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

• Main Authors: Viet-Ha Nhu, Ataollah Shirzadi, Himan Shahabi, Wei Chen, John J Clague, Marten Geertsema, Abolfazl Jaafari, Mohammadtaghi Avand, Shaghayegh Miraki, Davood Talebpour Asl, Binh Thai Pham, Baharin Bin Ahmad, Saro Lee
• Format: Article
• Language: English
• Published: MDPI AG 2020-04-01
• Series: Forests
• Subjects: shallow landslide; machine learning; goodness-of-fit; over-fitting; GIS; Iran
• Online Access: https://www.mdpi.com/1999-4907/11/4/421

<b> </b>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.