A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11269-024-03940-7.pdf
Reference146 articles.
1. Abu-Salih B, Wongthongtham P, Coutinho K, Qaddoura R, Alshaweesh O, Wedyan M (2023) The development of a road network flood risk detection model using optimised ensemble learning. Eng Appl Artif Intell 122:106081. https://doi.org/10.1016/j.engappai.2023.106081
2. Adnan MSG, Siam ZS, Kabir I, Kabir Z, Ahmed MR, Hassan QK, Rahman RM, Dewan A (2023) A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction. J Environ Manage 326:116813. https://doi.org/10.1016/j.jenvman.2022.116813
3. Agonafir C, Lakhankar T, Khanbilvardi R, Krakauer N, Radell D, Devineni N (2023) A review of recent advances in urban flood research. Water Secur 19:100141. https://doi.org/10.1016/j.wasec.2023.100141
4. Al-Abadi AM, Al-Najar NA (2020) Comparative assessment of bivariate, multivariate and machine learning models for mapping flood proneness. Nat Hazards 100(2):461–491. https://doi.org/10.1007/s11069-019-03821-y
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, Hameedi AL, M. M. W, Liu X (2022) Assessment Analysis of Flood susceptibility in Tropical Desert Area: a case study of Yemen. Remote Sens 14(16). https://doi.org/10.3390/rs14164050
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