Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China
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Fecha
2021-10
Profesor/a Guía
Facultad/escuela
Idioma
en_US
Título de la revista
ISSN de la revista
Título del volumen
Editor
Springer Science and Business Media B.V.
Nombre de Curso
Licencia CC
Licencia CC
Resumen
Landslide hazard assessment is critical for preventing and mitigating landslide disasters. The tuning of hyperparameters is of great importance to achieve better accuracy in a landslide hazard assessment model. In this study, a novel approach is proposed for landslide hazard assessment with support vector machine (SVM) as the primary model and Bayesian optimization (BO) algorithm as the parameter tuning method. This study describes 1711 historical landslide disaster points in Nanping City, and a total of 12 landslide conditioning factors including elevation, slope, aspect, curvature, lithology, soil type, soil erosion, rainfall, river, land use, highway, and railway were selected. The multicollinearity diagnosis was performed on the factors using the Spearman correlation coefficient. For model validation, 1711 landslides and 1711 non-landslides were collected as the dataset and divided into a training dataset (50 %) and a testing dataset (50 %). The performance of the model was evaluated by the confusion matrix and receiver operating characteristic (ROC) curve. The results of the confusion matrix accuracy and the area under the ROC curve showed that the BO-SVM model (89.53 %, 0.97) performed better than the SVM model (84.91 %, 0.93). In addition, the landslide hazard maps generated by the BO-SVM model had better overall results than that by the SVM model.
Notas
Indexación: Scopus.
Palabras clave
Bayesian optimization method, GIS, Landslide hazard assessment, Machine learning, Support vector machine
Citación
Natural Hazards Volume 109, Issue 1, Pages 931 - 948 October 2021