Improving generalization performance of landslide susceptibility model considering spatial heterogeneity by using the geomorphic label-based LightGBM
Author:
Funder
Key Technologies Research and Development Program
Natural Science Foundation Project of Chongqing, Chongqing Science and Technology Commission
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10064-024-03859-8.pdf
Reference60 articles.
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3. Binh Thai P, Pradhan B, Bui DT, Prakash I, Dholakia MB (2016) A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India). Environ Modell Softw 84:240–250. https://doi.org/10.1016/j.envsoft.2016.07.005
4. Canavesi V, Segoni S, Rosi A, Ting X, Nery T, Catani F, Casagli N (2020) Different Approaches to Use Morphometric Attributes in Landslide Susceptibility Mapping Based on Meso-Scale Spatial Units: A Case Study in Rio de Janeiro (Brazil). Remote Sens-Basel 12(11):1826. https://doi.org/10.3390/rs12111826
5. Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazards Earth Syst Sci 13(11):2815–2831. https://doi.org/10.5194/nhess-13-2815-2013
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