Assessment of landslide susceptibility using machine learning classifiers in Ziz upper watershed, SE Morocco

Abstract

ABSTRACTLandslides present a significant hazard to human life, infrastructure, and property, particularly in mountainous regions. In Morocco, these risks have garnered increased attention due to their detrimental impact. This study seeks to model landslide susceptibility using three machine learning classifiers (MLCs): Multi-Layer Perceptron (MLP), Random Forest (RF), and Adaptive Boosting Classifier (AdaBoost), and compare their performance. Initially, 144 landslide sites were identified, and thirteen factors pertaining to landslides were considered. The models’ performance was assessed by calculating the area under the receiver operating characteristic curve (AUC-ROC). The findings reveal that AUC values range from 68.7% for AdaBoost to 82.2% for RF. The generated landslide susceptibility maps can aid decision-makers in avoiding areas with a high susceptibility to landslides.KEYWORDS: Landslide susceptibilitymachine learninggeographic information systemZizSE Morocco AcknowledgmentsThis research is carried out using data provided by the agency of Hydraulic Basin of Guir-Ziz-Rhris (ABH-GZR). The authors would like to thank Mr. N. Khan for his valuable help in validating process.Disclosure statementNo potential conflict of interest was reported by the authors.Data availability statementThe data used to support the results of this research are available from the corresponding author upon request.Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.