Automated Machine Learning-Based Landslide Susceptibility Mapping for the Three Gorges Reservoir Area, China

• Published in: Mathematical geosciences 2024-07, Vol.56 (5), p.975-1010
• Main Authors: Ma, Junwei, Lei, Dongze, Ren, Zhiyuan, Tan, Chunhai, Xia, Ding, Guo, Haixiang
• Format: Article
• Language: English
• Subjects: Automation; Canyons; Chemistry and Earth Sciences; Computer Science; Earth and Environmental Science; Earth Sciences; Environmental risk; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrologic cycle; Hydrological cycle; Landslides; Landslides & mudslides; Learning algorithms; Machine learning; Mapping; Multilayer perceptrons; Optimization; Particle swarm optimization; Physics; Regression analysis; Reservoirs; Risk management; Search algorithms; Statistics for Engineering; Support vector machines; Swarm intelligence
• ISSN: 1874-8961; 1874-8953
• DOI: 10.1007/s11004-023-10116-3

Machine learning (ML)-based landslide susceptibility mapping (LSM) has achieved substantial success in landslide risk management applications. However, the complexity of classically trained ML is often beyond nonexperts. With the rapid growth of practical applications, an “off-the-shelf” ML technique that can be easily used by nonexperts is highly relevant. In the present study, a new paradigm for an end-to-end ML modeling was adopted for LSM in the Three Gorges Reservoir area (TGRA) using automated machine learning (AutoML) as the backend model support for the paradigm. A well-defined database consisting of data from 290 landslides and 11 conditioning factors was collected for implementing AutoML and compared with classically trained ML approaches. The stacked ensemble model from AutoML achieved the best performance (0.954), surpassing the support vector machine with artificial bee colony optimization (ABC-SVM, 0.931), gray wolf optimization (GWO-SVM, 0.925), particle swarm optimization (PSO-SVM, 0.925), water cycle algorithm (WCA-SVM, 0.925), grid search (GS-SVM, 0.920), multilayer perceptron (MLP, 0.908), classification and regression tree (CART, 0.891), K-nearest neighbor (KNN, 0.898), and random forest (RF, 0.909) in terms of the area under the receiver operating characteristic curve (AUC). Notable improvements of up to 11% in AUC demonstrate that the AutoML approach succeeded in LSM and could be used to select the best model with minimal effort or intervention from the user. Moreover, a simple model that has been customarily ignored by practitioners and researchers has been identified with performance satisfying practical requirements. The experimental results indicate that AutoML provides an attractive alternative to manual ML practice, especially for practitioners with little expert knowledge in ML, by delivering a high-performance off-the-shelf solution for ML model development for LSM.