Abstract
Integration of different models may improve the performance of landslide susceptibility assessment, but few studies have tested it. The present study aims at exploring the way to integrating different models and comparing the results among integrated and individual models. Our objective is to answer this question: Will the integrated model have higher accuracy compared with individual model? The Lvliang mountains area, a landslide-prone area in China, was taken as the study area, and ten factors were considered in the influencing factors system. Three basic machine learning models (the back propagation (BP), support vector machine (SVM), and random forest (RF) models) were integrated by an objective function where the weight coefficients among different models were computed by the gray wolf optimization (GWO) algorithm. 80 and 20% of the landslide data were randomly selected as the training and testing samples, respectively, and different landslide susceptibility maps were generated based on the GIS platform. The results illustrated that the accuracy expressed by the area under the receiver operating characteristic curve (AUC) of the BP-SVM-RF integrated model was the highest (0.7898), which was better than that of the BP (0.6929), SVM (0.6582), RF (0.7258), BP-SVM (0.7360), BP-RF (0.7569), and SVM-RF models (0.7298). The experimental results authenticated the effectiveness of the BP-SVM-RF method, which can be a reliable model for the regional landslide susceptibility assessment of the study area. Moreover, the proposed procedure can be a good option to integrate different models to seek an “optimal” result.
Highlights
Landslides are one of the most dangerous mass movements in mountainous areas, resulting in substantial loss of life and damage of property on a yearly basis (Petley, 2012; Chen et al, 2017a; Guo et al, 2018)
Considering the individual back propagation (BP), support vector machine (SVM), and random forest (RF) models have been widely used, we only introduced the principles on the integration of them
The initial population of the gray wolf optimization (GWO) algorithm is randomly distributed in the analytical space
Summary
Landslides are one of the most dangerous mass movements in mountainous areas, resulting in substantial loss of life and damage of property on a yearly basis (Petley, 2012; Chen et al, 2017a; Guo et al, 2018). Many potential landslides bring severe challenges to the risk management of geological disasters (Klimešl et al, 2017). The demand for land is increasing with the acceleration of urban construction. The high risks caused by the uncertainty of landslide disaster seriously restrict land use planning in landslideprone areas (Fell et al, 2008). Proper strategies or measures for landslide risk mitigation are increasingly attracting the attention of the academia, especially at this stage (Van Westen et al, 2008).
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
