth Sciences Earth System Sciences Economic analysis Economic factors Economics Ecosystems Environment Environmental factors Math. Appl. in Environmental Science Mathematical Applications in the Physical Sciences Original Article Physics Sanitation Social factors Socioeconomic aspects Socioeconomic factors Socioeconomics Spatial analysis Statistics for Engineering Vulnerability Water quality Water supply Waterborne diseases |
title | Waterborne diseases vulnerability analysis using fuzzy analytic hierarchy process: a case study of Azamgarh city, India |
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