Assessment of Landslide Susceptibility Using Integrated Ensemble Fractal Dimension with Kernel Logistic Regression Model

Abstract

The main aim of this study was to compare and evaluate the performance of fractal dimension as input data in the landslide susceptibility mapping of the Baota District, Yan’an City, China. First, a total of 632 points, including 316 landslide points and 316 non-landslide points, were located in the landslide inventory map. All points were divided into two parts according to the ratio of 70%:30%, with 70% (442) of the points used as the training dataset to train the models, and the remaining, namely the validation dataset, applied for validation. Second, 13 predisposing factors, including slope aspect, slope angle, altitude, lithology, mean annual precipitation (MAP), distance to rivers, distance to faults, distance to roads, normalized differential vegetation index (NDVI), topographic wetness index (TWI), plan curvature, profile curvature, and terrain roughness index (TRI), were selected. Then, the original numerical data, box-counting dimension, and correlation dimension corresponding to each predisposing factor were calculated to generate the input data and build three classification models, namely the kernel logistic regression model (KLR), kernel logistic regression based on box-counting dimension model (KLRbox-counting), and the kernel logistic regression based on correlation dimension model (KLRcorrelation). Next, the statistical indexes and the receiver operating characteristic (ROC) curve were employed to evaluate the models’ performance. Finally, the KLRcorrelation model had the highest area under the curve (AUC) values of 0.8984 and 0.9224, obtained by the training and validation datasets, respectively, indicating that the fractal dimension can be used as the input data for landslide susceptibility mapping with a better effect.

Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-based FR–RF Integrated Model and Multiresolution DEMs

“…Qualitative methods, such as analytical hierarchy process (AHP) [17,18], are based on the judgements of one or more experts. Quantitative approaches, such as frequency ratio (FR) [19][20][21][22][23][24][25], logistic regression (LR) [26][27][28][29], statistical index (SI) [23], weight of evidence (WoE) [30], evidential belief function (EBF) [31], information value (IV) [32][33][34], information content model (ICM) [35], certainty factors (CF) [36], multivariate regression (MR) [37], multivariate adaptive regression spline (MARS) [38][39][40], linear discriminant analysis (LDA) [41], and quadratic discriminant analysis (QDA) [41] are based on strict mathematical rules, regardless of any personal judgement. Artificial intelligence techniques, such as kernel logistic regression (KLR) [27], artificial neural network (ANN) [42][43][44][45], support vector machines (SVM) [46][47][48][49], boosted regression trees (BRT) [12,50], neuro-fuzzy system (NFS) [51,52], naive Bayes (NB) [28], decision tree (DT) [11,…”

Landslide Susceptibility Mapping and Assessment Using Geospatial Platforms and Weights of Evidence (WoE) Method in the Indian Himalayan Region: Recent Developments, Gaps, and Future Directions

“…More recently, machine learning (ML) techniques are gaining importance due to high predictive results, reproducibility, and superior performance capabilities than other statistical or knowledge-based methods. Many researchers widely applied ML techniques to find out the best suitable model to assess the landslide susceptibility mapping including neural-fuzzy [71], support vector machines (SVMs) [72,73], decision tree (DT) method [74], artificial neuronal networks (ANNs) [75], neuro-fuzzy-NF and adaptive neuro-fuzzy inference system (ANFIS) [76], generalized additive model (GAM) [77], adaBoost (AB) [78,79], random forest (RF) [80,81], naïve Bayes' (NB) [82], kernel logistic regression (KLR) [83], boosted regression tree (BRT) [84], classification and regression tree (CART) [85], general linear model (GLM) [85], multivariate adaptive regression spline (MAR Spline) model [86], maximum entropy (MaxEnt) [87], and quadratic discriminant analysis (QDA) [88]. Many researchers compared different machine learning (ML) techniques and ensemble with the statistical method to test the model performance, which can be found in the literature [87,88].…”

Evaluation of Different Machine Learning Models and Novel Deep Learning-based Algorithm for Landslide Susceptibility Mapping

“…Due to data quality, factor selection, model parameter adjustment and other factors, some low accuracy, over fitting, and owe fitting problems often appear. In order to solve these problems, hybrid model was developed in recent years, such as reduced error pruning trees (REPT) (Pham et al, 2019b), kernel logistic regression model integrated with fractal dimension (KLRbox-counting) (Zhang et al, 2019), support vector regression model integrated with gray wolf optimization algorithm (SVR-GWO) (Balogun et al, 2021), adaptive neuro-fuzzy inference system model integrated with satin bowerbird optimizer algorithms (ANFIS-SBO) (Chen et al, 2021). Although several models listed above have been previously applied in assessment field of landslide susceptibility and performed well, applying these models to forecast landslide occurrence and explore how to raise prediction accuracy are still the focus of current researches.…”