Advances and Applications of Artificial Intelligence and Numerical Simulation in Risk Emergency Management and Treatment

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Original Research
16 January 2023

Areas with vulnerable ecological environments often breed many geological disasters, especially landslides, which pose a severe threat to the safety of people’s lives and property in these areas. To aid in landslide prevention and mitigation, an approach combining the coefficient of determination method (CF) and a deep neural network (DNN) were proposed in this study for landslide susceptibility evaluation. The deep neural network can excavate the deep features of samples and improve the accuracy of the susceptibility model. In addition, the logistic regression model (LRM) and support vector machine (SVM) were selected to create landslide susceptibility maps for comparison, which also involved the coefficient of determination method (CF). Based on landslide remote sensing interpretation and field investigations, a spatial database of mudstone landslides in the Xining area was established. Eight different conditional factors, including the elevation, slope, slope aspect, undulation, curvature, watershed, distance from a fault, and distance from a road, in the study area were selected as the evaluation factors to evaluate the susceptibility. The results revealed that four factors (i.e., the ground elevation, curvature, distance from a fault, and distance from a road) had relatively significant influences on the landslide susceptibility in the study area. Finally, the confusion matrix was used to evaluate the accuracy of the results obtained using the three methods, and the optimal result was selected to evaluate the landslide susceptibility in the study area. It was found that the combined CF-DNN method was more suitable for evaluating the landslide susceptibility in this area. Landslide susceptibility zoning was conducted to divide the study area into four sensitivity levels: low (32.65%), medium (35.12%), high (22.44%), and extremely high (9.79%) susceptibility. The high-risk areas were primarily distributed in the high-elevation areas along the eastern edge of the Huangshui Basin.

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Goaf ground collapse has great constraints on people’s lives, property safety, and social development within the influence scope because of its concealment and suddenly happening characteristics. The high-density resistivity method is used to explore the goaf of a quarry in Xiangtan City, Hunan Province. The surface subsidence of the goaf is analyzed using the apparent resistivity inversion section diagram and the comprehensive analysis results. The filling water or sediment in the goaf is reflected as a low-resistivity abnormal body, with a resistivity change that is significantly different from the resistivity change of the surrounding bedrock and the contour fluctuation. The morphological characteristics and geological conditions of the underground abnormal body in the goaf are deduced. Based on the geophysical prospecting method, geological disasters such as goaf ground collapse can be explored. From the combined analysis and processing of inversion data, the geological structure and stratum information of goaf can be inferred, which provides a theoretical basis for further disaster prevention.

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