Flood susceptibility mapping in the Yom River Basin, Thailand: stacking ensemble learning using multi-year flood inventory data
Accurate assessment models of flood susceptibility are crucial for informing risk management strategies related to severe threats posed by floods. This study assessed flood susceptibility in the Yom River Basin, Thailand, using conventional and ML methods. SE was compared with KNN, SVM, DT, RF, and...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Geocarto International |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2025.2461531 |
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| author | Gen Long Sarintip Tantanee Korakod Nusit Pitikhate Sooraksa |
| author_facet | Gen Long Sarintip Tantanee Korakod Nusit Pitikhate Sooraksa |
| author_sort | Gen Long |
| collection | DOAJ |
| description | Accurate assessment models of flood susceptibility are crucial for informing risk management strategies related to severe threats posed by floods. This study assessed flood susceptibility in the Yom River Basin, Thailand, using conventional and ML methods. SE was compared with KNN, SVM, DT, RF, and a Stacking ensemble model (SVM, DT, RF). A point-based flood inventory was sampled from multi-year flood polygons using a method considering flood frequency and inundation size. Results showed all ML models, except KNN, outperformed SE. RF achieved AUCs of 96.0% (test) and 96.1% (verification), while Stacking achieved 99.9% (test) and 96.1% (verification). Stacking also outperformed in accuracy (0.982, 0.893), precision (0.974, 0.915), F1 (0.990, 0.866), sensitivity (0.982, 0.890), specificity (0.974, 0.920), and kappa (0.964, 0.786). These findings highlight the potential of using ensemble ML techniques to significantly improve flood susceptibility mapping and risk management in data-limited regions such as the Yom River Basin. |
| format | Article |
| id | doaj-art-0fc611a0edb44765a2d19679fb2341a7 |
| institution | OA Journals |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-0fc611a0edb44765a2d19679fb2341a72025-08-20T01:56:42ZengTaylor & Francis GroupGeocarto International1010-60491752-07622025-12-0140110.1080/10106049.2025.2461531Flood susceptibility mapping in the Yom River Basin, Thailand: stacking ensemble learning using multi-year flood inventory dataGen Long0Sarintip Tantanee1Korakod Nusit2Pitikhate Sooraksa3Civil of Engineering Department, Faculty of Engineering, Naresuan University, Phitsanulok, ThailandCenter of Excellence on Energy Technology and Environment, Faculty of Engineering, Naresuan University, Phitsanulok, ThailandCivil of Engineering Department, Faculty of Engineering, Naresuan University, Phitsanulok, ThailandDepartment of Robotics and AI, School of Engineering, King Mongkut’s Institute of Technology, Ladkrabang, Bangkok, ThailandAccurate assessment models of flood susceptibility are crucial for informing risk management strategies related to severe threats posed by floods. This study assessed flood susceptibility in the Yom River Basin, Thailand, using conventional and ML methods. SE was compared with KNN, SVM, DT, RF, and a Stacking ensemble model (SVM, DT, RF). A point-based flood inventory was sampled from multi-year flood polygons using a method considering flood frequency and inundation size. Results showed all ML models, except KNN, outperformed SE. RF achieved AUCs of 96.0% (test) and 96.1% (verification), while Stacking achieved 99.9% (test) and 96.1% (verification). Stacking also outperformed in accuracy (0.982, 0.893), precision (0.974, 0.915), F1 (0.990, 0.866), sensitivity (0.982, 0.890), specificity (0.974, 0.920), and kappa (0.964, 0.786). These findings highlight the potential of using ensemble ML techniques to significantly improve flood susceptibility mapping and risk management in data-limited regions such as the Yom River Basin.https://www.tandfonline.com/doi/10.1080/10106049.2025.2461531Flood susceptibilityflood hazardflood riskmachine learningensemble machine learninghybrid modelling |
| spellingShingle | Gen Long Sarintip Tantanee Korakod Nusit Pitikhate Sooraksa Flood susceptibility mapping in the Yom River Basin, Thailand: stacking ensemble learning using multi-year flood inventory data Geocarto International Flood susceptibility flood hazard flood risk machine learning ensemble machine learning hybrid modelling |
| title | Flood susceptibility mapping in the Yom River Basin, Thailand: stacking ensemble learning using multi-year flood inventory data |
| title_full | Flood susceptibility mapping in the Yom River Basin, Thailand: stacking ensemble learning using multi-year flood inventory data |
| title_fullStr | Flood susceptibility mapping in the Yom River Basin, Thailand: stacking ensemble learning using multi-year flood inventory data |
| title_full_unstemmed | Flood susceptibility mapping in the Yom River Basin, Thailand: stacking ensemble learning using multi-year flood inventory data |
| title_short | Flood susceptibility mapping in the Yom River Basin, Thailand: stacking ensemble learning using multi-year flood inventory data |
| title_sort | flood susceptibility mapping in the yom river basin thailand stacking ensemble learning using multi year flood inventory data |
| topic | Flood susceptibility flood hazard flood risk machine learning ensemble machine learning hybrid modelling |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2025.2461531 |
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