Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey
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DOI: 10.1007/s11069-017-3043-8
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- John List & Matti Liski, 2005. "Introduction," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 31(2), pages 121-121, June.
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Cited by:
- Yin Xing & Saipeng Huang & Jianping Yue & Yang Chen & Wei Xie & Peng Wang & Yunfei Xiang & Yiqun Peng, 2023. "Patterns of influence of different landslide boundaries and their spatial shapes on the uncertainty of landslide susceptibility prediction," 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(1), pages 709-727, August.
- Sara Beheshtifar, 2023. "Identification of landslide-prone zones using a GIS-based multi-criteria decision analysis and region-growing algorithm in uncertain conditions," 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. 115(2), pages 1475-1497, January.
- Yumiao Wang & Xueling Wu & Zhangjian Chen & Fu Ren & Luwei Feng & Qingyun Du, 2019. "Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China," IJERPH, MDPI, vol. 16(3), pages 1-27, January.
- Rui Yuan & Jing Chen, 2022. "A hybrid deep learning method for landslide susceptibility analysis with the application of InSAR data," 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. 114(2), pages 1393-1426, November.
- Quoc Dung Cao & Youngjun Choe, 2020. "Building damage annotation on post-hurricane satellite imagery based on convolutional neural networks," 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. 103(3), pages 3357-3376, September.
- Deborah Simon Mwakapesa & Yimin Mao & Xiaoji Lan & Yaser Ahangari Nanehkaran, 2023. "Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance," Sustainability, MDPI, vol. 15(5), pages 1-20, February.
- Vera Wendler-Bosco & Charles Nicholson, 2022. "Modeling the economic impact of incoming tropical cyclones using machine learning," 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. 110(1), pages 487-518, January.
- Halil Akinci & Mustafa Zeybek, 2021. "Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), 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. 108(2), pages 1515-1543, September.
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Keywords
Landslide susceptibility mapping; Machine learning; Two-level random sampling; Support vector machine; Random forest; Antalya;All these keywords.
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