Data-driven topo-climatic mapping with machine learning methods
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DOI: 10.1007/s11069-008-9339-y
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Cited by:
- Jiazheng Lu & Yu Liu & Guoyong Zhang & Bo Li & Lifu He & Jing Luo, 2018. "Partition dynamic threshold monitoring technology of wildfires near overhead transmission lines by satellite," 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. 94(3), pages 1327-1340, December.
- Ling Tan & Ji Guo & Selvarajah Mohanarajah & Kun Zhou, 2021. "Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices," 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. 107(3), pages 2389-2417, July.
- Jian Zhou & Xibing Li & Hani Mitri, 2015. "Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability 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. 79(1), pages 291-316, October.
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Keywords
Machine learning; Support vector machines; Topo-climatic mapping; Feature selection; Environmental modelling; Downscaling; Decision support systems; Natural hazards;All these keywords.
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