Flood mapping, damage assessment, and susceptibility zonation in northeastern Bangladesh in 2022 using geospatial datasets
This study assesses flood inundation, impacts, and susceptibility zones in northeastern Bangladesh during the 2022 flood. The region is highly vulnerable to recurrent flooding Due to its geographic position and climate change impacts. The Sentinel-1 SAR data on the Google Earth Engine (GEE) platform...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | Progress in Disaster Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590061724000929 |
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| author | Md. Mizanur Rahman Mohammad Kamruzzaman Limon Deb H.M. Touhidul Islam |
| author_facet | Md. Mizanur Rahman Mohammad Kamruzzaman Limon Deb H.M. Touhidul Islam |
| author_sort | Md. Mizanur Rahman |
| collection | DOAJ |
| description | This study assesses flood inundation, impacts, and susceptibility zones in northeastern Bangladesh during the 2022 flood. The region is highly vulnerable to recurrent flooding Due to its geographic position and climate change impacts. The Sentinel-1 SAR data on the Google Earth Engine (GEE) platform was used to generate flooded areas using a simple change detection technique with thresholding. This analysis was further supported by incorporating cropland, population, national highway, and DEM datasets for a comprehensive damage assessment. Findings show that 55.76 % (10,993.09 km2) of the area was inundated, impacting 10.69 million people and causing severe displacement and health hazards. Sylhet, Kishoreganj, and Brahmanbaria districts were the most affected, with 2.73 million impacted in Sylhet alone. Additionally, 67.87 % of agricultural land was flooded, particularly in Sunamganj, and 43.38 % of national highways (535.08 km2) were damaged. A flood susceptibility zonation map identified high-susceptibility areas like central Sunamganj and parts of Kishoreganj to assist authorities in resource allocation and mitigation. The flood extent model achieved strong predictive accuracy (AUC: 0.97 % RF, 0.96 % LR, and 0.94 % DT), providing crucial insights for regional flood management and guiding communities with limited modeling capacities. |
| format | Article |
| id | doaj-art-c2fc4bb9931f439bb0e5fd87b226ec38 |
| institution | DOAJ |
| issn | 2590-0617 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Progress in Disaster Science |
| spelling | doaj-art-c2fc4bb9931f439bb0e5fd87b226ec382025-08-20T02:55:04ZengElsevierProgress in Disaster Science2590-06172025-01-012510040210.1016/j.pdisas.2024.100402Flood mapping, damage assessment, and susceptibility zonation in northeastern Bangladesh in 2022 using geospatial datasetsMd. Mizanur Rahman0Mohammad Kamruzzaman1Limon Deb2H.M. Touhidul Islam3Farm Machinery and Postharvest Technology Division, Bangladesh Rice Research Institute, Gazipur 1701, Bangladesh; Corresponding author.Farm Machinery and Postharvest Technology Division, Bangladesh Rice Research Institute, Gazipur 1701, BangladeshAgricultural Economics Division, Bangladesh Rice Research Institute, Gazipur 1701, BangladeshDepartment of Disaster Management, Begum Rokeya University, Rangpur 5400, BangladeshThis study assesses flood inundation, impacts, and susceptibility zones in northeastern Bangladesh during the 2022 flood. The region is highly vulnerable to recurrent flooding Due to its geographic position and climate change impacts. The Sentinel-1 SAR data on the Google Earth Engine (GEE) platform was used to generate flooded areas using a simple change detection technique with thresholding. This analysis was further supported by incorporating cropland, population, national highway, and DEM datasets for a comprehensive damage assessment. Findings show that 55.76 % (10,993.09 km2) of the area was inundated, impacting 10.69 million people and causing severe displacement and health hazards. Sylhet, Kishoreganj, and Brahmanbaria districts were the most affected, with 2.73 million impacted in Sylhet alone. Additionally, 67.87 % of agricultural land was flooded, particularly in Sunamganj, and 43.38 % of national highways (535.08 km2) were damaged. A flood susceptibility zonation map identified high-susceptibility areas like central Sunamganj and parts of Kishoreganj to assist authorities in resource allocation and mitigation. The flood extent model achieved strong predictive accuracy (AUC: 0.97 % RF, 0.96 % LR, and 0.94 % DT), providing crucial insights for regional flood management and guiding communities with limited modeling capacities.http://www.sciencedirect.com/science/article/pii/S2590061724000929Sentinel-1 SARGEEFlood mapDamage assessmentMachine learningSusceptibility zonation |
| spellingShingle | Md. Mizanur Rahman Mohammad Kamruzzaman Limon Deb H.M. Touhidul Islam Flood mapping, damage assessment, and susceptibility zonation in northeastern Bangladesh in 2022 using geospatial datasets Progress in Disaster Science Sentinel-1 SAR GEE Flood map Damage assessment Machine learning Susceptibility zonation |
| title | Flood mapping, damage assessment, and susceptibility zonation in northeastern Bangladesh in 2022 using geospatial datasets |
| title_full | Flood mapping, damage assessment, and susceptibility zonation in northeastern Bangladesh in 2022 using geospatial datasets |
| title_fullStr | Flood mapping, damage assessment, and susceptibility zonation in northeastern Bangladesh in 2022 using geospatial datasets |
| title_full_unstemmed | Flood mapping, damage assessment, and susceptibility zonation in northeastern Bangladesh in 2022 using geospatial datasets |
| title_short | Flood mapping, damage assessment, and susceptibility zonation in northeastern Bangladesh in 2022 using geospatial datasets |
| title_sort | flood mapping damage assessment and susceptibility zonation in northeastern bangladesh in 2022 using geospatial datasets |
| topic | Sentinel-1 SAR GEE Flood map Damage assessment Machine learning Susceptibility zonation |
| url | http://www.sciencedirect.com/science/article/pii/S2590061724000929 |
| work_keys_str_mv | AT mdmizanurrahman floodmappingdamageassessmentandsusceptibilityzonationinnortheasternbangladeshin2022usinggeospatialdatasets AT mohammadkamruzzaman floodmappingdamageassessmentandsusceptibilityzonationinnortheasternbangladeshin2022usinggeospatialdatasets AT limondeb floodmappingdamageassessmentandsusceptibilityzonationinnortheasternbangladeshin2022usinggeospatialdatasets AT hmtouhidulislam floodmappingdamageassessmentandsusceptibilityzonationinnortheasternbangladeshin2022usinggeospatialdatasets |