GIS-Based Evaluation of Landslide Susceptibility Models Using Certainty Factors and Functional Trees-Based Ensemble Techniques

Abstract

The main purpose of this paper is to use ensembles techniques of functional tree-based bagging, rotation forest, and dagging (functional trees (FT), bagging-functional trees (BFT), rotation forest-functional trees (RFFT), dagging-functional trees (DFT)) for landslide susceptibility modeling in Zichang County, China. Firstly, 263 landslides were identified, and the landslide inventory map was established, and the landslide locations were randomly divided into 70% (training data) and 30% (validation data). Then, 14 landslide conditioning factors were selected. Furthermore, the correlation analysis between conditioning factors and landslides was applied using the certainty factor method. Hereafter, four models were applied for landslide susceptibility modeling and zoning. Finally, the receiver operating characteristic (ROC) curve and statistical parameters were used to evaluate and compare the overall performance of the four models. The results showed that the area under the curve (AUC) for the four models was larger than 0.74. Among them, the BFT model is better than the other three models. In addition, this study also illustrated that the integrated model is not necessarily more effective than a single model. The ensemble data mining technology used in this study can be used as an effective tool for future land planning and monitoring.

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

“…Appropriate conditioning factors may differ from region to region, depending on geology, soils, topography, climate, and land use [161]. Thus, protocols must be developed to test the predictive ability of the entire suite of factors that are under consideration [162,163]. In this study, we prepared a landslide inventory map comprising 111 landslides and considered 20 conditioning factors.…”

Groundwater Spring Potential Mapping Using Artificial Intelligence Approach Based on Kernel Logistic Regression, Random Forest, and Alternating Decision Tree Models

“…The validation of the success and predictive performance of the three models was performed based on the receiver operating characteristic (ROC) curves [61][62][63][64][65]. The estimated AUC values range between 0.50 and 1.00 and can be classified based on a quantitative-qualitative classification scheme as follows: 0.5-0.6 (poor), 0.6-0.7 (average), 0.7-0.8 (good), 0.8-0.9 (very good), and 0.9-1 (excellent) [66].…”

Spatial Prediction of Landslides Using Hybrid Integration of Artificial Intelligence Algorithms with Frequency Ratio and Index of Entropy in Nanzheng County, China

“…However, the classifications of landslide conditioning factors were based on previous studies and might not be suitable for the present study. Therefore, further studies should be conducted to find an objective classification method for landslide conditioning factors [113,114]. In order to obtain more reliable landslide susceptibility maps, the significant differences of six landslide susceptibility methods were calculated and compared.…”