Abstract
This study validated the robust performances of the recently proposed comprehensive landslide susceptibility index model (CLSI) for landslide susceptibility mapping (LSM) by comparing it to the logistic regression (LR) and the analytical hierarchy process information value (AHPIV) model. Zhushan County in China, with 373 landslides identified, was used as the study area. Eight conditioning factors (lithology, slope structure, slope angle, altitude, distance to river, stream power index, slope length, distance to road) were acquired from digital elevation models (DEMs), field survey, remote sensing imagery, and government documentary data. Results indicate that the CLSI model has the highest accuracy and the best classification ability, although all three models can produce reasonable landslide susceptibility (LS) maps. The robust performance of the CLSI model is due to its weight determination by a back-propagation neural network (BPNN), which successfully captures the nonlinear relationship between landslide occurrence and the conditioning factors.
Highlights
In terms of economic and death impact, landslides rank seventh globally [1]; they cause damage to roads, railways, power lines, and even tourism and historical sites [2,3].China is a mountainous country, with its development severely restricted by landslides.Many efforts have been made to prevent and alleviate landslides
It can be seen that all the minimum difference value (D-value) fall into the analytical hierarchy process information value (AHPIV) and logistic regression (LR) model, while there are two maximum D-values that fall into the comprehensive landslide susceptibility index model (CLSI) model
This result indicates that, in addition to the accuracy, the CLSI model performs the best in classification ability. This is to be expected because the CLSI uses the trained back-propagation neural network (BPNN) to calculate the weights, and this captures the nonlinear relationship between the conditioning factors (CFs) and the occurrence of landslides, leading to more objective and scientific
Summary
In terms of economic and death impact, landslides rank seventh globally [1]; they cause damage to roads, railways, power lines, and even tourism and historical sites [2,3]. To confirm that different sampling methods can affect the accuracy of LSM, Nefeslioglu et al [38] compared the LR method and back-propagation neural network (BPNN) model. Bui et al [15] found that the LSM results obtained by a support vector machine (SVM) have the best performance, compared to the decision tree and Naïve Bayes They further explored some new sophisticated machine learning techniques [41], such as multi-layer perceptron neural networks, kernel LR method, etc. LR represents the traditional statistical method, and AHPIV represents an integrated method that combines prior knowledge and subjective weight determination With these methods, the landslide susceptibility (LS) maps of the study region were produced, respectively, and their performances were evaluated in terms of prediction accuracy and classification ability. The verification methods included the area under the receiver operating feature curve (AUC), seed cell area index (SCAI), and the cumulative number of landslide points
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