Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Abstract

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.

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

“…Although domain-knowledge-driven qualitative approach is advantageous in predicting landslides, data-driven quantitative methods are widely used because collecting field data from landslide areas are challenging and hard to acquire [3]. Pourghasemi et al [14] reported that a variety of quantitatively-statistical, multi-criteria decision making, and machine learning-methods have been applied for predicting landslide susceptibility, of which logistical regression [15][16][17][18] is the most frequently used method, followed by the frequency ratio [19,20], weights-of-evidence [18,21], artificial neural networks [22,23], analytic hierarchy process [24,25], statistical index [26], index of entropy [27][28][29][30], and support vector machine [31,32]. Environmental data collected from fields as well as extracted from satellite images to develop landslide prediction models are diverse in nature, and therefore prone to inaccuracies [13].…”

Assessment of landslide susceptibility at a local spatial scale applying the multi-criteria analysis and GIS: a case study from Slovakia

“…The group of the quantitative methods includes statistical methods such as frequency ratio or logistic regression (Lee and Pradhan 2007;Vakhshoori and Pourghasemi 2019), machine learning methods, e.g. random forest (Zhang et al 2017), artificial neural networks (Dou et al 2015) or support vector machines (Hong et al 2015;Zhang et al 2018). Many landslide susceptibility studies aimed at comparing these methods and finding the most suitable alternative (Abedini and Tulabi 2018).…”

Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania

“…The present study proposes the use of three models to detect areas prone to floods and flash floods: the frequency ratio (FR), a bivariate statistical method; the Multilayer Perceptron Neural Networks (MLP), a machine learning solution; and the hybrid integration of the FR and the MLP models. The FR method has been used in previous studies, due to its easy applicability [26][27][28][29][30]. The MLP represents a supervised machine learning solution which uses the backpropagation algorithm and is a commonly used method in landslide and flood hazard assessment studies.…”