With the development of modernization, traditional fossil energy reserves are decreasing, and the power industry, as one of the main energy consumption forces, has begun to pay attention to increasing the proportion of clean energy generation. With the deepening of electrification, the peak-valley difference of residential electricity consumption increases, but photovoltaic and wind power generation have fluctuations and are manifested as reverse peak regulation. Thermal power plants as the main force of peak regulation gradually reduce the market share, making nuclear power plants bear the heavy responsibility of participating in peak regulation. The traditional method of adjusting operating power by inserting and removing control rods has great safety risks and wastes resources. Therefore, this paper proposes a new energy storage system that can keep the nuclear power plant running at full power and produce hydrogen to synthesize ammonia from excess power. A comprehensive evaluation model of energy storage based on z-score data standardization and objective parameter assignment AHP (analytic hierarchy process) analysis method was established to evaluate energy storage systems according to a multi-index system. With an AP1000 daily load tracking curve as the input model, the simulation model built by Aspen Plus V14 was used to calculate the operating conditions of the system. In order to provide a construction basis for practical engineering use, Haiyang Nuclear Power Plant in Shandong Province is taken as an example. The system layout scheme is proposed according to the local environmental conditions. The accident tree analysis method is combined with ALOHA 5.4.1.2 (Areal Locations of Hazardous Atmospheres) hazardous chemical analysis software and MARPLOT 5.1.1 geographic information technology. A qualitative and quantitative assessment of risk factors and the consequences of leakage, fire, and explosion accidents caused by hydrogen and ammonia storage processes is carried out to provide guidance for accident prevention and emergency rescue. The design of an “Electric-Hydrogen-Ammonia” energy storage system proposed in this paper provides a new idea for zero-carbon energy storage for the peak shaving of nuclear power plants and has a certain role in promoting the development of clean energy.

Landslides are one of the most widespread and hazardous geologic hazards in the world. Landslide susceptibility mapping (LSM) is an effective way to identify landslide-prone areas to prevent and reduce landslide hazards. However, the accuracy of LSM is greatly limited by the balance of landslide data. The collection of high-quality landslide inventories is labor-intensive. In this paper, four data enhancement techniques are used to correct the unbalanced landslide dataset based on GIS. The method is applied to LSM in Guangzhou City. And the difference in the accuracy of the enhancement is compared with two assessment models, SVM and RF. The experimental results show that the data enhancement technique helps to improve the accuracy of the evaluated models, and the evaluation results of all models show the best performance by GAN-RF. This study shows that the balance of landslide data greatly affects the accuracy of the assessment model. And the data enhancement technique enhances the robustness of the assessment model and improves the accuracy of the prediction results.

Ground collapse is one of the common geological hazards in modern cities. With the development of urbanization, the risk of ground collapse increases, which has a great impact on urban public safety. Ground collapse accidents typically occur due to the presence of unstable cavities under the surface, or the generation and expansion of cavities induced by triggering factors. Investigating the stability of cavities in the strata is significant for identifying subsidence risks and mitigating the consequences of subsidence. This study proposed a method for predicting ground subsidence settlement based on the ARMA model. Firstly, CFD-DEM coupled simulation is employed to simulate the mechanism of cavity changes in the soil layers under the influence of triggering factors and to calculate the safety coefficient for ground subsidence stability. Subsequently, the safety coefficient data at different time points are fitted to predict the subsequent stability of the subsidence. We selected a subway permeable collapse accident in Foshan City, Guangdong Province for experimental verification, and compared the predicted results with the actual situation. The result shows that this method can effectively predict the changes in ground collapse safety factor and the collapse time point. With 40% of the data, high accuracy prediction can be achieved, improving the efficiency of collapse evolution prediction and providing strong support for ground collapse risk prevention and control.

Urban road collapse is a common disaster in modern cities, posing a severe threat to the safety of life and property of urban residents. Effective risk assessment is crucial for preventing collapses. This study proposes a novel method for assessing the risk of urban road collapse by integrating Interferometric Synthetic Aperture Radar (InSAR) technology with Long Short-Term Memory (LSTM) networks. We conduct experimental analysis using collapse incidents and corresponding subsidence maps from five areas in Guangzhou. The results demonstrate that the proposed model successfully establishes a mapping relationship between the temporal features of InSAR data and the risk of road collapse, enabling a risk assessment of specific areas solely based on InSAR data. This model overcomes the difficulty of obtaining numerous assessment indicators in traditional risk assessment methods, providing strong support for the prevention and control of regional road collapse risks.