Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling
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School of Earth Science and Resources, Chang’an University, Key Laboratory of Degraded and Unutilized Land Remediation Engineering, Ministry of Land and Resources, Shaanxi Provincial Key Laboratory of Land Rehabilitation, Xi’an 710064, Shaanxi, China
2
College of Geology & Environment, Xi’an University of Science and Technology, Xi’an 710054, Shaanxi, China
3
Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(11), 884; https://doi.org/10.3390/e20110884
Received: 7 October 2018
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Revised: 7 November 2018
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Accepted: 7 November 2018
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Published: 17 November 2018
The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR–IOE, and the SVM–IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR–IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM–IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR–IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM–IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.
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Keywords:
landslides; hybrid model; statistical method; machine learning; loess area
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MDPI and ACS Style
Zhang, T.; Han, L.; Chen, W.; Shahabi, H. Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling. Entropy 2018, 20, 884. https://doi.org/10.3390/e20110884
AMA Style
Zhang T, Han L, Chen W, Shahabi H. Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling. Entropy. 2018; 20(11):884. https://doi.org/10.3390/e20110884
Chicago/Turabian StyleZhang, Tingyu, Ling Han, Wei Chen, and Himan Shahabi. 2018. "Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling" Entropy 20, no. 11: 884. https://doi.org/10.3390/e20110884
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