Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey
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DOI: 10.1007/s11069-021-04743-4
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- Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
- Dieu Bui & Owe Lofman & Inge Revhaug & Oystein Dick, 2011. "Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 59(3), pages 1413-1444, December.
- Hamid Reza Pourghasemi & Amiya Gayen & Sungjae Park & Chang-Wook Lee & Saro Lee, 2018. "Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms," Sustainability, MDPI, vol. 10(10), pages 1-23, October.
- Chang-Jo Chung & Andrea Fabbri, 2003. "Validation of Spatial Prediction Models for Landslide Hazard Mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 30(3), pages 451-472, November.
- Dieu Tien Bui & Biswajeet Pradhan & Owe Lofman & Inge Revhaug, 2012. "Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-26, July.
- Metehan Ada & B. Taner San, 2018. "Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(1), pages 237-263, January.
- Qianqian Wang & Dongchuan Wang & Yong Huang & Zhiheng Wang & Lihui Zhang & Qiaozhen Guo & Wei Chen & Wengang Chen & Mengqin Sang, 2015. "Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment," Sustainability, MDPI, vol. 7(12), pages 1-17, December.
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- Txomin Bornaetxea & Juan Remondo & Jaime Bonachea & Pablo Valenzuela, 2023. "Exploring available landslide inventories for susceptibility analysis in Gipuzkoa province (Spain)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(3), pages 2513-2542, September.
- Qiang Liu & Aiping Tang & Ziyuan Huang & Lixin Sun & Xiaosheng Han, 2022. "Discussion on the tree-based machine learning model in the study of landslide susceptibility," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(2), pages 887-911, September.
- Aihua Wei & Kaining Yu & Fenggang Dai & Fuji Gu & Wanxi Zhang & Yu Liu, 2022. "Application of Tree-Based Ensemble Models to Landslide Susceptibility Mapping: A Comparative Study," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
- Ayse Yavuz Ozalp & Halil Akinci, 2023. "Evaluation of Land Suitability for Olive ( Olea europaea L.) Cultivation Using the Random Forest Algorithm," Agriculture, MDPI, vol. 13(6), pages 1-22, June.
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Keywords
GIS; Landslide susceptibility assessment; Logistic regression; Support vector machine; Random forest;All these keywords.
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