A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping

Abstract

Landslides cause huge damage to social economy and human beings every year. Landslide susceptibility mapping (LSM) occupies an important position in land use and risk management. This study is to investigate a hybrid model which makes full use of the advantage of supervised learning model (SLM) and unsupervised learning model (ULM). Firstly, ten continuous variables were used to develop a ULM which consisted of factor analysis (FA) and k-means cluster for a preliminary landslide susceptibility map. Secondly, 351 landslides with “1” label were collected and the same number of non-landslide samples with “0” label were selected from the very low susceptibility area in the preliminary map, constituting a new priori condition for a SLM, and thirteen factors were used for the modeling of gradient boosting decision tree (GBDT) which represented for SLM. Finally, the performance of different models was verified using related indexes. The results showed that the performance of the pretreated GBDT model was improved with sensitivity, specificity, accuracy and the area under the curve (AUC) values of 88.60%, 92.59%, 90.60% and 0.976, respectively. It can be concluded that a pretreated model with strong robustness can be constructed by increasing the purity of samples.

Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods

“…It continuously improves the prediction accuracy through interactions. A new decision tree was established in the gradient direction of the reducing residuals in each iteration [80]. The basic idea of GBDT is to build several weak classifiers and finally combine them to form a strong classifier after multiple iterations.…”

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…”

“…Nhu et al [42] stated choosing random non-landslide locations using a trial-and-error method has limitations and suggested a more standardized approach for selecting a non-landslide location for future studies. Liang et al [71] used an unsupervised ML model to generate the nonlandslide locations, and the generated dataset was used by supervised ML to generate LSM. The additional process of using an unsupervised ML model for data generation improved the accuracy of the LSM generated by the supervised ML model.…”

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

“…The scoring functions were determined (Equation ( 11)) and LR model was established using five common factors scores (C1-C5) as independent variables. Accordingly, Equation (13) restored the relationship between the 10 conditioning factors and the occurrence of landslides. The coefficients of F1, F4, F5 and F7 were negative, which were not conducive to the occurrence of landslide.…”

“…Traditional machine learning methods (TMLM) as the Logistic regression (LR) model, clustering analysis and principal component analysis have been well verified in LSP [7][8][9]. The new machine learning methods (NMLM), such as support vector machines, artificial neural networks and deep learning, have gained consideration with the development of computer technology [10][11][12][13][14][15][16]. Ensemble learning offers the possibility to further improve the accuracy and reflection of nonlinear relationships between landslide and conditioning factors [17].…”