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Coal mining at deep levels can cause mine water inrush and groundwater contamination, making it important to accurately and rapidly identify the water inrush source. In this study, 52 water samples were extracted from three types of aquifers in the Linhuan mining area, China. The water sample components Na+ + K+, Ca2+, Mg2+, HCO3−, Cl−, and SO42−, measured in the experiment, were used as evaluation variables, and the piecewise function equation was established by using the exponential whitening function. Finally, combined with water sample data and the CRITIC weighted grey situation decision-making method, the comprehensive membership degree was obtained, and the water inrush source was identified according to the principle of the maximum membership degree. The comprehensive accuracy of the model was 92.3%. The traditional grey situation decision-making method uses the linear whitening function to determine the membership value, ignoring that the value of the whitening function outside the adjacent level is 0, which improves the weight of the adjacent level, causes the loss of effective information, and reduces the discrimination rate. The exponential whitenization function in this paper will solve this problem and further improves the grey situation decision-making method to discriminate the water inrush source, which would also be beneficial regarding the prevention and control of mine water inrush and groundwater contamination. [ABSTRACT FROM AUTHOR]

In this study, we introduced novel hybrid of evidence believe function (EBF) with logistic regression (EBF-LR) and logistic model tree (EBF-LMT) for landslide susceptibility modelling. Fourteen conditioning factors were selected, including slope aspect, elevation, slope angle, profile curvature, plan curvature, topographic wetness index (TWI), stream sediment transport index (STI), stream power index (SPI), distance to rivers, distance to faults, distance to roads, lithology, normalized difference vegetation index (NDVI), and land use. The importance of factors was assessed using correlation attribute evaluation method. Finally, the performance of three models was evaluated using the area under the curve (AUC). The validation process indicated that the EBF-LMT model acquired the highest AUC for the training (84.7%) and validation (76.5%) datasets, followed by EBF-LR and EBF models. Our result also confirmed that combination of a decision tree-logistic regression-based algorithm with a bivariate statistical model lead to enhance the prediction power of individual landslide models. [ABSTRACT FROM AUTHOR]

Landslides cause a considerable amount of damage around the world every year. Landslide susceptibility assessments are useful for the mitigation of the associated potential risks to local economic development, land use planning, and decision makers. The main aim of this study was to present a novel hybrid approach of bagging (B)-based kernel logistic regression (KLR), named the BKLR model, for spatial prediction of landslides in the Shangnan County, China. We first selected 15 conditioning factors for landslide susceptibility modeling. Then, the prediction capability of all conditioning factors was evaluated using the least square support vector machine method. Model validation and comparison were performed based on the area under the receiver operating characteristic curve and several statistical-based indexes, including positive predictive rate, negative predictive rate, sensitivity, specificity, kappa index, and root mean square error. Results indicated that the BKLR ensemble model outperformed and outclassed the KLR and the benchmark support vector machine model. Our findings overall confirmed that a combination of the meta model with a decision tree classifier based on a functional algorithm can decrease the over-fitting and variance problems of data, which could enhance the prediction power of the landslide model. The resultant susceptibility maps could be useful for hazard mitigation in the study area and other similar landslide-prone areas. [ABSTRACT FROM AUTHOR]

The main goal of this study was to produce landslide susceptibility mapping by frequency ratio (FR), statistical index (SI), and index of entropy (IOE) models based on geographic information system (GIS) for the Baozhong region of Baoji, China. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out a field survey. A total of 79 landslides were mapped and out of which, 55 (70 %) were randomly selected for building landslide susceptibility models, while the remaining 24 (30 %) were used for validating the models. In this case study, the following landslide conditioning factors were evaluated: slope degree, slope aspect, plan curvature, altitude, geomorphology, lithology, distance from faults, distance from rivers, and precipitation. Subsequently, landslide susceptibility maps were produced using FR, SI, and IOE models. Finally, the validation of landslide susceptibility map was carried out using areas under the curve (AUC). The AUC plot estimation results showed that the susceptibility map using FR model has the highest prediction accuracy of 82.49 %, followed by the SI model (81.43 %) and the IOE model (79.62 %). Similarly, the AUC plot showed that the success rate of the three models was 84.95 % for FR model, 82.37 % for SI model, and 82.05 % for IOE model, respectively. According to the validation results of the AUC evaluation, the map produced by the FR model exhibits the most satisfactory properties. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose. [ABSTRACT FROM AUTHOR]

Abstract Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world. Graphical abstract Unlabelled Image Highlights • The NB, RBF Classifier and RBF Network models were applied for landslide modelling. • Support vector machine (SVM) was used for conditioning factor selection process. • Evaluation and comparison of MLAs were performed by some statistical metrics. • The RBF Classifier model outperformed the NB and the RBF Network models. • The proposed model was suggested for other landslide prone areas over the world. [ABSTRACT FROM AUTHOR]