সংরক্ষণ করুন:
| প্রধান লেখক: | , , , , |
|---|---|
| বিন্যাস: | Recurso digital |
| ভাষা: | ইংরেজি |
| প্রকাশিত: |
Zenodo
2026
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | https://doi.org/10.66050/xfxaaq83 |
| ট্যাগগুলো: |
ট্যাগ যুক্ত করুন
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সূচিপত্রের সারণি:
- <p>The flood risk in the Niger-East region of Niger State is increasingly becoming an annual event. Climatic<br>shifts, land-surface modifications, and human socioeconomic factors are among the conditions that trigger<br>floods. This study explores geospatial technology and multicriteria decision analysis-analytical hierarchy pro-<br>cess (MCDA-AHP) to develop a flood risk prediction system that leverages Google Earth Engine to process<br>remote sensing data directly influencing flood risk. Elevation, slope, drainage density, rainfall, soil, proximity<br>to drainage, proximity to road, population density, flow accumulation, and land use land cover (LULC). The<br>weightage assignment was performed using the MCDA-AHP technique. Flood risk classes predicted as very<br>low, 13.82 km2 (9.29%), low, 18.77 km2 (12.61%), low – moderate, 111.97 km2 (75.24%), high, 3.32 km2 (2.23%),<br>and very high, 0.93 km2 (0.63%) of the study area, respectively. This research presents a flood emergency re-<br>sponse system that highlights the impact of different prioritization criteria across multiple conditions. There-<br>fore, integrating GEE to generate different flood-conditioning risk indicators, prioritized and ranked using<br>MCDA-AHP, is crucial for developing an efficient methodological framework for flood risk prediction across<br>a wide region, achieving 88% precision. Thus, effective for evidence-based decision-making by authorities,<br>policy makers, and emergency response agencies</p>