Landslide risk assessment in Nanping City based on artificial neural networks model

Abstract

Landslide hazards continuous sequence the safety of people's lives and property and the sustainable development of regional society and economy, and landslide risk assessment can provide an effective theoretical basis for disaster mitigation and regional planning. A total of 1711 historical landslide hazard sites around Nanping City were obtained, and 11 impact factors, including elevation, slope, aspect, curvature, geological lithology, soil type, rainfall, water system, land use, road and railway etc. were selected. The covariance analysis of each factor was carried out using the Spearman correlation coefficient. Based on the data of 1711 landslides and 1711 non-landslides, an artificial neural network (ANN) model was used to evaluate the landslide risk in the study area, and the model was validated using a confusion matrix and receiver operating characteristic (ROC) curve. The results show that the confusion matrix accuracy was 84.91% and the area under the ROC curve (AUC) was 0.93, indicating that the model has high accuracy and prediction rate. The landslide risk index was classified into five classes by natural break method, and the results show that the highest risk areas in the study area locate in Yanping District and Pucheng County, followed by Shunchang County and Songxi County, and the rest of the areas were mostly low-risk areas and lower-risk areas. The results of the study can provide theoretical basis and scientific guidance for local regional planning and disaster mitigation.

Keywords

• landslides
• landslide risk assessment
• artificial neural networks
• machine learning