Search Results (135)

Natech events, involving multi-hazard coupling and cascading effects, pose serious threats to coal mine safety. This paper addresses flood-induced Natech scenarios in coal mining and introduces a two-stage cascading analysis framework based on hazard systems theory. A tri-layered network—comprising natural hazards, exposed elements, and secondary hazards—models hazard propagation. In Stage 1, an improved adjacency information entropy algorithm with multi-hazard coupling coefficients identifies critical exposed elements. In Stage 2, Dijkstra’s algorithm extracts key risk transmission paths. A dual-dimensional classification method, based on entropy and transmission risk, is then applied to prioritize emergency responses. This method integrates the criticality of exposed elements with the risk levels associated with secondary disaster propagation paths. Case studies validate the framework, revealing: (1) Hierarchical heterogeneity in the network, with surface facilities and surrounding hydrological systems as central hubs; shaft and tunnel systems and surrounding geological systems are significantly affected by propagation from these core nodes, exhibiting marked instability. (2) Strong risk polarization in secondary hazard propagation, with core-node-originated paths being more efficient and urgent. (3) The entropy-risk classification enables targeted hazard control, improving efficiency. The study proposes chain-breaking strategies for precise, hierarchical, and timely emergency management, enhancing coal mine resilience to flood-induced Natech events. Full article

Reservoir group flood control scheduling decision-making faces multiple uncertainties, such as dynamic fluctuations of evaluation indicators and conflicts in weight assignment. This study proposes a risk analysis model for the decision-making process: capturing the temporal uncertainties of flood control indicators (such as reservoir maximum water level and downstream control section flow) through the Long Short-Term Memory (LSTM) network, constructing a feasible weight space including four scenarios (unique fixed value, uniform distribution, etc.), resolving conflicts among the weight results from four methods (Analytic Hierarchy Process (AHP), Entropy Weight, Criteria Importance Through Intercriteria Correlation (CRITIC), Principal Component Analysis (PCA)) using game theory, defining decision-making risk as the probability that the actual safety level fails to reach the evaluation threshold, and quantifying risks based on the First-Order Second-Moment (FOSM) method. Case verification in the cascade reservoirs of the Qiantang River Basin of China shows that the model provides a risk assessment framework integrating multi-source uncertainties for flood control scheduling decisions through probabilistic description of indicator uncertainties (e.g., Zmax1 with μ = 65.3 and σ = 8.5) and definition of weight feasible regions (99% weight distribution covered by the 3σ criterion), filling the methodological gap in risk quantification during the decision-making process in existing research. Full article

Geological surveys have vital importance at the planning stage of dammed reservoir construction projects. The results of these surveys determine the majority of the technical solutions adopted in the construction design to ensure the proper safety and stability parameters of the structure during water damming. Where the ground type is found to be different from what is expected, the construction project may be delayed or even cancelled. This study analyses issues and design modifications caused by the identification of different soil conditions during the construction of four new flood control reservoirs in the Nysa Kłodzka River basin in south-western Poland. The key findings are as follows: (1) a higher density of exploratory boreholes in areas with potentially fractured rock mass is essential for selecting the appropriate anti-filtration protection; (2) when deciding to apply deep piles, it is reasonable to verify, at the planning stage, whether they can be installed using the given technology directly at the planned site; (3) inaccurate identification of foundation soils under the dam body can lead to significant design modifications—in contrast, a denser borehole grid helps to determine the precise elevation of the base layer, which is essential for reliably estimating the volume of material required for the embankment; (4) in order to correctly assess the soil deposits located, for instance, in the reservoir basin area, it is more effective to use test excavations rather than relying solely on borehole-based investigations—as a last resort, test excavations can be used to supplement the latter. Full article

Autonomous vehicles (AVs) rely on a heterogeneous sensor suite of RGB cameras, LiDAR, GPS/IMU, and emerging event-based dynamic vision sensors (DVS) to perceive and navigate complex environments. However, these sensors can be deceived by realistic cyberattacks, undermining safety. In this work, we systematically implement seven attack vectors in the CARLA simulator—salt and pepper noise, event flooding, depth map tampering, LiDAR phantom injection, GPS spoofing, denial of service, and steering bias control—and measure their impact on a state-of-the-art end-to-end driving agent. We then equip each sensor with tailored defenses (e.g., adaptive median filtering for RGB and spatial clustering for DVS) and integrate a unsupervised anomaly detector (EfficientAD from anomalib) trained exclusively on benign data. Our detector achieves clear separation between normal and attacked conditions (mean RGB anomaly scores of 0.00 vs. 0.38; DVS: 0.61 vs. 0.76), yielding over 95% detection accuracy with fewer than 5% false positives. Defense evaluations reveal that GPS spoofing is fully mitigated, whereas RGB- and depth-based attacks still induce 30–45% trajectory drift despite filtering. Notably, our research-focused evaluation of DVS sensors suggests potential intrinsic resilience advantages in high-dynamic-range scenarios, though their asynchronous output necessitates carefully tuned thresholds. These findings underscore the critical role of multi-modal anomaly detection and demonstrate that DVS sensors exhibit greater intrinsic resilience in high-dynamic-range scenarios, suggesting their potential to enhance AV cybersecurity when integrated with conventional sensors. Full article

Dams are critical infrastructures that provide essential services such as water supply, hydroelectric power generation, and flood control. As many dams age, the risk of structural failure increases, making safety assurance more urgent than ever. Traditional monitoring systems typically employ predictive models—based on techniques such as the finite element method (FEM) or machine learning (ML)—to compare real-time data against expected performance. However, these models often rely on static warning thresholds, which fail to reflect the dynamic conditions affecting dam behavior, including fluctuating water levels, temperature variations, and extreme weather events. This study introduces an adaptive warning threshold methodology for dam safety based on kernel density estimation (KDE). The approach incorporates a boosted regression tree (BRT) model for predictive analysis, identifying influential variables such as reservoir levels and ambient temperatures. KDE is then used to estimate the density of historical data, allowing for dynamic calibration of warning thresholds. In regions of low data density—where prediction uncertainty is higher—the thresholds are widened to reduce false alarms, while in high-density regions, stricter thresholds are maintained to preserve sensitivity. The methodology was validated using data from an arch dam, demonstrating improved anomaly detection capabilities. It successfully reduced false positives in data-sparse conditions while maintaining high sensitivity to true anomalies in denser data regions. These results confirm that the proposed methodology successfully meets the goals of enhancing reliability and adaptability in dam safety monitoring. This adaptive framework offers a robust enhancement to dam safety monitoring systems, enabling more reliable detection of structural issues under variable operating conditions. Full article

Small reservoirs in hilly areas serve as critical water conservancy infrastructure, playing an essential role in flood control, irrigation, and regional water security. However, dam-break events pose significant risks to downstream urban areas, threatening the sustainability and resilience of cities. This study takes Guangyuan City as a case study and employs numerical simulation methods—including dam-break modeling, hydrological modeling, and hydrodynamic modeling—to analyze the impact of dam-break floods on downstream urban regions. The results reveal that dam failure in small reservoirs can cause peak flood velocities exceeding 15 m/s, severely endangering urban infrastructure, ecosystems, and public safety. Additionally, for reservoirs with large catchment areas, dam-break floods combined with rainfall-induced flash floods may create compound disaster effects, intensifying urban flood risks. These findings underscore the importance of sustainable reservoir management and integrated flood risk strategies to enhance urban resilience and reduce disaster vulnerability. This research contributes to sustainable development by providing scientific insights and practical support for flood risk mitigation and resilient infrastructure planning in mountainous regions. Full article

Sluices play a critical role in flood control, power generation, water supply, etc. With decades of service, sluice safety assurance becomes a structural engineering imperative. Previous investigations have revealed that failures of sluices are often associated with seepage damage. To gain further insight into sluice seepage and ensure the safety of sluice structures, proposing an effective prediction method for sluice seepage nevertheless remains a challenging fundamental and practical perspective. Therefore, in this paper, a novel prediction model for sluice seepage based on impact factor screening methods, the improved Kepler optimization algorithm (IKOA) and the bidirectional gated recurrent unit (BiGRU), is presented. Primarily, the maximal information coefficient and the correlation-based feature selection (MIC–CFS) are introduced to screen the impact factors of the model, aiming to reduce redundant information and the complexity of the model. Subsequently, the Kepler optimization algorithm (KOA) is enhanced using three strategies: chaotic mapping-based initialization, Runge–Kutta-based position updating, and the enhanced solution quality (ESQ) strategy to optimize the hyperparameters of the BiGRU network. On this basis, the prediction model is established, which is applied in the Bengbu sluice to verify its fitting and prediction performance. Eventually, comparison analyses with a traditional stepwise regression model, IKOA–LSTM, and IKOA–GRU, were conducted based on monitoring sequences of three monitoring points. The coefficients of determination of the proposed model were located in the range of 0.974 to 0.988. Correspondingly, the mean absolute error values of the proposed model were the lowest, ranging from 0.074 to 0.064. The results of six evaluation metrics confirm that the proposed model consistently exhibits superior interpretability and is able to serve as a promising tool for sluice seepage prediction. Full article

To investigate the erosion and deposition evolution characteristics of the Xinqiman–Kelelik reach along the main stem of the Tarim River, this study analyzed river channel dynamics and planform morphological changes using Landsat satellite imagery (1993–2024) and hydrological data (water discharge and sediment load) from gauge stations. The results show that the thalweg line swings indefinitely in the river. The thalweg length increased by 29 km, while the mean channel width decreased by 0.28 km. The sinuosity index rose from 1.95 to 2.34, indicating a gradual intensification of channel curvature. The north bank is in a state of siltation, while the south bank is in a state of erosion. The riverbank exhibited an overall southward migration. The farmland area in the study area increased from 1510 hectares in 1993 to 5140 hectares in 2024. During this period, the thalweg near the water-diversion sluice continuously shifted toward the sluice side. To ensure flood protection safety for farmlands and villages on both banks, as well as ecological water diversion, river channel regulation and channel pattern control should be implemented. Full article

In response to the growing demand for improved operational efficiency in road and bridge networks, constructing parallel bridges in complex river sections has become a crucial strategy. This study focuses on a parallel bridge construction project in the Jinan section of the lower Yellow River, conducting physical model tests to investigate the unique aspects of the impacts of different pier shapes and spans on the flow characteristics of sediment-laden rivers under real-world engineering scenarios. The experimental results demonstrate that the hydraulic physical model of this river section that was constructed is reliable, with a relative error of <20% in sediment deposition, in the simulation of sediment erosion and deposition, flow velocity patterns, water levels, and riverbed morphological changes during parallel bridge construction in bridge-clustered river sections. The newly constructed bridges have a limited influence on the overall regime of this river section, with their impacts on both banks remaining within controllable limits, and the river flow remains largely stable. In areas with denser pier arrangements, the phenomenon of backwater upstream of the bridges is more pronounced, and under characteristic flood conditions, the newly built bridges amplify the water level differences between the upstream and downstream sections near the bridge sites. The ranges of influence of the water level drop downstream of the bridges increase, particularly in the main flow areas. Flow velocities generally increase in the main channel, while significant fluctuations are observed in the floodplain areas. Flood process experiments reveal that the slope at the junction between the main channel and the floodplain becomes gentler, with noticeable scouring occurring in the main channel. After flood events, the river tends to evolve toward a U-shaped channel, posing certain safety risks to the piers located at the junction of the floodplains and the main channel. This research methodology can serve as a reference for studying flow characteristics in similar parallel bridge construction projects in river sections, and the findings hold significant implications for practical engineering. Full article

Constructions located in rivers play a critical role in mitigating flood risks and supporting sustainable economic development. However, the specific impacts of bridge construction on local flood dynamics have not been thoroughly examined. This study addresses this research gap using hydrodynamic modeling with the one-dimensional MIKE11 module in MIKE Zero. A case study of the Nanyang (NY) Road Bridge in Zhejiang Province analyzed backwater effects at critical locations, including the Shili (SL) River outlet and Chengqing (CQ) Harbor. Unsteady flow simulations quantified changes in backwater height and backwater length upstream and downstream of the bridge, assessing their influence on flood conveyance capacity. The results indicate a narrowing of the river channel by approximately 4.8 m at the bridge location. Additionally, under flood conditions corresponding to 5-year, 10-year, and 20-year return periods, upstream water levels increased by 1 cm (6.53 m), 4 cm (7.15 m), and 5 cm (7.75 m), respectively. This research provides valuable insights and a scientific basis for developing flood control strategies, optimizing bridge design, and planning infrastructure projects, thereby enhancing regional flood safety and supporting sustainable economic development. Full article

In the case of extreme disasters such as local rainstorm and excessive flood, the safety risk analysis and prevention and control of cascade reservoirs face new challenges. Therefore, this article conducted a risk analysis based on typical watersheds and proposed a method for calculating the risk rate of overtopping in cascade reservoir groups, dynamically simulated the evolution process of overtopping floods in cascade reservoirs under different scenarios, delineated the scope of flood inundation, and evaluated the risk of overtopping of cascade reservoirs under different scenarios. Research has shown that dam failure floods in cascade reservoirs have both cumulative and cumulative effects, with scenario 3 being the most unfavorable. In scenario 3, the peak flow rates at the dam sites of each reservoir reached 24,500, 19,200, and 20,100 m³/s. According to the comprehensive risk assessment criteria, scenarios 1 and 2 are classified as moderate risks, while scenario 3 is classified as mild risk. Research has found that although the probability of dam overflow is extremely low, the high vulnerability calculated for each scenario indicates that a breach will cause significant social losses. This study can provide reference for the risk assessment of overtopping in cascade reservoirs and flood control and disaster reduction. Full article

The Internet of Things (IoT) offers transformative capabilities in enhancing public safety, disaster response, and emergency management by leveraging interconnected devices and real-time data. Through the IoT, smart sensors and networks are deployed across cities and environments to monitor critical parameters including air quality, structural integrity, and environmental changes. These systems provide early warnings for natural disasters such as earthquakes, floods, and wildfires, enabling authorities to respond proactively. In emergency management, IoT devices help coordinate resources and improve situational awareness during crises. Real-time data from wearable devices, smart infrastructure, and communication systems allow responders to track people, manage evacuations, and deploy resources more effectively. For example, IoT-enabled drones and autonomous vehicles are used to deliver supplies or assess damage in hazardous areas without risking human lives. IoT technologies improve post-disaster recovery by continuously monitoring areas for safety hazards and supporting infrastructure restoration. Smart traffic management systems assist in controlling traffic flow for emergency vehicles, while IoT-based communication networks ensure connectivity when traditional systems fail. The IoT significantly enhances the speed, accuracy, and effectiveness of disaster response and public safety operations, leading to the better protection of communities and faster recovery from emergencies. Full article

This study addresses the problems of multi-source data redundancy, insufficient feature capture timing, and delayed risk warning in the prediction of gas concentration in fully mechanized coal-mining operations by constructing a three-pronged technical approach that integrates feature dimensionality reduction, hybrid modeling, and intelligent early warning. First, sparse kernel principal component analysis (SKPCA) is used to accomplish the feature decoupling of multi-source monitoring data, and its optimal dimensionality reduction performance is verified using long-term and short-term neural networks (LSTMs). Second, an innovative TCN–Transformer hybrid architecture is proposed. The transient fluctuation characteristics of gas concentration are captured using causal dilation convolution, while a multi-head self-attention mechanism is used to analyze the cross-scale correlation of geological mining parameters. A flood optimization algorithm (FLA) is used to establish a hyperparameter collaborative optimization framework. Compared to TCN-LSTM, CNN-GRU, and other hybrid models, the hybrid model proposed in this study exhibits superior point prediction performance, with a maximum R² of 0.980988. Finally, a dynamic confidence interval is established using the locally weighted kernel density estimation (LWD-KDE) method with an optimized bandwidth, and an unsupervised early warning mechanism for the risk of gas concentration fluctuations in coal mines is constructed. The results provide a comprehensive approach to preventing and controlling gas disasters in fully mechanized mining operations. This research effectively promotes the transformation and upgrading of coal-mine-safety-monitoring systems to an active defense paradigm. Full article

Underground coal gasification (UCG) is a clean and automated coal technological process that has great potential. Environmental hazards such as the risk of ground surface subsidence, flooding, and water pollution are among the problems that restrict the application of UCG. Overburden rock stability above UCG cavities plays a key role in the prevention of the mentioned environmental hazards. It is necessary to optimize the safety pillar width to maintain rock stability and ensure minimal coal losses. This study focused on the investigation of the influence of pillar parameters on surface subsidence, taking into account the non-rectangular shape of the pillar and the presence of voids above the UCG reactor in the immediate roof. The main research was carried out using the finite element method in ANSYS 17.2 software. The results of the first simulation stage demonstrated that during underground gasification of a thin coal seam using the Controlled Retraction Injection Points method, with reactor cavities measuring 30 m in length and pillars ranging from 3.75 to 15 m in width, the surface subsidence and rock movement above gasification cavities remain within the pre-peak limits, provided the safety pillar’s bearing capacity is maintained. The probability of crack initiation in the rock mass and subsequent environmental hazards is low. However, in the case of the safety pillars’ destruction, there is a high risk of crack evolution in the overburden rock. In the case of crack formation above the gasification panel, the destruction of aquiferous sandstones and water breakthroughs into the gasification cavities become possible. The surface infrastructure is therefore at risk of destruction. The assessment of the pillars’ stability was carried out at the second stage using numerical simulation. The study of the stress–strain state and temperature distribution in the surrounding rocks near a UCG reactor shows that the size of the heat-affected zone of the UCG reactor is less than the thickness of the coal seam. This shows that there is no significant direct influence of the gasification process on the stability of the surrounding rocks around previously excavated cavities. The coal seam failure in the side walls of the UCG reactor, which occurs during gasification, leads to a reduction in the useful width of the safety pillar. The algorithm applied in this study enables the optimization of pillar width under any mining and geological conditions. This makes it possible to increase the safety and reliability of the UCG process. For the conditions of this research, the failure of coal at the stage of gasification led to a decrease in the useful width of the safety pillar by 0.5 m. The optimal width of the pillar was 15 m. Full article

Tailings ponds can recycle water resources through the water recirculation system by clarifying and purifying the wastewater discharged from the mining production process. Due to factors such as flooding and heavy rainfall, once a tailings dams burst, the spread of heavy metals in the tailings causes underground and surface water pollution, endangering the lives and properties of people downstream. To effectively assess the potential impact of tailings dams bursting, many problems such as the difficulty of taking values in predicting the volume of silt penetration through empirical formulae, model testing, and numerical simulation need to be solved. In this study, 65 engineering cases were collected to develop a sample dataset containing dam height and storage capacity. The Support Vector Machine (SVM) algorithm was used to develop a nonlinear regression model for tailings discharge volume after tailings dam failure. In addition, the model penalty parameter C and kernel function g were optimized using the powerful global search capability of the Slime Mold Algorithm (SMA) to develop an SMA–SVM prediction model for tailings discharge volume. The results indicate that the volume of tailings discharged increases nonlinearly with increasing dam height and tailings storage capacity. The SMA-SVM model showed higher prediction accuracy compared to the predictions made by the Random Forest (RF), Radial Basis Function (RBF), and Least Squares SVM (LS-SVM) algorithms. The average absolute error in tailings discharge volume compared to actual values was 30,000 m³, with an average relative error of less than 25%. This is very close to practical engineering scenarios. The ability of the SMA-SVM optimization algorithm to produce predictions with minimal error relative to actual values was further confirmed by the combination of numerical simulations. In addition, the numerical simulations revealed the flow characteristics and inundation area of the discharged sediment during tailings dam failure, and the research results can provide reference for water resource protection and downstream safety prevention and control of tailings ponds. Full article