Flood Vulnerability Mapping Using MaxEnt Machine Learning and Analytical Hierarchy Process (AHP) of Kamrup Metropolitan District, Assam
Addressing a natural hazard’s complexity is essential in preventing human fatalities and conserving natural ecosystems as natural hazards are varied and unbalanced in both time and place. Therefore, the main objective of this study is to present a flood vulnerability hazard map and its evaluation fo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Environmental Sciences Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4931/25/1/73 |
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| author | Akshayasimha Channarayapatna Harshasimha Chandra Mohan Bhatt |
| author_facet | Akshayasimha Channarayapatna Harshasimha Chandra Mohan Bhatt |
| author_sort | Akshayasimha Channarayapatna Harshasimha |
| collection | DOAJ |
| description | Addressing a natural hazard’s complexity is essential in preventing human fatalities and conserving natural ecosystems as natural hazards are varied and unbalanced in both time and place. Therefore, the main objective of this study is to present a flood vulnerability hazard map and its evaluation for hazard management and land use planning. The flood inventory map is generated for different flood locations using multiple official reports. To generate the vulnerability maps, a total of nine geo-environmental parameters are chosen as predictors form Maximum Entropy (MaxEnt) machine learning and Analytical Hierarchy Process (AHP). Accuracy assessment of the outputs from MaxEnt is performed using the area under the curve. Similarly, for AHP outputs, the accuracy is tested using the generated inventory map and the AUC. It is observed that topographical wetness index, elevation, and slope are significant for the assessment of flooded areas. Finally, flood hazard maps are generated and a comparative analysis is performed for both methods. According to the study’s findings, The AUC of the flood map generated by MaxEntis 0.83, whereas the AUC of the flood map generated by AHP is 0.76, which means that the flood map generated by MaxEnt is better. From this study, it can be concluded that hazard maps could be a useful tool for local authorities to identify places that are vulnerable to hazards on a large scale. |
| first_indexed | 2024-03-11T02:29:50Z |
| format | Article |
| id | doaj.art-8cbe67c090544a9082bf278581284731 |
| institution | Directory Open Access Journal |
| issn | 2673-4931 |
| language | English |
| last_indexed | 2024-03-11T02:29:50Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Environmental Sciences Proceedings |
| spelling | doaj.art-8cbe67c090544a9082bf2785812847312023-11-18T10:19:56ZengMDPI AGEnvironmental Sciences Proceedings2673-49312023-04-012517310.3390/ECWS-7-14301Flood Vulnerability Mapping Using MaxEnt Machine Learning and Analytical Hierarchy Process (AHP) of Kamrup Metropolitan District, AssamAkshayasimha Channarayapatna Harshasimha0Chandra Mohan Bhatt1Indian Institute of Remote Sensing, Dehradun 248001, IndiaIndian Institute of Remote Sensing, Dehradun 248001, IndiaAddressing a natural hazard’s complexity is essential in preventing human fatalities and conserving natural ecosystems as natural hazards are varied and unbalanced in both time and place. Therefore, the main objective of this study is to present a flood vulnerability hazard map and its evaluation for hazard management and land use planning. The flood inventory map is generated for different flood locations using multiple official reports. To generate the vulnerability maps, a total of nine geo-environmental parameters are chosen as predictors form Maximum Entropy (MaxEnt) machine learning and Analytical Hierarchy Process (AHP). Accuracy assessment of the outputs from MaxEnt is performed using the area under the curve. Similarly, for AHP outputs, the accuracy is tested using the generated inventory map and the AUC. It is observed that topographical wetness index, elevation, and slope are significant for the assessment of flooded areas. Finally, flood hazard maps are generated and a comparative analysis is performed for both methods. According to the study’s findings, The AUC of the flood map generated by MaxEntis 0.83, whereas the AUC of the flood map generated by AHP is 0.76, which means that the flood map generated by MaxEnt is better. From this study, it can be concluded that hazard maps could be a useful tool for local authorities to identify places that are vulnerable to hazards on a large scale.https://www.mdpi.com/2673-4931/25/1/73vulnerability mappingMaximum Entropy (MaxEnt)Analytical Hierarchy Process (AHP)area under the curve (AUC) |
| spellingShingle | Akshayasimha Channarayapatna Harshasimha Chandra Mohan Bhatt Flood Vulnerability Mapping Using MaxEnt Machine Learning and Analytical Hierarchy Process (AHP) of Kamrup Metropolitan District, Assam Environmental Sciences Proceedings vulnerability mapping Maximum Entropy (MaxEnt) Analytical Hierarchy Process (AHP) area under the curve (AUC) |
| title | Flood Vulnerability Mapping Using MaxEnt Machine Learning and Analytical Hierarchy Process (AHP) of Kamrup Metropolitan District, Assam |
| title_full | Flood Vulnerability Mapping Using MaxEnt Machine Learning and Analytical Hierarchy Process (AHP) of Kamrup Metropolitan District, Assam |
| title_fullStr | Flood Vulnerability Mapping Using MaxEnt Machine Learning and Analytical Hierarchy Process (AHP) of Kamrup Metropolitan District, Assam |
| title_full_unstemmed | Flood Vulnerability Mapping Using MaxEnt Machine Learning and Analytical Hierarchy Process (AHP) of Kamrup Metropolitan District, Assam |
| title_short | Flood Vulnerability Mapping Using MaxEnt Machine Learning and Analytical Hierarchy Process (AHP) of Kamrup Metropolitan District, Assam |
| title_sort | flood vulnerability mapping using maxent machine learning and analytical hierarchy process ahp of kamrup metropolitan district assam |
| topic | vulnerability mapping Maximum Entropy (MaxEnt) Analytical Hierarchy Process (AHP) area under the curve (AUC) |
| url | https://www.mdpi.com/2673-4931/25/1/73 |
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