Emami, Sayed Naeim, Yousefi, Saleh, Pourghasemi, Hamid Reza, Tavangar, Shahla, Santosh, M. (2020) A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran) Bulletin of Engineering Geology and the Environment, 79 (10) 5291-5308 doi:10.1007/s10064-020-01915-7

See Also

These are possibly similar items as determined by title/reference text matching only.

	Chen, Wei, Pourghasemi, Hamid Reza, Naghibi, Seyed Amir (2018) A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. Bulletin of Engineering Geology and the Environment, 77. 647-664 doi:10.1007/s10064-017-1010-y
	Yousefi, Saleh, Pourghasemi, Hamid Reza, Rahmati, Omid, Keesstra, Saskia, Emami, Sayed Naim, Hooke, Janet (2021) Geomorphological change detection of an urban meander loop caused by an extreme flood using remote sensing and bathymetry measurements (a case study of Karoon River, Iran) Journal of Hydrology, 597. 125712pp. doi:10.1016/j.jhydrol.2020.125712
	Youssef, Ahmed M., Mahdi, Ali M., Pourghasemi, Hamid Reza (2022) Landslides and flood multi-hazard assessment using machine learning techniques. Bulletin of Engineering Geology and the Environment, 81 (9) doi:10.1007/s10064-022-02874-x
	Yousefi, Saleh, Mirzaee, Somayeh, Keesstra, Saskia, Surian, Nicola, Pourghasemi, Hamid Reza, Zakizadeh, Hamid Reza, Tabibian, Sahar (2018) Effects of an extreme flood on river morphology (case study: Karoon River, Iran) Geomorphology, 304. 30-39 doi:10.1016/j.geomorph.2017.12.034
	Meena, Sansar Raj; Hussain, Muhammad Afaq; Ullah, Hafiz; Ullah, Ibad (2025) Landslide susceptibility mapping using hybrid machine learning classifiers: a case study of Neelum Valley, Pakistan. Bulletin of Engineering Geology and the Environment, 84 (5). doi:10.1007/s10064-025-04270-7
	Pourghasemi, Hamid Reza, Sadhasivam, Nitheshnirmal, Amiri, Mahdis, Eskandari, Saeedeh, Santosh, M. (2021) Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques. Natural Hazards, 108 (1) 1291-1316 doi:10.1007/s11069-021-04732-7
	Pourghasemi, Hamid Reza, Kariminejad, Narges, Amiri, Mahdis, Edalat, Mohsen, Zarafshar, Mehrdad, Blaschke, Thomas, Cerda, Artemio (2020) Assessing and mapping multi-hazard risk susceptibility using a machine learning technique. Scientific Reports, 10. doi:10.1038/s41598-020-60191-3
	Kaur, Ramandeep, Gupta, Vikram, Chaudhary, B. S. (2024) Landslide susceptibility mapping and sensitivity analysis using various machine learning models: a case study of Beas valley, Indian Himalaya. Bulletin of Engineering Geology and the Environment, 83 (6) doi:10.1007/s10064-024-03712-y
	Eskandari, Saeedeh, Amiri, Mahdis, Sãdhasivam, Nitheshnirmal, Pourghasemi, Hamid Reza (2020) Comparison of new individual and hybrid machine learning algorithms for modeling and mapping fire hazard: a supplementary analysis of fire hazard in different counties of Golestan Province in Iran. Natural Hazards, 104 (1) 305-327 doi:10.1007/s11069-020-04169-4
	Samanta, Ratan Kumar, Bhunia, Gouri Sankar, Shit, Pravat Kumar, Pourghasemi, Hamid Reza (2018) Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin, India. Modeling Earth Systems and Environment, 4. 395-408 doi:10.1007/s40808-018-0427-z
	Al-Fugara, A’kif, Pourghasemi, Hamid Reza, Al-Shabeeb, Abdel Rahman, Habib, Maan, Al-Adamat, Rida, Al-Amoush, Hani, Collins, Adrian L. (2020) A comparison of machine learning models for the mapping of groundwater spring potential. Environmental Earth Sciences, 79 (10) doi:10.1007/s12665-020-08944-1