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Shallow landslide susceptibility mapping by random forest base classifier and its ensembles in a semi-arid region of iran

Viet, H. N. and Shirzadi, A. and Shahabi, H. and Wei, C. and John, J. C. and Marten, G. and Jaafari, A. and Avand, M. and Miraki, S. and Asl, D. T. and Binh, T. P. and Ahmad, B. and Lee, Saro (2020) Shallow landslide susceptibility mapping by random forest base classifier and its ensembles in a semi-arid region of iran. Forests, 11 (4). p. 421.

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Official URL: http://www.doi.org/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.

Item Type:Article
Uncontrolled Keywords:machine learning, goodness-of-fit, over-fitting
Subjects:G Geography. Anthropology. Recreation > G Geography (General) > G70.39-70.6 Remote sensing
Divisions:Built Environment
ID Code:86974
Deposited By: Narimah Nawil
Deposited On:22 Oct 2020 12:21
Last Modified:22 Oct 2020 12:21

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