Improving the forecast performance of landslide susceptibility mapping by using ensemble gradient boosting algorithms
Author:
Funder
Bộ Giáo dục và Ðào tạo
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10668-024-04694-3.pdf
Reference84 articles.
1. Abuzied, S. M., & Alrefaee, H. A. (2019). Spatial prediction of landslide-susceptible zones in El-Qaá area, Egypt, using an integrated approach based on GIS statistical analysis. Bulletin of Engineering Geology and the Environment, 78, 2169–2195. https://doi.org/10.1007/s10064-018-1302-x
2. Achour, Y., & Pourghasemi, H. R. (2020). How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geoscience Frontiers, 11(3), 871–883. https://doi.org/10.1016/j.gsf.2019.10.001
3. Adnan, M. S. G., Dewan, A., Zannat, K. E., & Abdullah, A. Y. M. (2019). The use of watershed geomorphic data in flash flood susceptibility zoning: A case study of the Karnaphuli and Sangu river basins of Bangladesh. Natural Hazards, 99, 425–448. https://doi.org/10.1007/s11069-019-03749-3
4. Arabameri, A., Pradhan, B., Rezaei, K., Lee, S., & Sohrabi, M. (2020a). An ensemble model for landslide susceptibility mapping in a forested area. Geocarto International, 35(15), 1680–1705. https://doi.org/10.1080/10106049.2019.1585484
5. Arabameri, A., Saha, S., Roy, J., Chen, W., Blaschke, T., & Tien Bui, D. (2020b). Landslide susceptibility evaluation and management using different machine learning methods in the Gallicash River Watershed, Iran. Remote Sensing, 12(3), 475. https://doi.org/10.3390/rs12030475
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