Multiple data-driven approach for predicting landslide deformation
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Geotechnical Engineering and Engineering Geology
Link
http://link.springer.com/content/pdf/10.1007/s10346-019-01320-6.pdf
Reference45 articles.
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3. Chen W, Pourghasemi HR, Kornejady A, Zhang N (2017) Landslide spatial modeling: introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma 305:314–327
4. Corsini A, Mulas M (2017) Use of ROC curves for early warning of landslide displacement rates in response to precipitation (Piagneto landslide, Northern Apennines, Italy). Landslides 14(3):1241–1252
5. Damoulas T, Girolami MA (2008) Probabilistic multi-class multi-kernel learning: on protein fold recognition and remote homology detection. Bioinformatics 24(10):1264–1270
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