Sinkhole susceptibility mapping: a comparison between Bayes-based machine learning algorithms Kamal Taheri1, Himan Shahabi2, *, Kamran Chapi3, Ataollah Shirzadi3, Francisco Gutiérrez4, Khabat Khosravi5 1 Karst Research and Study Office of Western Iran, Kermanshah Regional Water Authority, Kermanshah, Iran 2Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran 3Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran 4Earth Science Department, Edificio Geológicas, Universidad de Zaragoza, Zaragoza, Spain 5 Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Mazandaran, Iran *Corresponding Author: Himan Shahabi, Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran, E-mail:
[email protected] Tel: +98- 9186658739 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/ldr.3255 This article is protected by copyright. All rights reserved. ABSTRACT Land degradation has been recognized as one of the most adverse environmental impacts during the last century. The occurrence of sinkholes is increasing dramatically in many regions worldwide contributing to land degradation. The rise in the sinkhole frequency is largely due to human-induced hydrological alterations that favour dissolution and subsidence processes. Mitigating detrimental impacts associated with sinkholes requires understanding different aspects of this phenomenon such as the controlling factors and the spatial distribution patterns. This research illustrates the development and validation of sinkhole susceptibility models in Hamadan Province, Iran, where a large number of sinkholes are occurring under poorly understood circumstances.