Exploring novel hybrid soft computing models for landslide susceptibility mapping in Son La hydropower reservoir basin

Abstract

In this study, two novel hybrid models namely Bagging-based Rough Set (BRS) and AdaBoost-based Rough Set (ABRS) were used to generate landslide susceptibility maps of Son La hydropower reservoir basin, Vietnam. In total, 186 past landslide events and twelve landslides affecting factors (slope degree, slope aspect, elevation, curvature, focal flow, river density, rainfall, aquifer, weathering crust, lithology, fault density and road density) were considered in the modeling study. The landslide data was split into training (70%) and testing (30%) for the model's development and validation. One R feature selection method was used to select and prioritize the landslide affecting factors based on their importance in model prediction. Performance of the hybrid developed models was evaluated and also compared with single rough set (RS) and support vector machine (SVM) models using various standard statistical measures including area under the curve (AUC)-receiver operating characteristics (ROC) curve. The results show that the developed hybrid model BRS (AUC ¼ 0.845) is the most accurate model in comparison to other models (ABRS, SVM and RS) in predicting landslide susceptibility. Therefore, the BRS model can be used as an effective tool in the development of an accurate landslide susceptibility map of the hilly area.

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…”

“…Dung et al [127] developed a novel hybrid method consisting of bagging-based rough set (BRS) and AdaBoost-based rough set (ABRS) for the generation of LSM. One-R algorithm was used for feature selection.…”

Landslide susceptibility prediction mapping with advanced ensemble models: Son La province, Vietnam

“…Landslide hazard has caused serious damage to humans, houses, road networks, agriculture, road infrastructure, and drainage system in the province (IFRC, 2021). For example, there were 10 killed people, 4 injured, 258 damaged houses by landslides in the Muong La district in 2017 (Dung et al, 2021).…”

“…Heavy prolonged rainfall is considered the primary cause of landslides in mountain areas (Singh et al, 2021). It might trigger unexpected landslides depending on the topographical and geological characteristics of the ground/rock mass (Dung et al, 2021). In this study, the rainfall data were derived from 25 rain gauge stations in Son La province and neighboring provinces from 2010 to 2021.…”

Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning

“…Therefore, the selection of crucial evaluation factors and reduction in redundant factors in the dataset can not only resolve the fitting problem of machine learning but also reduce the computational burden and improve the efficiency of the model. To enhance the model's accuracy and address potential overfitting issues in machine learning-based models, methodologies such as frequency ratio [27], deterministic factor [28], Pearson correlation coefficient [29], factor analysis [30], rough set [31], information gain [32], and recursive feature elimination [33] were employed.…”