Investigation of land-subsidence phenomenon and aquifer vulnerability using machine models and GIS technique
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DOI: 10.1007/s11069-023-06058-y
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- Omid Ghorbanzadeh & Hashem Rostamzadeh & Thomas Blaschke & Khalil Gholaminia & Jagannath Aryal, 2018. "A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility 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. 94(2), pages 497-517, November.
- Yishao Shi & Donghui Shi & Xiangyang Cao, 2018. "Impacting Factors and Temporal and Spatial Differentiation of Land Subsidence in Shanghai," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
- T. Fernández & C. Irigaray & R. El Hamdouni & J. Chacón, 2003. "Methodology for Landslide Susceptibility Mapping by Means of a GIS. Application to the Contraviesa Area (Granada, 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. 30(3), pages 297-308, November.
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- Elham Hosseinzadeh & Sara Anamaghi & Massoud Behboudian & Zahra Kalantari, 2024. "Evaluating Machine Learning-Based Approaches in Land Subsidence Susceptibility Mapping," Land, MDPI, vol. 13(3), pages 1-27, March.
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Keywords
Land-subsidence; Machine learning; Random forest; Support vector machine; PLS; GRACE satellite;All these keywords.
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