remote sensing Article Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran Alireza Arabameri 1,* , Sunil Saha 2 , Jagabandhu Roy 2, Wei Chen 3,4,5 , Thomas Blaschke 6 and Dieu Tien Bui 7,* 1 Department of Geomorphology, Tarbiat Modares University, Tehran 14117-13116, Iran 2 Department of Geography, University of Gour Banga, Malda 732101, West Bengal, India;
[email protected] (S.S.);
[email protected] (J.R.) 3 College of Geology & Environment, Xi’an University of Science and Technology, Xi’an 710054, China;
[email protected] 4 Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Land and Resources, Xi’an 710021, China 5 Shaanxi Provincial Key Laboratory of Geological Support for Coal Green Exploitation, Xi’an 710054, China 6 Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria;
[email protected] 7 Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam * Correspondence:
[email protected] (A.A.);
[email protected] (D.T.B.) Received: 19 December 2019; Accepted: 22 January 2020; Published: 3 February 2020 Abstract: This analysis aims to generate landslide susceptibility maps (LSMs) using various machine learning methods, namely random forest (RF), alternative decision tree (ADTree) and Fisher’s Linear Discriminant Function (FLDA). The results of the FLDA, RF and ADTree models were compared with regard to their applicability for creating an LSM of the Gallicash river watershed in the northern part of Iran close to the Caspian Sea. A landslide inventory map was created using GPS points obtained in a field analysis, high-resolution satellite images, topographic maps and historical records.