Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville–Thermalito Complex (OTC) – a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation–storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California.     

Modeling of Water Resources Allocation and Water Quality Management for Supporting Regional Sustainability under Uncertainty in an Arid Region

In this study, a scenario-based interval-stochastic fraticle optimization with Laplace criterion (SISFL) method is developed for sustainable water resources allocation and water quality management (WAQM) under multiple uncertainties. SISFL can tackle uncertainties presented as interval parameters and probability distributions; meanwhile, it can also quantify artificial fuzziness such as risk-averse attitude in a decision-making issue. Besides, it can reflect random scenario occurrence under the supposition of no data available. The developed method is applied to a real case of water resources allocation and water quality management in the Kaidu-kongque River Basin, where encounter serve water deficit and water quality degradation simultaneously in Northwest China. Results of water allocation pattern, pollution mitigation scheme, and system benefit under various scenarios are analyzed. The tradeoff between economic activity and water-environment protection with interval necessity levels and Laplace criterions can support policymakers generating an effective and robust manner associated with risk control for WAQM under multiple uncertainties. These discoveries avail local policymakers gain insight into the capacity planning of water-environment to satisfy the basin’s integrity of socio-economic development and eco-environmental sustainability.     

基于SWMM与NSGA-Ⅱ耦合的城区雨水泵站多目标优化调控

针对城区复杂水系统中雨水泵站的调控系统性弱和优化时效性慢的问题,将SWMM的水量水质过程模拟与NSGA-Ⅱ的快速非支配排序进行深度耦合,从减缓城区内涝、控制面源污染负荷输出与减低耗电量的角度,建立了多线程并行计算和带有决策者偏好的城区雨水泵站多目标优化调控模型。对天津空港经济区的雨水泵站优化调度进行实例研究,结果表明,与常规调度方式相比,在典型设计降水情景下,该优化模型在不显著增加节点积水累计时间的前提下,减少输出了1383.34 kg的污染负荷,节约了621.92 kW·h电能,求解速率可提升至单线程串行计算的4.2倍。该优化方案充分利用了泵站调蓄池的库容缓冲和水质稀释作用,减缓了场次降水对于城区水环境的瞬时冲击影响,降低了运行成本。     

A novel deep learning prediction model for concrete dam displacements using interpretable mixed attention mechanism

Dam displacements can effectively reflect its operational status, and thus establishing a reliable displacement prediction model is important for dam health monitoring. The majority of the existing data-driven models, however, focus on static regression relationships, which cannot capture the long-term temporal dependencies and adaptively select the most relevant influencing factors to perform predictions. Moreover, the emerging modeling tools such as machine learning (ML) and deep learning (DL) are mostly black-box models, which makes their physical interpretation challenging and greatly limits their practical engineering applications. To address these issues, this paper proposes an interpretable mixed attention mechanism long short-term memory (MAM-LSTM) model based on an encoder-decoder architecture, which is formulated in two stages. In the encoder stage, a factor attention mechanism is developed to adaptively select the highly influential factors at each time step by referring to the previous hidden state. In the decoder stage, a temporal attention mechanism is introduced to properly extract the key time segments by identifying the relevant hidden states across all the time steps. For interpretation purpose, our emphasis is placed on the quantification and visualization of factor and temporal attention weights. Finally, the effectiveness of the proposed model is verified using monitoring data collected from a real-world dam, where its accuracy is compared to a classical statistical model, conventional ML models, and homogeneous DL models. The comparison demonstrates that the MAM-LSTM model outperforms the other models in most cases. Furthermore, the interpretation of global attention weights confirms the physical rationality of our attention-based model. This work addresses the research gap in interpretable artificial intelligence for dam displacement prediction and delivers a model with both high-accuracy and interpretability.     

基于河道径流可变区间的河流水资源可开发利用率分析

为了确定水电开发活动对河流水资源的可开发利用率,建立分期展布的河道径流可变区间核算方法,其中河道径流包括河道最小生态需水和河道最大洪流,并以西藏拉萨河为例进行核算。结果表明:拉萨河河道最小生态需水和最大洪流年内动态变化分别为29.5~328.3 m3/s和95.1~1 673.4 m3/s。与河道最小生态需水约束相比,河道径流可变区间约束使得拉萨河年调节型水电开发的水资源可开发利用率从60.2%下降到18.7%。指出对于径流丰枯特征十分明显的季节性河流,大型水利工程在平衡径流季节分布的过程中,应该受河道径流可变区间约束,尤其是枯水期最大洪流约束下河道径流量的可增加空间。     

Deriving operating policies for multi-objective reservoir systems: Application of Self-Learning Genetic Algorithm

Optimal multi-reservoir operation is a multi-objective problem in nature and some of its objectives are nonlinear, non-convex and multi-modal functions. There are a few areas of application of mathematical optimization models with a richer or more diverse history than in reservoir systems optimization. However, actual implementations remain limited or have not been sustained.Genetic Algorithms (GAs) are probabilistic search algorithms that are capable of solving a variety of complex multi-objective optimization problems, which may include non-linear, non-convex and multi-modal functions. GA is a population based global search method that can escape from local optima traps and find the global optima. However GAs have some drawbacks such as inaccuracy of the intensification process near the optimal set.In this paper, a new model called Self-Learning Genetic Algorithm (SLGA) is presented, which is an improved version of the SOM-Based Multi-Objective GA (SBMOGA) presented by Hakimi-Asiabar et al. (2009) [45]. The proposed model is used to derive optimal operating policies for a three-objective multi-reservoir system. SLGA is a new hybrid algorithm which uses Self-Organizing Map (SOM) and Variable Neighborhood Search (VNS) algorithms to add a memory to the GA and improve its local search accuracy. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm can enhance the local search efficiency in the Evolutionary Algorithms (EAs).To evaluate the applicability and efficiency of the proposed methodology, it is used for developing optimal operating policies for the Karoon-Dez multi-reservoir system, which includes one-fifth of Iran's surface water resources. The objective functions of the problem are supplying water demands, generating hydropower energy and controlling water quality in downstream river.     

Identification of relationship between sunspots and natural runoff in the Yellow River based on discrete wavelet analysis

Annual natural runoff is an important index of a river, which may be affected by solar activities. In this study, 304 years of annual natural runoff at the Sanmenxia station located in the Yellow River and the sunspot relative number are decomposed with the application of a Complex Morlet. According to the results of real part, modulus and second power of modulus, the annual runoff series at the Sanmenxia station has an obvious periodic oscillation on 90–100, 50–80, 35–50, 15–35, about 10, and less than 10-year scales. Also, there are obvious periodic variability with 60–90 years, 30–50 years and about 10 years. There are two centers of energy: one is about 1840–1850 on 7–11-year scale and the other is about 1825–1925 on 60–70-year scale. From the wavelet variance, 3, 26, 46, 68 year periods are detected within a 100-year scale, and the 68-year period is the most significant. Similar analyses are conducted for the sunspot relative number within the same period 1700–2003. The sunspot series shows 11- and 60-year period variation, as well as eight energy centers. Then, the correlation analyses for 11- and 60-year serial scales are computed. From a long-term period (1700–2003) view, there is no notable correlation between the natural runoff and the sunspot relative number; however, it is evident that the correlations exist within a short-term period. The results also indicate that the relationships between solar activities and the natural runoff in the Yellow River are complicated.     

MULTISTAGE ANALYSIS OF HYDROLOGIC ALTERATIONS IN THE YELLOW RIVER,CHINA

Natural river flow regimes provide an array of ecological and social functions by sustaining the health of riverine ecosystems. To identify the hydrologic alterations in the lower Yellow River basin caused by natural factors and human activities, we developed multistage hydrologic analysis to investigate the temporal variability of the river's flow regimes. We used a cumulative departure curve and Mann–Whitney–Pettitt nonparametric tests to determine possible change points based on hydrologic data from 1950 to 2006. We then used the range of variability approach to characterize and to quantify the temporal variability of the hydrologic regimes that were associated with perturbations such as dam operation, flow diversions or intensive conversion of land use within the watershed. In the case study, three stages in hydrologic alterations of the flow regime were found: a stage without human impacts, a stage with excessive human impacts and a reservoir‐regulation stage. Our results indicated that (i) after 1997, dam operation efficiently achieved flood control using sediment regulation activities; (ii) although effective in flood control, the Xiaolangdi Reservoir could not handle situations with extremely low flow, such as during droughts; and (iii) under the arid climate of the Yellow River basin, water consumption by agriculture was the main cause of water shortages. The current study shows that multistage hydrologic analysis can greatly assist regional water resources management and the restoration of riparian eco‐environmental systems affected by dam construction under a changing environment. Copyright © 2012 John Wiley & Sons, Ltd.     

土壤—植物系统净化地表径流非点源 污染物实验研究

利用自行设计的6 m土槽构建土壤—植物系统,以无植被土槽为空白对照,设置不同的污染物进水浓度,采用模拟径流方式进行非点源污染净化实验。结果表明：（1）植被的存在能有效滞缓径流和提高污染物去除率,其中土壤—高羊茅系统的净化效果略优于土壤—紫花苜蓿系统,其SS、NO-3-N、NH3-N、TDP、PP去除率分别达到86.61%、25.83%、52.03%、26.53%及76.59%;（2）在本研究的进水浓度范围内,浓度变化对处理系统净化效果的影响与污染物存在状态有关,进水浓度增加后颗粒态污染物去除率无显著变化而溶解态氮去除率均出现明显下降;（3）以污染物出水浓度随径流长度的变化表征污染物截留特征,SS和PP出水浓度均随径流长度增加而呈指数降低,NO-3-N、NH3-N和TDP出水浓度随径流长度增加而呈线性降低,植被及进水浓度条件对污染物截留特征无明显影响。