Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

Viet-Ha Nhu,Marten Geertsema,Baharin Bin Ahmad,Saro Lee,Abolfazl Jaafari,Binh Thai Pham,Himan Shahabi,John J Clague,Davood Talebpour Asl,Mohammadtaghi Avand,Ataollah Shirzadi,Wei Chen,Shaghayegh Miraki

doi:10.3390/f11040421

Abstract

We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers—Bagging (BA, BA-RAF), Random Subspace (RS, RS-RAF), and Rotation Forest (RF, RF-RAF). Modeling and validation were done on 111 shallow landslide locations using 20 conditioning factors tested by the Information Gain Ratio (IGR) technique. We assessed model performance with statistically based indexes, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). All four machine learning models that we tested yielded excellent goodness-of-fit and prediction accuracy, but the RF-RAF ensemble model (AUC = 0.936) outperformed the BA-RAF, RS-RAF (AUC = 0.907), and RAF (AUC = 0.812) models. The results also show that the Random Forest model significantly improved the predictive capability of the RAF-based classifier and, therefore, can be considered as a useful and an effective tool in regional shallow landslide susceptibility mapping.

Highlights

Landslide susceptibility mapping is an important step in mitigating the damage and injury inflicted by landslides
We evaluate and compare the performances of the models, and lastly derive a map of landslide data set forfrom our study area to develop three ensemble models, namely BA-Random Forest (RAF), Random Forest (RF)-RAF, and RSsusceptibility each model
We provide a successful application of Random Forest and its ensembles for shallow landslide susceptibility mapping in a semi-arid region of Iran

Summary

Introduction

Landslide susceptibility mapping is an important step in mitigating the damage and injury inflicted by landslides. The selection of proper methods/models for generating the most accurate and informative landslide susceptibility maps remains challenging [3]. In which the researchers used a variety of GIS-aided methods for the development of landslide susceptibility maps, have focused on this issue. Yesilnacar and Topal [4] showed that artificial neural network (ANN) performed better than logistic regression (LR) in predicting future landslides and producing landslide susceptibility maps. Yilmaz [5] reported that a susceptibility map produced by ANN was more accurate than the map generated by LR and the frequency ratio (FR). Pradhan [6] analyzed the predictive performance of the support vector machine (SVM), decision tree, and adaptive neuro-fuzzy inference (ANFIS) models and reported that ANFIS outperformed the other models. Van Dao et al [3] and Nhu et al [10]

Results

Discussion

Conclusion