Comparison of optimized data-driven models for landslide susceptibility mapping
Author:
Publisher
Springer Science and Business Media LLC
Subject
Management, Monitoring, Policy and Law,Economics and Econometrics,Geography, Planning and Development
Link
https://link.springer.com/content/pdf/10.1007/s10668-023-03212-1.pdf
Reference87 articles.
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3. Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M., 2019. Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2623–2631.
4. Ali, S. A., Parvin, F., Vojteková, J., Costache, R., Linh, N. T. T., Pham, Q. B., Vojtek, M., Gigović, L., Ahmad, A., & Ghorbani, M. A. (2021). GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms. Geoscience Frontiers, 12, 857–876. https://doi.org/10.1016/j.gsf.2020.09.004
5. Alilou, H., Rahmati, O., Singh, V. P., Choubin, B., Pradhan, B., Keesstra, S., Ghiasi, S. S., & Sadeghi, S. H. (2019). Evaluation of watershed health using Fuzzy-ANP approach considering geo-environmental and topo-hydrological criteria. J Environ Manag, 232, 22–36. https://doi.org/10.1016/j.jenvman.2018.11.019
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