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A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area

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Abstract

The objective of this study is to explore and compare the least square support vector machine (LSSVM) and multiclass alternating decision tree (MADT) techniques for the spatial prediction of landslides. The Luc Yen district in Yen Bai province (Vietnam) has been selected as a case study. LSSVM and MADT are effective machine learning techniques of classification applied in other fields but not in the field of landslide hazard assessment. For this, Landslide inventory map was first constructed with 95 landslide locations identified from aerial photos and verified from field investigations. These landslide locations were then divided randomly into two parts for training (70 % locations) and validation (30 % locations) processes. Secondly, landslide affecting factors such as slope, aspect, elevation, curvature, lithology, land use, distance to roads, distance to faults, distance to rivers, and rainfall were selected and applied for landslide susceptibility assessment. Subsequently, the LSSVM and MADT models were built to assess the landslide susceptibility in the study area using training dataset. Finally, receiver operating characteristic curve and statistical index-based evaluations techniques were employed to validate the predictive capability of these models. As a result, both the LSSVM and MADT models have high performance for spatial prediction of landslides in the study area. Out of these, the MADT model (AUC = 0.853) outperforms the LSSVM model (AUC = 0.803). From the landslide study of Luc Yen district in Yen Bai province (Vietnam), it can be conclude that the LSSVM and MADT models can be applied in other areas of world also for and spatial prediction. Landslide susceptibility maps obtained from this study may be helpful in planning, decision making for natural hazard management of the areas susceptible to landslide hazards.

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Acknowledgments

Authors would like to sincerely thank to the Vietnam Institute of Geosciences and Mineral Resources for sharing the data to implement this study and to Dr. Le Quoc Hung, Manager, VIGMR’s Stated-Funded Landslide Project Vietnam Institute Geosciences and Mineral Resources (VIGMR) for his encouragements. Authors are also thankful to Director, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Department of Science & Technology, Government of Gujarat, Gandhinagar, Gujarat, India for providing facilities to carry out this research work.

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Authors and Affiliations

  1. Department of Civil Engineering, Gujarat Technological University, Nr.Visat Three Roads, Visat - Gandhinagar Highway, Chandkheda, Ahmadabad, Gujarat, 382424, India

    Binh Thai Pham

  2. Department of Geotechnical Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, Vietnam

    Binh Thai Pham

  3. Geographic Information System Group, Department of Business Administration and Computer Science, Telemark University College, Hallvard Eikas Plass 1, 3800, Bø i Telemark, Norway

    Dieu Tien Bui

  4. Department of Civil Engineering, LDCE, Gujarat Technological University, Ahmadabad, 380015, Gujarat, India

    M. B. Dholakia

  5. Department of Science and Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar, India

    Indra Prakash

  6. Vietnam Institute of Geosciences and Mineral Resources, Thanh Xuan, Hanoi, Vietnam

    Ha Viet Pham

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Correspondence to Binh Thai Pham.

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Pham, B.T., Tien Bui, D., Dholakia, M.B. et al. A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area. Geotech Geol Eng 34, 1807–1824 (2016). https://doi.org/10.1007/s10706-016-9990-0

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