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In view of the problem of floor water inrush in the process of deep coal seam mining, propose to establish a risk assessment model for coal seam floor water inrush using GIS and combined weight TOPSIS method. Take coal 12-1 of level −950 in Donghuantuo Coal Mine as an example, the coal seam hosting thickness, coal seam burial depth, fault intensity index, aquifer thickness, water-rich aquifer thickness, first aquiclude thickness and second aquiclude thickness are taken as decisive indexes. Based on actual engineering exploration, the entropy weighted AHP weighted TOPSIS method is used to determine the partition threshold and classification level, analyse the risk of water inrush from coal seam floor, and visualise it based on GIS platform. The results show that the combined weight values of coal seam burial depth and fault scale index are 0.4008 and 0.2201, which have a significant impact on water inrush from the coal seam floor. The zoning threshold for the risk coefficient of water inrush from the 12–1 coal seam floor is 0.478, The overall water inrush risk of the mine field is less. Only a few areas in the southwest of the mine field are water inrush risk areas.

Urban land subsidence (LS) results in a reduction in ground elevation, compromising infrastructure integrity, disrupting the hydrological cycle, and posing significant risks to economic, demographic, and environmental security. This phenomenon is characterized by a certain degree of latency. In recent years, as Shanghai has undergone rapid urban expansion and high-density development, the issue of LS has become increasingly pronounced. This study employs a multi-criteria decision analysis framework, integrating advanced technologies such as Remote Sensing (RS), Google Earth Engine (GEE), and Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR), to develop a comprehensive evaluation index system comprising fifteen indicators, which consider geological, hydrological, and anthropogenic factors. By applying matrix theory, the study utilizes the Analytic Hierarchy Process (AHP) and the Entropy Weight Method (EWM) to integrate subjective and objective weights, thereby determining the comprehensive weight for each indicator. Subsequently, the comprehensive natural disaster risk theory was employed to assess the risk levels across different regions within the study area, which were visualized using ArcGIS. The study area was classified into five risk categories: very low, low, medium, high, and very high, comprising 67.00%, 17.87%, 9.25%, 3.39%, and 2.48% of the total area, respectively. The results closely align with historical cumulative subsidence data and the current LS prevention map of Shanghai, confirming the validity and efficacy of the selected indicators and evaluation methodologies. The findings suggest that the overall risk level in the study area is relatively low, with high-risk zones concentrated in densely populated and economically urbanized central districts.

Water resource carrying capacity is an important indicator for measuring sustainable development. Given the rapid economic and social development in China today, coordinating the sustainable development of water resources, socio-economy, and eco-environment has become an urgent problem to be solved. This study takes the Gansu section of Wei River mainstream basin (GWRB) as a case study and constructs a three-dimensional WRCC evaluation system and status standards. Based on this research framework, we analyzed the trends in WRCC changes of GWRB from 2008 to 2022. Additionally, we conducted an in-depth study of the internal relationships and influencing factors within the WRCC system. The results show that the combination weighting method of multi-weight models avoids the one-sidedness of single weighting, leading to a more realistic distribution of weights. The result status standard derived from the indicator status standard prevents a disconnect between the result and the status, making the evaluation more rational and accurate. The WRCC of the GWRB increased from 0.098 (overloaded) in 2008 to 0.621 (weakly bearable) in 2022. During this period, the eco-environmental system improved from critical to bearable, while the socio-economic system improved from overloaded to weakly bearable. Due to geographical and climatic limitations, the water resource system continued to bear significant pressure and remained in overloaded state for most of the time. The key factors limiting the further improvement of WRCC in the GWRB are per capita water resources, utilization rate of water resources, COD emission per 10,000 yuan of GDP, ecological water use rate, water consumption per 10,000 GDP and agricultural water use rate. To improve the WRCC, we propose a series of targeted recommendations based on the research findings. The proposed research framework can also serve as a reference for related studies in arid and semi-arid regions.

Optimal siting of electric vehicle charging stations (EVCSs) is crucial to the sustainable development of electric vehicle systems. Considering the defects of previous heuristic optimization models in tackling subjective factors, this paper employs a multi-criteria decision-making (MCDM) framework to address the issue of EVCS siting. The initial criteria for optimal EVCS siting are selected from extended sustainability theory, and the vital sub-criteria are further determined by using a fuzzy Delphi method (FDM), which consists of four pillars: economy, society, environment and technology perspectives. To tolerate vagueness and ambiguity of subjective factors and human judgment, a fuzzy Grey relation analysis (GRA)-VIKOR method is employed to determine the optimal EVCS site, which also improves the conventional aggregating function of fuzzy Vlsekriterijumska Optimizacijia I Kompromisno Resenje (VIKOR). Moreover, to integrate the subjective opinions as well as objective information, experts’ ratings and Shannon entropy method are employed to determine combination weights. Then, the applicability of proposed framework is demonstrated by an empirical study of five EVCS site alternatives in Tianjin. The results show that A3 is selected as the optimal site for EVCS, and sub-criteria affiliated with environment obtain much more attentions than that of other sub-criteria. Moreover, sensitivity analysis indicates the selection results remains stable no matter how sub-criteria weights are changed, which verifies the robustness and effectiveness of proposed model and evaluation results. This study provides a comprehensive and effective method for optimal siting of EVCS and also innovates the weights determination and distance calculation for conventional fuzzy VIKOR.

Gas accidents threaten the safety of underground coal mining, which are always accompanied by abnormal gas concentration trend. The purpose of this paper is to improve the prediction accuracy of gas concentration so as to prevent gas accidents and improve the level of coal mine safety management. Combining the LSTM model with the LightGBM model, the LSTM-LightGBM model is proposed with variable weight combination method based on residual assignment, which considers not only the time subsequence feature of data, but also the nonlinear characteristics of data. During the data preprocessing, the optimal parameters of gas concentration prediction are determined through the analysis of the Pearson correlation coefficients of different sensor data. The experimental results demonstrate that the mean absolute errors of LSTM-LighGBM, LSTM and LightGBM are 1.94%, 2.19% and 2.77%, respectively. The accuracy of LSTM-LightGBM variable weight combination model is better than that of the two above models, respectively. In this way, this study provides a novel idea and method for gas accident prevention based on gas concentration prediction.

Due to the high uncertainties in temperature changes, traditional regression analysis and time series prediction methods fail to provide accurate temperature forecasts to reduce the impact of extreme weather on human society. Considering the spatiotemporal features of temperature changes, this paper proposes a variable weight combination model based on a temporal graph convolutional network (T-GCN), Luong attention network (LUA) and gated recurrent unit (GRU) network, which fully utilizes spatiotemporal information to predict future temperature changes more accurately. The model uses the T-GCN model to capture spatiotemporal features while introducing Luong attention to weight the inputs at different time steps to improve the prediction accuracy and further reduce the prediction error by fusing the outputs of the T-GCN-Luong attention and GRU models through the variable weight combination method. The results revealed that (1) the inclusion of spatial information significantly improved the effectiveness of the temperature predictions. (2) The Luong attention mechanism weights different time steps and improves the prediction accuracy of the T-GCN model. (3) The TGLAG combination model constructed via the variable weight method exhibited good predictive performance at 15 sites. Compared with that of the simple GRU model, the accuracy of the proposed model is improved by approximately 31.949% in terms of the root mean square error (RMSE) and 26.913% in terms of the mean absolute error (MAE). Compared with the second-best model, T-GCN-Luong attention, the TGLAG model yields a 5.946% lower RMSE and 9.535% lower MAE, which indicates that TGLAG has good application prospects in the field of temperature prediction.

According to the natural condition of water resources and the economic, social, and ecological environment status of Zhangye City, the water resources carrying capacity of Zhangye City is evaluated by using the water resources carrying capacity Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) model with combination assignment. The results show that: (1) From 2010 to 2020, the water resources carrying capacity of Zhangye City was generally stable at the macro level, ranking at grades III and IV. However, from the micro level, the water resources carrying capacity fluctuates to a certain extent and shows an increasing trend year by year. (2) The steady improvement of economic and social conditions is the main driving force for the improvement of the comprehensive water resources carrying capacity of Zhangye City, and the changes in the ecological environment are also important factors affecting the carrying capacity of water resources. The results provided a decision basis for future comprehensive development and utilization of water resources in Zhangye City and a reference for water resource carrying capacity and water resource security assessment in other arid and semi-arid areas in our country.

The level of air pollution is reflected by the air quality index (AQI). People can use the AQI to organize their activities in a way that reduces or prevents exposure to air pollution altogether. Based on the AQI, governments, organizations, and businesses can also make plans to reduce air pollution. The multi-model ensemble has recently become a popular method for forecasting time series; however, it encounters the research problems of multi-parameter optimization and interaction analysis. To this end, a reduced-form ensemble of short-term air quality forecasting with the Sparrow search algorithm and decomposition error correction model is proposed in this paper. First, the data are decomposed using the CEEMDAN decomposition algorithm. Second, the Sparrow search algorithm is used in the model training process to obtain the optimal hyperparameters of the deep learning model and construct the optimal deep learning model. Next, the constructed models are used to predict the decomposed data, and the Lagrange multiplier method is used to determine the weights of each deep learning model. At last, the prediction results of each deep learning model are combined according to the weights to obtain the combined prediction results. Experiments show that (1) GRU, Bi-GRU, LSTM, and Bi-LSTM are used to predict the undecomposed data and the data decomposed by CEEMADN. The outcomes demonstrate that the CEEMDAN decomposition technique can enhance the accuracy of the forecast, specifically an 11.248% reduction in average RMSE and a 0.865% increase in average R 2 . (2) A multi-model combination method based on the Lagrange multiplier method is designed, which can obtain the weights of each deep learning model, and the weights can combine multiple models. The results of the multi-model combination are better than those of the single model. (3) The Lagrange multiplier method was compared with the simple average combination model and the MAE inverse combination model. The experimental results show that the results obtained using the Lagrange multiplier method are better than the other two.

Many tunnels in Western China are excavated through soft and water-rich rocks. Tunnel excavation in such regions is highly susceptible to disasters such as collapses and water and mud inrush. To control the risks associated with tunneling, this paper proposes a risk evaluation model applicable to soft and water-rich tunnels. First, geological data corresponding to typical soft and water-rich tunnels and related cases were analyzed. By analyzing the natural geology, tunnel characteristics, and construction management, ten influencing factors were selected as the risk evaluation indicators, and a risk evaluation hierarchy was established. Second, the improved combination weight method was applied to obtain the optimal weights of each indicator. A cloud model was then used to visualize the final risk level and establish an evaluation system for soft and water-rich surrounding rocks. Finally, the developed evaluation model was practically applied to a railway tunnel in Western China. The results were highly consistent with the actual situation and could play a guiding role in the construction process. This confirmed the reliability and applicability of the proposed model, which can also be used as a reference for other similar tunnels.

The increasing frequency and severity of forest fires, driven by climate change and intensified human activities, pose substantial threats to ecological security and sustainable development. However, most assessments remain centered on occurrence risk, lack a resilience-oriented perspective and comprehensive indicator systems, and therefore offer limited guidance for building system resilience. This study developed a forest fire resilience (FFR) assessment framework with 25 indicators in three levels and six domains across four resilience dimensions. Balancing expert judgment and data, we obtained indicator weights by integrating the Analytic Hierarchy Process (AHP) and the Criteria Importance Through Intercriteria Correlation (CRITIC) via a game-theoretic scheme. The analysis revealed that, among the level-2 indicators, climate factors, infrastructure, and vegetation characteristics exert the greatest influence on FFR. At the level-3 indicator scale, monthly minimum relative humidity, fine fuel load per unit area, and the deployment of smart monitoring systems were critical. Among the four resilience dimensions, absorption capacity plays the predominant role in shaping disaster response. Building on these findings, the study proposes targeted strategies to enhance FFR and applies the assessment framework to twelve administrative divisions of Baise City, China, highlighting marked spatial variability in resilience levels. The results offer valuable theoretical insights and practical guidance for strengthening FFR.