A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison

Abstract

This article uses an integrated methodology based on a chi-squared automatic interaction detection (CHAID) model combined with analytic hierarchy process (AHP) for pair-wise comparison to assess medium-scale landslide susceptibility in a catchment in the Inje region of South Korea. An inventory of 3596 landslide locations was collected using remote sensing, and a random sample comprising 30% of these was used to validate the model. The remaining portion (70%) was processed by the nearest-neighbour index (NNI) technique and used for extracting the cluster patterns at each location. These data were used for model training purposes. Ten landslide-conditioning factors (independent variables) representing four main domains, namely (1) topology, (2) geology, (3) hydrology, and (4) land cover, were used to produce two landslide-susceptibility maps. The first landslide-susceptibility map (LSM1) was produced by overlaying the terminal nodes of the CHAID result tree. The second landslide-susceptibility map (LSM2) was produced using the overlay result of AHP pair-wise comparisons of CHAID terminal nodes. The prediction rate curve results were better with LSM2 (area under the prediction curve (AUC) = 0.80) than with LSM1 (AUC = 0.76). The results confirmed that the integrated hybrid model has superior prediction performance and reliability, and it is recommended for future use in medium-scale landslidesusceptibility mapping.

Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree

“…Evaluation of these approaches has been well presented i.e., in Chacon et al [18] and Van Westen, et al [19]. In recent years, new approaches that are based on advanced statistical and machine learning methods have been proposed i.e., fuzzy k-Nearest Neighbor [17]; fuzzy rule based models [20][21][22][23]; neural networks [24][25][26][27][28][29][30]; support vector machines [31][32][33][34][35][36][37][38]; Random Forests; metaheuristic optimized least squares support vector machines [39,40]; Cuckoo optimized relevance vector machines [41]; Chi-squared automatic interaction detection (CHAID) [42]; tree-based algorithms [43][44][45][46][47]; and, gene expression programming [48]. The main advantage of these methods is that they are capable of involving several to a large number of variables for reliable results, and overall, these methods are able to provide better performance models when compared to those of conventional methods [43,49,50].…”

On Multi-Scale Entropy Analysis of Order-Tracking Measurement for Bearing Fault Diagnosis under Variable Speed

“…The merits of the decision tree classifier includes that the classification rule is simple and less computation effort is required. The algorithms of the decision trees that have been utilized broadly include ID3 [21], C4.5 [22], CHAID [23], CART [24], and QUEST [25]. Due to its flexible capability for continuous and discrete data processing, the C4.5 algorithm [22] was employed in this research to construct the decision tree model for the classification of different bearing defects.…”

Habitat Potential Mapping of Marten (Martes flavigula) and Leopard Cat (Prionailurus bengalensis) in South Korea Using Artificial Neural Network Machine Learning

“…It is a widely used method for validation [37][38][39][40]. ROC curves can show sensitivity and specificity on the x-axis and y-axis, respectively.…”