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Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods

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Abstract

The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the prediction capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning.

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Acknowledgments

This research is funded by the ICCR scholarship program of Indian government to the first author for his Ph.D studies at the Department of Civil Engineering, Gujarat Technological University, Gujarat, India. 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, Ahmedabad, 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, University College of Southeast Norway, Hallvard Eikas Plass 1, N-3800, Bø i Telemark, Norway

    Dieu Tien Bui

  4. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

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

    Prakash Indra

  6. Department of Civil Engineering, LDCE, Gujarat Technological University, Ahmedabad, Gujarat, 380015, India

    M. B. Dholakia

Authors
  1. Binh Thai Pham
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  2. Dieu Tien Bui
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  3. Hamid Reza Pourghasemi
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  4. Prakash Indra
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  5. M. B. Dholakia
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Corresponding author

Correspondence to Binh Thai Pham.

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Pham, B.T., Tien Bui, D., Pourghasemi, H.R. et al. Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol 128, 255–273 (2017). https://doi.org/10.1007/s00704-015-1702-9

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  • DOI: https://doi.org/10.1007/s00704-015-1702-9

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