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Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey

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Abstract

The aim of this study is to make a comparison of the performances of two machine-learning algorithms that support vector machine (SVM) and random forest (RF) for landslide susceptibility mapping. The study makes use of a sampling strategy called two-level random sampling (2LRS). During landslide susceptibility mapping, training and testing samples must be collected from different landslide seed cells, which are then put through a fully independent sampling using the 2LRS algorithm. This approach requires fewer samples for the improvement of the computation time of both machine-learning classifications. The proposed approach was tested in the Alakir catchment area (Western Antalya, Turkey) which features numerous active deep-seated rotational landslides. In order to compare the performance of the machine-learning algorithms, three random sets were generated for SVM and three random sets generated for 10, 100, 1000 and 10,000-tree size RF. A total of 15 models were generated for comparison, and their spatial performances were performed by the area under the receiver-operating characteristic curves, which ranged between 0.82 and 0.87. The highest and lowest performances were recorded from two models in SVM and two models from the 1000-tree and 10,000-tree sized RF, respectively. These results were confirmed the landslide happened just after producing the susceptibility maps in the field.

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Acknowledgements

This study is part of a scientific research project supported by the Scientific Research Projects Coordination Unit of Akdeniz University (Project No. FYL-2016-1732). The authors would like to thank Assoc. Prof. Dr. Hakan A. Nefeslioglu (Akdeniz University) and Murat GULER (Turkish State Meteorological Service, Antalya Regional Directorate) for their valuable comments. Finally the authors would like to thank three anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.

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  1. ISLEM GIS, 2024. Street No:14, 06800, Beysukent, Ankara, Turkey

    Metehan Ada

  2. Department of Geological Engineering, Engineering Faculty, Akdeniz University, Dumlupinar Blv, 07058, Konyaalti, Antalya, Turkey

    B. Taner San

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Ada, M., San, B.T. Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey. Nat Hazards 90, 237–263 (2018). https://doi.org/10.1007/s11069-017-3043-8

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