Robustness of machine learning algorithms to generate flood susceptibility maps for watersheds in Jordan
The study examined three machine learning algorithms (MLAs): random forest (RF), support vector machine (SVM), and artificial neural networks (ANN) for generating flood susceptibility maps in two watersheds in Jordan. Both were selected because they represent two climatic regimes: desert and mountai...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2024.2378991 |
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| author | Mohanned S. Al-Sheriadeh Mohammad A. Daqdouq |
| author_facet | Mohanned S. Al-Sheriadeh Mohammad A. Daqdouq |
| author_sort | Mohanned S. Al-Sheriadeh |
| collection | DOAJ |
| description | The study examined three machine learning algorithms (MLAs): random forest (RF), support vector machine (SVM), and artificial neural networks (ANN) for generating flood susceptibility maps in two watersheds in Jordan. Both were selected because they represent two climatic regimes: desert and mountainous areas. Because of a shortage of past floods location, a physical model was utilized to generate them based on simulations of 100-year rainfall. 10,000 of them were selected randomly and used for MLAs training and testing. During training, thirteen flood influential factors were identified. Out of them, the distance to stream, elevation, and topographic wetness index have shown an overwhelming effect in Zarqa Ma’in watershed (they gained 50% of IGR), while the distance to stream, stream density, and elevation have an overwhelming effect in Al-Buaida watershed (they gained 44% of IGR). For flood susceptibility mapping, RF outperformed the other two algorithms for both watersheds and was thus selected for susceptibility mapping. The maps were classified into five classes, and 11% of Al-Buaida watershed fell into high to very high classes, while 5.2% of Zarqa Ma’in watershed fell within these classes. In conclusion, MLAs were able to produce susceptibility maps efficiently, and they can form an alternative to physical modeling. |
| format | Article |
| id | doaj-art-93089e96689b4b16b958c71b2566de99 |
| institution | Kabale University |
| issn | 1947-5705 1947-5713 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj-art-93089e96689b4b16b958c71b2566de992024-12-12T18:11:17ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132024-12-0115110.1080/19475705.2024.2378991Robustness of machine learning algorithms to generate flood susceptibility maps for watersheds in JordanMohanned S. Al-Sheriadeh0Mohammad A. Daqdouq1Department of Civil Engineering, Jordan University of Science and Technology, Irbid, JordanDepartment of Civil Engineering, Jordan University of Science and Technology, Irbid, JordanThe study examined three machine learning algorithms (MLAs): random forest (RF), support vector machine (SVM), and artificial neural networks (ANN) for generating flood susceptibility maps in two watersheds in Jordan. Both were selected because they represent two climatic regimes: desert and mountainous areas. Because of a shortage of past floods location, a physical model was utilized to generate them based on simulations of 100-year rainfall. 10,000 of them were selected randomly and used for MLAs training and testing. During training, thirteen flood influential factors were identified. Out of them, the distance to stream, elevation, and topographic wetness index have shown an overwhelming effect in Zarqa Ma’in watershed (they gained 50% of IGR), while the distance to stream, stream density, and elevation have an overwhelming effect in Al-Buaida watershed (they gained 44% of IGR). For flood susceptibility mapping, RF outperformed the other two algorithms for both watersheds and was thus selected for susceptibility mapping. The maps were classified into five classes, and 11% of Al-Buaida watershed fell into high to very high classes, while 5.2% of Zarqa Ma’in watershed fell within these classes. In conclusion, MLAs were able to produce susceptibility maps efficiently, and they can form an alternative to physical modeling.https://www.tandfonline.com/doi/10.1080/19475705.2024.2378991Machine learning algorithmsflood susceptibilityrandom forestartificial neural networkssupport vector machine |
| spellingShingle | Mohanned S. Al-Sheriadeh Mohammad A. Daqdouq Robustness of machine learning algorithms to generate flood susceptibility maps for watersheds in Jordan Geomatics, Natural Hazards & Risk Machine learning algorithms flood susceptibility random forest artificial neural networks support vector machine |
| title | Robustness of machine learning algorithms to generate flood susceptibility maps for watersheds in Jordan |
| title_full | Robustness of machine learning algorithms to generate flood susceptibility maps for watersheds in Jordan |
| title_fullStr | Robustness of machine learning algorithms to generate flood susceptibility maps for watersheds in Jordan |
| title_full_unstemmed | Robustness of machine learning algorithms to generate flood susceptibility maps for watersheds in Jordan |
| title_short | Robustness of machine learning algorithms to generate flood susceptibility maps for watersheds in Jordan |
| title_sort | robustness of machine learning algorithms to generate flood susceptibility maps for watersheds in jordan |
| topic | Machine learning algorithms flood susceptibility random forest artificial neural networks support vector machine |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2024.2378991 |
| work_keys_str_mv | AT mohannedsalsheriadeh robustnessofmachinelearningalgorithmstogeneratefloodsusceptibilitymapsforwatershedsinjordan AT mohammadadaqdouq robustnessofmachinelearningalgorithmstogeneratefloodsusceptibilitymapsforwatershedsinjordan |