Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations
Author:
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology
Link
https://link.springer.com/content/pdf/10.1007/s11069-022-05793-y.pdf
Reference66 articles.
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3. Alganci U, Sertel E, Kaya S (2019) Determination of the flooded agricultural lands with spot 6 high resolution satellite images: A case study of Menderes plain, Turkey, 2019 8th International Conference on agro-geoinformatics (Agro-Geoinformatics), pp. 1–4 https://doi.org/10.1109/Agro-Geoinformatics.2019.8820242.
4. Arabameri A, Seyed Danesh A, Santosh M et al (2022) Flood susceptibility mapping using meta-heuristic algorithms. Geomat Nat Haz Risk 13:949–974. https://doi.org/10.1080/19475705.2022.2060138
5. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
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