Artificial neural network for flood susceptibility mapping in Bangladesh
The objective of the research is to investigate flood susceptibility in the Sylhet division of Bangladesh. Eight influential factors (i.e., elevation, slope, aspect, curvature, TWI, SPI, roughness, and LULC) were applied as inputs to the model. In this work, 1280 samples were taken at different loca...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-06-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023036666 |
| _version_ | 1797819909936250880 |
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| author | Rhyme Rubayet Rudra Showmitra Kumar Sarkar |
| author_facet | Rhyme Rubayet Rudra Showmitra Kumar Sarkar |
| author_sort | Rhyme Rubayet Rudra |
| collection | DOAJ |
| description | The objective of the research is to investigate flood susceptibility in the Sylhet division of Bangladesh. Eight influential factors (i.e., elevation, slope, aspect, curvature, TWI, SPI, roughness, and LULC) were applied as inputs to the model. In this work, 1280 samples were taken at different locations based on flood and non-flood characteristics; of these, 75% of the inventory dataset was used for training and 25% for testing. An artificial neural network was applied to develop a flood susceptibility model, and the results were plotted on a map using ArcGIS. According to the finding, 40.98% (i.e., 499433.50 hectors) of the study area is found within the very high-susceptibility zone, and 37.43% (i.e., 456168.76 hectors) are in the highly susceptible zone. Only 6.52% and 15% of the area were found in low and medium flood susceptibility zones, respectively. The results of model validation show that the overall prediction rate is around 89% and the overall model success rate is around 98%. The study's findings assist policymakers and concerned authorities in making flood risk management decisions in order to mitigate the negative impacts. |
| first_indexed | 2024-03-13T09:29:32Z |
| format | Article |
| id | doaj.art-c6132e9968094cd8a69867c2e9dc4889 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-13T09:29:32Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-c6132e9968094cd8a69867c2e9dc48892023-05-26T04:21:49ZengElsevierHeliyon2405-84402023-06-0196e16459Artificial neural network for flood susceptibility mapping in BangladeshRhyme Rubayet Rudra0Showmitra Kumar Sarkar1Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, BangladeshCorresponding author.; Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, 9203, BangladeshThe objective of the research is to investigate flood susceptibility in the Sylhet division of Bangladesh. Eight influential factors (i.e., elevation, slope, aspect, curvature, TWI, SPI, roughness, and LULC) were applied as inputs to the model. In this work, 1280 samples were taken at different locations based on flood and non-flood characteristics; of these, 75% of the inventory dataset was used for training and 25% for testing. An artificial neural network was applied to develop a flood susceptibility model, and the results were plotted on a map using ArcGIS. According to the finding, 40.98% (i.e., 499433.50 hectors) of the study area is found within the very high-susceptibility zone, and 37.43% (i.e., 456168.76 hectors) are in the highly susceptible zone. Only 6.52% and 15% of the area were found in low and medium flood susceptibility zones, respectively. The results of model validation show that the overall prediction rate is around 89% and the overall model success rate is around 98%. The study's findings assist policymakers and concerned authorities in making flood risk management decisions in order to mitigate the negative impacts.http://www.sciencedirect.com/science/article/pii/S2405844023036666FloodMachine learningGeographic information systemRemote sensingBangladesh |
| spellingShingle | Rhyme Rubayet Rudra Showmitra Kumar Sarkar Artificial neural network for flood susceptibility mapping in Bangladesh Heliyon Flood Machine learning Geographic information system Remote sensing Bangladesh |
| title | Artificial neural network for flood susceptibility mapping in Bangladesh |
| title_full | Artificial neural network for flood susceptibility mapping in Bangladesh |
| title_fullStr | Artificial neural network for flood susceptibility mapping in Bangladesh |
| title_full_unstemmed | Artificial neural network for flood susceptibility mapping in Bangladesh |
| title_short | Artificial neural network for flood susceptibility mapping in Bangladesh |
| title_sort | artificial neural network for flood susceptibility mapping in bangladesh |
| topic | Flood Machine learning Geographic information system Remote sensing Bangladesh |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023036666 |
| work_keys_str_mv | AT rhymerubayetrudra artificialneuralnetworkforfloodsusceptibilitymappinginbangladesh AT showmitrakumarsarkar artificialneuralnetworkforfloodsusceptibilitymappinginbangladesh |