Application of Naive Bayes, kernel logistic regression and alternation decision tree for landslide susceptibility mapping in Pengyang County, China
Author:
Funder
National Natural Science Foundation of China
Natural Science Basic Research Program of Shaanxi Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11069-024-06672-4.pdf
Reference104 articles.
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2. Abdollahizad S, Balafar MA, Feizizadeh B, Sangar AB, Samadzamini K (2022) Stacking ensemble approach in data mining methods for landslide prediction. J Supercomput 79(8):8583–8610. https://doi.org/10.1007/s11227-022-05006-0
3. Adnan Ikram RM, Khan I, Moayedi H, Ahmadi Dehrashid A, Elkhrachy I, Le Nguyen B (2023) Novel evolutionary-optimized neural network for predicting landslide susceptibility. Environ Dev Sustain. https://doi.org/10.1007/s10668-023-03356-0
4. Althuwaynee OF, Pradhan B, Lee S (2016) A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison. Int J Remote Sens 37(5):1190–1209. https://doi.org/10.1080/01431161.2016.1148282
5. Amato G, Fiorucci M, Martino S, Lombardo L, Palombi L (2023) Earthquake-triggered landslide susceptibility in Italy by means of Artificial Neural Network. Bull Eng Geol Environ 82(5):160. https://doi.org/10.1007/s10064-023-03163-x
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