Exploring Complementary Models Consisting of Machine Learning Algorithms for Landslide Susceptibility Mapping

Abstract

Landslides frequently occur because of natural or human factors. Landslides cause huge losses to the economy as well as human beings every year around the globe. Landslide susceptibility prediction (LSP) plays a key role in the prevention of landslides and has been under investigation for years. Although new machine learning algorithms have achieved excellent performance in terms of prediction accuracy, a sufficient quantity of training samples is essential. In contrast, it is hard to obtain enough landslide samples in most the areas, especially for the county-level area. The present study aims to explore an optimization model in conjunction with conventional unsupervised and supervised learning methods, which performs well with respect to prediction accuracy and comprehensibility. Logistic regression (LR), fuzzy c-means clustering (FCM) and factor analysis (FA) were combined to establish four models: LR model, FCM coupled with LR model, FA coupled with LR model, and FCM, FA coupled with LR model and applied in a specific area. Firstly, an inventory with 114 landslides and 10 conditioning factors was prepared for modeling. Subsequently, four models were applied to LSP. Finally, the performance was evaluated and compared by k-fold cross-validation based on statistical measures. The results showed that the coupled model by FCM, FA and LR achieved the greatest performance among these models with the AUC (Area under the curve) value of 0.827, accuracy of 85.25%, sensitivity of 74.96% and specificity of 86.21%. While the LR model performed the worst with an AUC value of 0.736, accuracy of 77%, sensitivity of 62.52% and specificity of 72.55%. It was concluded that both the dimension reduction and sample size should be considered in modeling, and the performance can be enhanced by combining complementary methods. The combination of models should be more flexible and purposeful. This work provides reference for related research and better guidance to engineering activities, decision-making by local administrations and land use planning.

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey

“…Peng et al [41] Novel hybrid method combining rough set theory and SVM Yu et al [40] SVM with geographical weighted regression and PSO Pham et al [86] Novel hybrid method using sequential minimal optimization and SVM Zhang et al [117] Fractal dimension with index of entropy and SVM Adnan et al [118] LSM generated by combining the LSM produced by four ML models KNN, MLP, RF, and SVM Wang et al [119] GeoSOM with RF and ensemble ML model consisting of ANN-SVM-GBDT Fang et al [13] Proposed three hybrid models CNN-SVM, CNN-RF, and CNN-LGR Hu et al [48] Combining fractal theory with SVM and NB Rong et al [74] Combination of Bayesian optimization with RF and GBDT Wang et al [55] Integration of MultiBoost with RBFN and CDT Sahana et al [120] Multi-layer perceptron neural network classifier with ensemble ML models like Bagging, Dagging, and DECORATE Xie et al [79] GeoDetector using factor detectors and interaction detectors with four ML models ANN, BN, LGR, and SVM Alqadhi et al [121] Four optimized ML model namely PSO-ANN, PSO-RF, PSO-M5P, and PSO-SVM with LGR Arabameri et al [122] Credal decision tree based hybrid models namely CDT-bagging, CDT-MultiBoost, and CDT-SubSpace Saha et al [123] Hybrid ensemble method using RF as a base classifier and ensemble methods, namely RotFor-RF, RSS-RF, and bagging-RF Xing et al [124] The output of ML models namely back propagation, RF, and SVM are combined using weight factors Hu et al [125] Fuzzy c-means clustering and factor analysis with LGR Zhou et al [51] RF with GeoDetector and recursive feature elimination Sun et al [126] GeoDetector and RF Lui et al [61] GeoDetector with RF Liang et al [71] Combination of unsupervised and supervised ML method Dung et al [127] Novel hybrid method consisting bagging-based rough set and AdaBoost-based rough set Wei et al [128] Spatial response feature with ML classifiers…”

“…The AUC value, when compared to other articles, is low. Hu et al [125] explored the hybrid implementation of LGR coupled with fuzzy c-means clustering (FCM) and factor analysis (FA) for the generation of LSM. The two methods, FCM and FA, were introduced to compensate limitation of the LGR model.…”

Landslide Susceptibility Prediction Based on High-Trust Non-Landslide Point Selection

“…Semi-supervised learning has been widely used in sample data analysis and evaluation [26][27][28]. In landslide susceptibility prediction and landslide detection, supervised learning frameworks, semi-supervised learning frameworks, and unsupervised learning frameworks have also demonstrated their superiority [29][30][31]. This paper selects Fu'an City, Fujian Province, China, as the research area.…”