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

• Published in: Theoretical and applied climatology Vol. 128; no. 1-2; pp. 255 - 273
• Main Authors: Pham, Binh Thai, Tien Bui, Dieu, Pourghasemi, Hamid Reza, Indra, Prakash, Dholakia, M. B.
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.04.2017; Springer Nature B.V
• Subjects: Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Climatology; data collection; Earth and Environmental Science; Earth Sciences; Geographic information systems; Hazards; India; inventories; land cover; land use; Land use planning; Landslides; Landslides & mudslides; Lithology; Neural networks; Original Paper; planning; prediction; rain; rivers; roads; Slope stability; soil; Spatial analysis; support vector machines; Training; trees; Waste Water Technology; Water Management; Water Pollution Control; ISW, India; India; Landslide Susceptibility; Landslide Occurrence; Landslide Susceptibility Assessment; Landslide Location; Functional Tree
• ISSN: 0177-798X; 1434-4483
• DOI: 10.1007/s00704-015-1702-9

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.