The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China

Abstract

Landslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for the CMA using an information content model (ICM) with three knowledge-driven methods (the artificial hierarchy process with the ICM (AHP-ICM), the entropy weight method with the ICM (EWM-ICM), and the rough set with the ICM (RS-ICM)) and to explore the influence of different knowledge-driven methods for a series of parameters on the accuracy of landslide susceptibility mapping (LSM). In this research, the landslide inventory data (145 landslides) were randomly divided into a training dataset: 70% (81 landslides) were used for training the models and 30% (35 landslides) were used for validation. In addition, 13 layers of landslide conditioning factors, namely, altitude, slope gradient, slope aspect, lithology, distance to faults, distance to roads, distance to rivers, annual precipitation, land type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), plan curvature, and profile curvature, were taken as independent, causal predictors. Landslide susceptibility maps were developed using the ICM, RS-ICM, AHP-ICM, and EWM-ICM, in which weights were assigned to every conditioning factor. The resultant susceptibility was validated using the area under the ROC curve (AUC) method. The success accuracies of the landslide susceptibility maps produced by the ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883, respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that the four models used in this study gave close results, with the RS-ICM exhibiting the best performance in landslide susceptibility mapping.Entropy 2019, 21, 372 2 of 24 involving land use management as an efficient approach to reduce property damage and economic loss in landslide-prone areas [1,[6][7][8][9]. The outcome maps would be useful for general planned development activities and disaster management in the future, such as choosing new urban areas and infrastructural activities, as well as for environmental protection.Landslide susceptibility maps can be obtained using both qualitative (inventory-based and knowledge-driven methods) or quantitative approaches (data-driven methods and physically based models) [4,[10][11][12][13][14][15][16][17].Landslide inventory-based techniques, as a prelude to all other methods, include the collection of past landslide data, construction of databases, and production of susceptibility maps based on those data [18]. Landslide inventory mapping can be carried out using a variety of methods that were updated and summarized by Corominas et al. [17].Knowledge-driven methods that estimate landslide potential from the practical experience and expertise of the researcher are used by geomorphologists to analyze...

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,…”

A Method for Improving Controlling Factors Based on Information Fusion for Debris Flow Susceptibility Mapping: A Case Study in Jilin Province, China

“…The SI model is a binary statistical method, whose result can reflect the weights of the controlling factors. [ 50 , 51 ]. The weights are obtained by the following formula.…”

Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data

“…LSM and the evaluation of landslide conditioning factors play a major role in landslide mitigation [8]. Ma et al [9] categorized LSM models into inventory-based, knowledge-driven methods, data-driven methods and physically-based models. Pradhan et al [10] further categorized data-driven methods into two models: (i) bivariate and (ii) multivariate (which is based on correlations among regional conditioning factors, as mentioned by Dou et al [7]).…”