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Article

Performance Evaluation of GIS-Based Artificial Intelligence Approaches for Landslide Susceptibility Modeling and Spatial Patterns Analysis

1
College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
2
Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi’an 710021, China
3
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(7), 443; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070443
Received: 10 June 2020 / Revised: 8 July 2020 / Accepted: 15 July 2020 / Published: 17 July 2020
(This article belongs to the Special Issue The Use of GIS and Soft Computing Methods in Water Resource Planning)
The main purpose of this study was to apply the novel bivariate weights-of-evidence-based SysFor (SF) for landslide susceptibility mapping, and two machine learning techniques, namely the naïve Bayes (NB) and Radial basis function networks (RBFNetwork), as benchmark models. Firstly, by using aerial photos and geological field surveys, the 263 landslide locations in the study area were obtained. Next, the identified landslides were randomly classified according to the ratio of 70/30 to construct training data and validation models, respectively. Secondly, based on the landslide inventory map, combined with the geological and geomorphological characteristics of the study area, 14 affecting factors of the landslide were determined. The predictive ability of the selected factors was evaluated using the LSVM model. Using the WoE model, the relationship between landslides and affecting factors was analyzed by positive and negative correlation methods. The above three hybrid models were then used to map landslide susceptibility. Thirdly, the ROC curve and various statistical data (SE, 95% CI and MAE) were used to verify and compare the predictive power of the model. Compared with the other two models, the Sysfor model had a larger area under the curve (AUC) of 0.876 (training dataset) and 0.783 (validation dataset). Finally, by quantitatively comparing the susceptibility values of each pixel, the differences in spatial morphology of landslide susceptibility maps were compared, and the model was found to have limitations and effectiveness. The landslide susceptibility maps obtained by the three models are reasonable, and the landslide susceptibility maps generated by the SysFor model have the highest comprehensive performance. The results obtained in this paper can help local governments in land use planning, disaster reduction and environmental protection. View Full-Text
Keywords: landslide susceptibility; weights of evidence; radial basis function network; naïve Bayes; SysFor landslide susceptibility; weights of evidence; radial basis function network; naïve Bayes; SysFor
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MDPI and ACS Style

Lei, X.; Chen, W.; Pham, B.T. Performance Evaluation of GIS-Based Artificial Intelligence Approaches for Landslide Susceptibility Modeling and Spatial Patterns Analysis. ISPRS Int. J. Geo-Inf. 2020, 9, 443. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070443

AMA Style

Lei X, Chen W, Pham BT. Performance Evaluation of GIS-Based Artificial Intelligence Approaches for Landslide Susceptibility Modeling and Spatial Patterns Analysis. ISPRS International Journal of Geo-Information. 2020; 9(7):443. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070443

Chicago/Turabian Style

Lei, Xinxiang, Wei Chen, and Binh T. Pham 2020. "Performance Evaluation of GIS-Based Artificial Intelligence Approaches for Landslide Susceptibility Modeling and Spatial Patterns Analysis" ISPRS International Journal of Geo-Information 9, no. 7: 443. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070443

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