Development of an automated method for flood inundation monitoring, flood hazard, and soil erosion susceptibility assessment using machine learning and AHP–MCE techniques
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| Publicado en: | Geoenvironmental Disasters vol. 11, no. 1 (Dec 2024), p. 14 |
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| Outros autores: | , , , |
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Springer Nature B.V.
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| Acceso en liña: | Citation/Abstract Full Text - PDF |
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MARC
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| 001 | 2986800337 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2197-8670 | ||
| 024 | 7 | |a 10.1186/s40677-024-00275-8 |2 doi | |
| 035 | |a 2986800337 | ||
| 045 | 2 | |b d20241201 |b d20241231 | |
| 100 | 1 | |a Prakash, A. Jaya |u Indian Institute of Technology Kharagpur, Centre for Ocean, River, Atmosphere and Land Sciences, Kharagpur, India (GRID:grid.429017.9) (ISNI:0000 0001 0153 2859) | |
| 245 | 1 | |a Development of an automated method for flood inundation monitoring, flood hazard, and soil erosion susceptibility assessment using machine learning and AHP–MCE techniques | |
| 260 | |b Springer Nature B.V. |c Dec 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a BackgroundOperational large-scale flood monitoring using publicly available satellite data is possible with the advent of Sentinel-1 microwave data, which enables near-real-time (at 6-day intervals) flood mapping day and night, even in cloudy monsoon seasons. Automated flood inundation area identification in near-real-time involves advanced geospatial data processing platforms, such as Google Earth Engine and robust methodology (Otsu’s algorithm).ObjectivesThe current study employs Sentinel-1 microwave data for flood extent mapping using machine learning (ML) algorithms in Assam State, India. We generated a flood hazard and soil erosion susceptibility map by combining multi-source data on weather conditions and soil and terrain characteristics. Random Forest (RF), Classification and Regression Tool (CART), and Support Vector Machine (SVM) ML algorithms were applied to generate the flood hazard map. Furthermore, we employed the multicriteria evaluation (MCE) analytical hierarchical process (AHP) for soil erosion susceptibility mapping.SummaryThe highest prediction accuracy was observed for the RF model (overall accuracy [OA] > 82%), followed by the SVM (OA > 82%) and CART (OA > 81%). Over 26% of the study area indicated high flood hazard-prone areas, and approximately 60% showed high and severe potential for soil erosion due to flooding. The automated flood mapping platform is an essential resource for emergency responders and decision-makers, as it helps to guide relief activities by identifying suitable regions and appropriate logistic route planning and improving the accuracy and timeliness of emergency response efforts. Periodic flood inundation maps will help in long-term planning and policymaking, flood management, soil and biodiversity conservation, land degradation, planning sustainable agriculture interventions, crop insurance, and climate resilience studies. | |
| 653 | |a Soil erosion | ||
| 653 | |a Multiple criterion | ||
| 653 | |a Land conservation | ||
| 653 | |a Biodiversity | ||
| 653 | |a Machine learning | ||
| 653 | |a Decision trees | ||
| 653 | |a Mapping | ||
| 653 | |a Spatial data | ||
| 653 | |a Accuracy | ||
| 653 | |a Soil conservation | ||
| 653 | |a Wildlife conservation | ||
| 653 | |a Route planning | ||
| 653 | |a Climate change adaptation | ||
| 653 | |a Algorithms | ||
| 653 | |a Real time | ||
| 653 | |a Flood management | ||
| 653 | |a Analytic hierarchy process | ||
| 653 | |a Data processing | ||
| 653 | |a Long-term planning | ||
| 653 | |a Maps | ||
| 653 | |a Floods | ||
| 653 | |a Flood mapping | ||
| 653 | |a Data analysis | ||
| 653 | |a Emergency response | ||
| 653 | |a Flooding | ||
| 653 | |a Automation | ||
| 653 | |a Monitoring | ||
| 653 | |a Emergencies | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Sustainable agriculture | ||
| 653 | |a Model accuracy | ||
| 653 | |a Support vector machines | ||
| 653 | |a Weather | ||
| 653 | |a Climate adaptation | ||
| 653 | |a Policy and planning | ||
| 653 | |a Flood hazards | ||
| 653 | |a Flood control | ||
| 653 | |a Emergency preparedness | ||
| 653 | |a Crop insurance | ||
| 653 | |a Hydrologic data | ||
| 653 | |a Land degradation | ||
| 653 | |a First responders | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Begam, Sazeda |u University of Nottingham, Department of Civil Engineering, Nottingham, UK (GRID:grid.4563.4) (ISNI:0000 0004 1936 8868) | |
| 700 | 1 | |a Vilímek, Vít |u Charles University, Prague 2, Czech Republic (GRID:grid.4491.8) (ISNI:0000 0004 1937 116X) | |
| 700 | 1 | |a Mudi, Sujoy |u BeZero Carbon Ltd, London, UK (GRID:grid.4491.8) | |
| 700 | 1 | |a Das, Pulakesh |u University of Maine, School of Forest Resources, Orono, USA (GRID:grid.21106.34) (ISNI:0000 0001 2182 0794) | |
| 773 | 0 | |t Geoenvironmental Disasters |g vol. 11, no. 1 (Dec 2024), p. 14 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2986800337/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2986800337/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |