Monitoring Potential Geological Hazards with Different InSAR Algorithms: The Case of Western Sichuan

Prediction of Surface Subsidence in Mining Areas Based on Ascending-Descending Orbits Small Baseline Subset InSAR and Neural Network Optimization Models

Surface subsidence hazards in mining areas are common geological disasters involving issues such as vegetation degradation and ground collapse during the mining process, which also raise safety concerns. To address the accuracy issues of traditional prediction models and study methods for predicting subsidence in open-pit mining areas, this study first employed 91 scenes of Sentinel-1A ascending and descending orbits images to monitor long-term deformations of a phosphate mine in Anning City, Yunnan Province, southwestern China. It obtained annual average subsidence rates and cumulative surface deformation values for the study area. Subsequently, a two-dimensional deformation decomposition was conducted using a time-series registration interpolation method to determine the distribution of vertical and east-west deformations. Finally, three prediction models were employed: Back Propagation Neural Network (BPNN), BPNN optimized by Genetic Algorithm (GA-BP), and BPNN optimized by Artificial Bee Colony Algorithm (ABC-BP). These models were used to forecast six selected time series points. The results indicate that the BPNN model had Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) within 7.6 mm, while the GA-BP model errors were within 3.5 mm, and the ABC-BP model errors were within 3.7 mm. Both optimized models demonstrated significantly improved accuracy and good predictive capabilities.

Assessment of environmental geological disaster susceptibility under a multimodel comparison to aid in the sustainable development of the regional economy

Environmental geological disasters pose a significant threat to human life, property and environmental safety. It is necessary to conduct targeted governance in key prevention and control areas based on reasonable susceptibility assessment. Using the debris flow disaster in Xiuyan County as an example, this study compares and analyzes prone prediction models such as the frequency ratio (FR), decision tree (DT) and random forest (FR) models and evaluates the cost of prevention and control and the protection of life and property. The research results show that the FR, DT and RF models have good performance. The ROC test, disaster point density statistics and cross-validation results show that the RF model has the best performance. The study area was mainly less and mildly prone areas. The highly prone areas are mainly distributed in the northeast and southwest of the study area. It is the key area of disaster prevention and control. Elevation, rainfall intensity and population density have the largest influence on the susceptibility to debris flows. Based on the RF model, the disaster points in the highly prone areas account for 54% of the disaster points of the whole area, and the project treatment cost of the disaster points is 0.78 million yuan per single gully, which protects 56% of the lives and property in the study area, which is better than the DT and FR models. The RF model not only has good prediction performance in terms of susceptibility. It can realize the targeted management of disasters, achieve the targeted investment of governance costs and the effective protection of life and property and serve the sustainable development of the regional environment and economy with greater value.

Analysis on the susceptibility of environmental geological disasters considering regional sustainable development

Environmental geological disasters seriously threaten human lives and property. A reasonable analysis of the susceptibility to environmental geological disasters is the basis for disaster prevention and mitigation and can promote the sustainable development of regional economy. This study analyzes the susceptibility of environmental geological disasters, such as collapses, landslides, and debris flows in Helong City, China. Through investigation and comprehensive analysis, ten environmental, geological disaster causing factors, including stratum lithology, distance from the fault, elevation, slope, aspect, rainfall, distance from the water system, NDVI, distance from the road, and profile curvature, were extracted. Combined with GIS, a vulnerability analysis database of environmental geological disasters was established, and vulnerability zoning prediction was performed by using two models of information amount and a generalized regression neural network (GRNN). Then, disaster-vulnerability factors such as population density, road density, GDP, and land use type were added. The results show that the predicted results of the two models are similar to the actual survey results.. The environmental geological disasters in the study area are mainly low and not prone to occur, and the northeast and central areas are highly prone to environmental geological disasters, which are the key prevention and control areas in the study area. The coverage rate of high-vulnerability areas with a high degree of economic development is 8.63%, and the prediction results of the GRNN model are mostly distributed in spots and strips, which is more conducive to accurate disaster prevention and mitigation and cost reduction, promotes regional sustainable development, and has guiding significance for disaster prevention and control and urban planning and construction.