Classification machine learning models for urban flood hazard mapping: case study of Zaio, NE Morocco
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11069-024-06596-z.pdf
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2. Aden-Antoniów F, Frank WB, Seydoux L (2022) An adaptable random forest model for the declustering of earthquake catalogs. J Geophys Res: Solid Earth 127(2):e2021JB023254. https://doi.org/10.1029/2021JB023254
3. Youssef AM, Biswajeet Pradhan AD, Mahdi AM (2022) Comparative study of convolutional neural network (CNN) and support vector machine (SVM) for flood susceptibility mapping: a case study at Ras Gharib, Red Sea, Egypt. Geocarto Int 37(26):11088–11115. https://doi.org/10.1080/10106049.2022.2046866
4. Akay H (2021) Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods. Soft Comput 25(14):9325–9346. https://doi.org/10.1007/s00500-021-05903-1
5. Al-Aizari AR, Al-Masnay YA, Aydda A, Zhang J, Ullah K, Islam ARMT, Habib T, Kaku DU, Nizeyimana JC, Al-Shaibah B, Khalil YM, AL-Hameedi WMM, Liu X (2022) Assessment analysis of flood susceptibility in tropical desert area: a case study of Yemen. Remote Sens 14(16):4050. https://doi.org/10.3390/rs14164050
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1. A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping;Water Resources Management;2024-08-03
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