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A comparison study on the quantitative statistical methods for spatial prediction of shallow landslides (case study: Yozidar-Degaga Route in Kurdistan Province, Iran)

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Abstract

The main purpose of this study was to compare the performance of Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), and Bayesian Logistic Regression (BLR) algorithms for landslide susceptibility modeling in the Yozidar-Degaga region, Iran. Initially, a distribution map with 175 landslides and 175 non-landslide locations was prepared and the data were classified into a ratio of 80% and 20% for training and model validation, respectively. Based on Information Gain Ratio (IGR) technique, 13 derived factors from topographic data, land cover and rainfall were selected for modeling. Then, the SVM, SGD, and BLR algorithms were selected based on size of the data and required accuracy of the output, to learn and prepare landslide susceptibility maps. Statistical criteria were employed to evaluate the models for both training and validation datasets. Finally, the performance of these models was evaluated by the area under the receiver operating curve (AUC). The results showed that SVM algorithm (AUC = 0.920) performed better than SGD (AUC = 0.918) and BLR (AUC = 0.918) algorithms. Therefore, the SVM model can be suggested as a useful tool for better management of landslide-affected areas in the study area. In this study, all three models (SVM, SGD and BLR) were implemented in WEKA 3.6.9 software environment to prepare landslide susceptibility maps.

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Acknowledgements

The authors would like to thank the University of Kurdistan, Sanandaj, Iran for supplying required data, reports, useful maps, and their nationwide geodatabase for the first author (Mitra Asadi) as Ph.D. student during her passing internal sabbatical under the guidance of Dr. Himan Shahabi in this university. The authors also greatly appreciate the assistance of anonymous reviewers for their constructive comments that helped us to improve the paper.

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  1. Department of Climatology and Geomorphology, Faculty of Geography and Environmental Science, Hakim Sabzevari University, 9617976487, Sabzevar, Iran

    Mitra Asadi & Leila Goli Mokhtari

  2. Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran

    Ataollah Shirzadi

  3. Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran

    Himan Shahabi

  4. Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj, Iran

    Himan Shahabi

  5. Department of Physical Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran

    Shahram Bahrami

Authors
  1. Mitra Asadi
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  2. Leila Goli Mokhtari
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  3. Ataollah Shirzadi
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  4. Himan Shahabi
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  5. Shahram Bahrami
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Correspondence to Leila Goli Mokhtari.

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Asadi, M., Goli Mokhtari, L., Shirzadi, A. et al. A comparison study on the quantitative statistical methods for spatial prediction of shallow landslides (case study: Yozidar-Degaga Route in Kurdistan Province, Iran). Environ Earth Sci 81, 51 (2022). https://doi.org/10.1007/s12665-021-10152-4

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  • DOI: https://doi.org/10.1007/s12665-021-10152-4

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