Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China.

By Wei Chen, Jian-bing Peng, Haoyuan Hong, Himan Shahabi, Biswajeet Pradhan, Junzhi Liu, A-Xing Zhu, X. Pei, Zhao Duan. Science of the Total Environment, 2018

426 citations recorded by the source corpus.

The preparation of a landslide susceptibility map is considered to be the first step for landslide hazard mitigation and risk assessment. However, these maps are accepted as end products that can be used for land use planning. The main goal of this study is to assess and compare four advanced machine learning techniques, namely the Bayes' net (BN), radical basis function (RBF) classifier, logistic model tree (LMT), and random forest (RF) models, for landslide susceptibility modelling in Chongren County, China. A total of 222 landslide locations were identified in the study area using historical reports, interpretation of aerial photographs, and extensive field surveys. The landslide inventory data was randomly split into two groups with a ratio of 70/30 for training and validation purposes. Fifteen landslide conditioning factors were prepared for landslide susceptibility modelling. The spatial correlation between landslides and conditioning factors was analyzed using the information gain (IG) method. The BN, RBF classifier, LMT, and RF models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) and statistical measures, including sensitivity, specificity, and accuracy, were employed to validate and compare the predictive capabilities of the models. Out of the tested models, the RF model had the highest sensitivity, specificity, and accuracy values of 0.787, 0.716, and 0.752, respectively, for the training dataset. Overall, the RF model produced an optimized balance for the training and validation datasets in terms of AUC values and statistical measures. The results of this study also demonstrate the benefit of selecting optimal machine learning techniques with proper conditioning selection methods for landslide susceptibility modelling.

Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/7462a01c52fa6b4828267fdb2703a60a1471742b

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Citations in the corpus

Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis (2019, positive): “Factors with no or negative contribution on modelling results should be eliminated to increase of model performance (Chen et al. 2018b).”
Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms (2018, methodology): “The bett r results of the LB model due to it solving the data over fitting problems [76] an also this model uses maximum likelihood to find the smallest robable d fference between predicted and observed values [93].”
A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction (2019, methodology): “…true positive and false positive, the negative predictive ratio (NPR) defined as the ratio of true negative to the sum of true negative and false negative, and the total accuracy (TR) defined as the ratio of sum of true positive and true negative to the number of testing samples (Chen et al. 2018).”
Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree (2019, background): “…et al. 2018), logistic regression (LR) (Colkesen et al. 2016; Aditian et al. 2018), multivariate adaptive regression spline (Zabihi et al. 2016; Chen et al. 2018d), weight of evidence (Tehrany et al. 2014; Guo et al. 2015), index of entropy (Tsangaratos et al. 2017; Chen et al. 2018c),…”
New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed (2019, background): “…decision trees such as naïve Bayes tree (NBT) [5,52], radial basis function (RBF) [53], kernel logistic regression (KLR) [54,55], Bayes’ net (BN) [56], bivariate statistical index (SI) [57], stochastic gradient descent (SGD) [58], particle swarm optimization (PSO) [59], best-ﬁrst decision tree…”
Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models (2020, methodology): “…(d) Profile Curvature; (e) Lithology; (f) Bedding Structure; (g) Fractional Vegetation Cover; (h) Distance to River. following previous studies (Chen et al. 2018; Wang et al. 2019): 80% of the landslides and the same number of non-landslide pixels were randomly selected to form the training…”
Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran (2020, background): “A wide variety of machine learning models are now in use in natural hazard research, including: artificial neural network (ANN) [39,40], adaptive neuro-fuzzy inference system (ANFIS) [41–44], support vector machine (SVM) [15,45,46], K-nearest neighbor (KNN) [47], logistic model tree (LMT) [48,49], a”