Multi-objective reservoir operation of reservoir flood control involves numerous factors and complex model solving, and exploring effective methods for solving the operation models has always been a hot topic in reservoir optimization operation research. The Multi-Objective Ant Lion Algorithm (MOALO) is an emerging heuristic intelligent optimization algorithm, but it has not yet been applied in reservoir optimization operation. Testing the effectiveness of this method on multi-objective reservoir scheduling and further improving the optimization performance of this method is of great significance for enhancing the overall benefits of reservoir operation. In this study, MOALO is applied to the optimal scheduling of reservoir flood control. To increase the search efficiency of MOLAO, the advanced MOALO method (AMOLAO) is proposed by reconstructing the search distribution in MOALO using a power function. Taking the Songshu Reservoir and Dongfeng Reservoir in the Fuzhou River Basin in Dalian City as an example, MOALO, AMOLAO, and other two traditional methods are applied for solving the multi-objective reservoir operation problem. Results show that the AMOALO method has high search efficiency, strong optimization ability, and good stability. AMOALO performs better than MOALO and the two traditional methods. The study provides an efficient method for solving the problems in multi-objective reservoir operation.

Groundwater pollution has become a global challenge, posing significant threats to human health and ecological environments. Machine learning, with its superior ability to capture non-linear relationships in data, has shown significant potential in addressing the groundwater pollution issues. This review presents a comprehensive bibliometric analysis of 1,462 articles published between 2000 and 2023, offering an overview of the current state of research, analyzing development trends, and suggesting future directions. The analysis reveals a growing trend in publications over the 24-year period, with a sharp expansion since 2020. China, the USA, India, and Iran are identified as the leading contributors to publications and citations, with prominent institutions such as Jilin University, the United States Geological Survey, and the University of Tabriz. Moreover, keyword frequency analysis indicates that principal component analysis (PCA) is the most commonly used method, followed by artificial neural network (ANN) and hierarchical clustering analysis (HCA). The most studied groundwater pollutants include nitrate, arsenic, heavy metals, and fluoride. As machine learning has rapidly advanced, research focuses have evolved from basic tasks like hydrochemical evolution analysis, water quality index evaluation, and groundwater vulnerability assessments to more complex issues, such as pollutant concentration prediction, pollution risk assessment, and pollution source identification. Despite these advances, challenges related to data quality, data scarcity, model generalization, and interpretability remain. Future research should prioritize data sharing, improving model interpretability, broadening research horizons and advancing theory-guided machine learning. These will enhance our understanding of groundwater pollution mechanisms, and ultimately facilitate more effective pollution control and remediation strategies. In summary, this review provides valuable insights and suggestions for researchers and policymakers working in this critical field.

Joint operation of the reservoir system can greatly improve flood water utilization and alleviate the contradiction between water supply and water demand. But yet, the flood risk brought by forecast uncertainties transfers between the reservoirs and propagates from upstream to downstream, which is crucial for flood water utilization but is rarely investigated. To address this issue, this study proposes a FLWL optimal control framework, which consists of three modules: the FLWL upper bound derivation module, the flood risk analysis module, and the optimal FLWL decision-making module. The first module is to derive the upper bound relationship of the flood limited water level (FLWL), i.e., the specific substitution relationship of storage, for the parallel reservoirs coupling the capacity-constrained pre-release method and the aggregation-decomposition method. In the second module, the method to calculate the flood risk of the reservoirs and downstream caused by rainfall and runoff forecast uncertainties based on the total probability formula is proposed. In the third module, the transmission and propagation of flood risks in the parallel reservoir system under different rainfall grades are quantified, based on which the optimal FLWLs are determined. The implication in the Qinghe-Chaihe parallel reservoir system located in Northeast China shows that FLWL in Qinghe Reservoir and Chaihe Reservoir can be raised to 129.5 m from 127 m and 108 m from 104 m, respectively. Controlling the FLWL at any point in the safety domain can improve both flood water utilization and flood control benefits. However, the flood risk nonlinearly transfers between the parallel reservoirs and nonlinearly propagates from the upstream to the downstream. Based on this relationship, the optimal FLWLs of the reservoirs under different decision preferences are recommended.

The very nature of weather forecasts and verifications and the way they are used make it impossible for one single or absolute standard of evaluation. However, little research has been conducted on verifying deterministic multi‐category forecasts, which is based on the attribute of uncertainty. The authors propose a new approach using two mutual information theory‐based scores for assessing the comprehensive uncertainty of all categories and the uncertainty for a certain category in deterministic multi‐category precipitation forecasts, respectively. Specifically, the comprehensive uncertainty is defined as the average reduction in uncertainty about the observations resulting from the use of a predictive model to provide all categories forecasts; the uncertainty of a certain category is defined as the reduction in uncertainty about the observations resulting from the use of a predictive model to provide a certain category forecast. By applying the proposed approach and traditional verification methods, the four precipitation forecasting products from the China Meteorological Administration, European Centre for Medium‐Range Weather Forecasts, National Centers for Environmental Prediction, and United Kingdom Meteorological Office were verified in the Dahuofang Reservoir Drainage Basin, China. The results indicate that: (a) the proposed approach can better capture the changing patterns of uncertainties with lead times and distinguish the forecasting performance among different forecast products; (b) the proposed approach is resistant to the extreme bias; (c) the proposed approach needs a careful choice of bin width; and (d) the bias analysis is necessary before verifying the uncertainties in precipitation forecasts.

Risk analysis is vital for reservoir flood control operation considering forecast because forecast uncertainties may create risks of multiple hazard events, including the single-hazard events, the union and intersection of single-hazard events (UHE and IHE). The probability of UHE and IHE are the major concern of decision-makers. This research proposes an analytical flood risk probability calculation framework for single-hazard event, UHE and IHE caused by uncertainties in flood forecasting and takes Dahuofang Reservoir, located in the Hunhe River basin, Northeast China, as a case study. In the framework, the risk is calculated by the integral of the joint probability density function (Pdf) of the risk sources over the hazard domain, which is the collection of the values of risk sources that cause hazards. The determination method of the hazard domain and the Pdf of risk sources and the procedure of risk analysis are elaborated. Results of single-hazard events show that the proposed methodology is of higher precision compared with the risk analysis method based on the law of total probability. Meanwhile, the flood risks of UHE and IHE are calculated for the reservoir operation under different levels of flood limit water level. The study provides a new method of flood risk analysis for flood control reservoir operation.