Intelligent reservoir operation system based on evolving artificial neural networks

We propose a novel intelligent reservoir operation system based on an evolving artificial neural network (ANN). Evolving means the parameters of the ANN model are identified by the GA evolutionary optimization technique. Accordingly, the ANN model should represent the operational strategies of reservoir operation. The main advantages of the Evolving ANN Intelligent System (ENNIS) are as follows: (i) only a small number of parameters to be optimized even for long optimization horizons, (ii) easy to handle multiple decision variables, and (iii) the straightforward combination of the operation model with other prediction models. The developed intelligent system was applied to the operation of the Shihmen Reservoir in North Taiwan, to investigate its applicability and practicability. The proposed method is first built to a simple formulation for the operation of the Shihmen Reservoir, with single objective and single decision. Its results were compared to those obtained by dynamic programming. The constructed network proved to be a good operational strategy. The method was then built and applied to the reservoir with multiple (five) decision variables. The results demonstrated that the developed evolving neural networks improved the operation performance of the reservoir when compared to its current operational strategy. The system was capable of successfully simultaneously handling various decision variables and provided reasonable and suitable decisions.     

Conceptual fuzzy neural network model for water quality simulation

Artificial neural networks (ANNs) have been applied successfully in various fields. However, ANN models depend on large sets of historical data, and are of limited use when only vague and uncertain information is available, which leads to difficulties in defining the model architecture and a low reliability of results. A conceptual fuzzy neural network (CFNN) is proposed and applied in a water quality model to simulate the Barra Bonita reservoir system, located in the southeast region of Brazil. The CFNN model consists of a rationally‐defined architecture based on accumulated expert knowledge about variables and processes included in the model. A genetic algorithm is used as the training method for finding the parameters of fuzzy inference and the connection weights. The proposed model may handle the uncertainties related to the system itself, model parameterization, complexity of concepts involved and scarcity and inaccuracy of data. The CFNN showed greater robustness and reliability when dealing with systems for which data are considered to be vague, uncertain or incomplete. The CFNN model structure is easier to understand and to define than other ANN‐based models. Moreover, it can help to understand the basic behaviour of the system as a whole, being a successful example of cooperation between human and machine. Copyright © 2006 John Wiley & Sons, Ltd.     

Artificial neural networks and fuzzy logic based control of spillway gates of dams

In this paper a new and efficient control method based on fuzzy logic is proposed for real‐time operation of spillway gates of a reservoir during any flood of any magnitude up to the probable maximum flood. Artificial neural networks are used to model the non‐linear relationship among the main variables of the reservoir under consideration. In order to demonstrate the performance of the proposed method, we simulate the control system using different probable overflow hydrographs. The results of the proposed control method are compared with the results of the conventional control methods. The results obtained from the simulations indicate that the proposed method exhibits superior performance over the conventional reservoir flood operation, with much more flexible results. Copyright © 2004 John Wiley & Sons, Ltd.     

Stormwater quantity and quality response to climate change using artificial neural networks

This research investigates the potential impacts of climate change on stormwater quantity and quality generated by urban residential areas on an event basis in the rainy season. An urban residential stormwater drainage area in southeast Calgary, Alberta, Canada is the focus of future climate projections from general circulation models (GCMs). A regression‐based statistical downscaling tool was employed to conduct spatial downscaling of daily precipitation and daily mean temperature using projection outputs from the coupled GCM. Projected changes in precipitation and temperature were applied to current climate scenarios to generate future climate scenarios. Artificial neural networks (ANNs) developed for modelling stormwater runoff quantity and quality used projected climate scenarios as network inputs. The hydrological response to climate change was investigated through stormwater runoff volume and peak flow, while the water quality responses were investigated through the event mean value (EMV) of five parameters: turbidity, conductivity, water temperature, dissolved oxygen (DO) and pH. First flush (FF) effects were also noted. Under future climate scenarios, the EMVs of turbidity increased in all storms except for three events of short duration. The EMVs of conductivity were found to decline in small and frequent storms (return period < 5 years); but conductivity EMVs were observed to increase in intensive events (return period ≥ 5 years). In general, an increasing EMV was observed for water temperature, whereas a decreasing trend was found for DO EMV. No clear trend was found in the EMV of pH. In addition, projected future climate scenarios do not produce a stronger FF effect on dissolved solids and suspended solids compared to that produced by the current climate scenario. Copyright © 2010 John Wiley & Sons, Ltd.     

Probabilistic reservoir operation using Bayesian stochastic model and support vector machine

Available water resources are often not sufficient or too polluted to satisfy the needs of all water users. Therefore, allocating water to meet water demands with better quality is a major challenge in reservoir operation. In this paper, a methodology to develop operating strategies for water release from a reservoir with acceptable quality and quantity is presented. The proposed model includes a genetic algorithm (GA)-based optimization model linked with a reservoir water quality simulation model. The objective function of the optimization model is based on the Nash bargaining theory to maximize the reliability of supplying the downstream demands with acceptable quality, maintaining a high reservoir storage level, and preventing quality degradation of the reservoir. In order to reduce the run time of the GA-based optimization model, the main optimization model is divided into a stochastic and a deterministic optimization model for reservoir operation considering water quality issues.The operating policies resulted from the reservoir operation model with the water quantity objective are used to determine the released water ranges (permissible lower and upper bounds of release policies) during the planning horizon. Then, certain values of release and the optimal releases from each reservoir outlet are determined utilizing the optimization model with water quality objectives. The support vector machine (SVM) model is used to generate the operating rules for the selective withdrawal from the reservoir for real-time operation. The results show that the SVM model can be effectively used in determining water release from the reservoir. Finally, the copula function was used to estimate the joint probability of supplying the water demand with desirable quality as an evaluation index of the system reliability. The proposed method was applied to the Satarkhan reservoir in the north-western part of Iran. The results of the proposed models are compared with the alternative models. The results show that the proposed models could be used as effective tools in reservoir operation.     

Adaptive neuro-fuzzy inference system for prediction of water level in reservoir

Accurate prediction of the water level in a reservoir is crucial to optimizing the management of water resources. A neuro-fuzzy hybrid approach was used to construct a water level forecasting system during flood periods. In particular, we used the adaptive network-based fuzzy inference system (ANFIS) to build a prediction model for reservoir management. To illustrate the applicability and capability of the ANFIS, the Shihmen reservoir, Taiwan, was used as a case study. A large number (132) of typhoon and heavy rainfall events with 8640 hourly data sets collected in past 31 years were used. To investigate whether this neuro-fuzzy model can be cleverer (accurate) if human knowledge, i.e. current reservoir operation outflow, is provided, we developed two ANFIS models: one with human decision as input, another without. The results demonstrate that the ANFIS can be applied successfully and provide high accuracy and reliability for reservoir water level forecasting in the next three hours. Furthermore, the model with human decision as input variable has consistently superior performance with regard to all used indexes than the model without this input.     

Applying wavelet transformation and artificial neural networks to develop forecasting-based reservoir operating rule curves

ABSTRACT

In order to provide more accurate reservoir-operating policies, this study attempts to implement effective monthly forecasting models. Seven inflow forecasting schemes, applying discrete wavelet transformation and artificial neural networks are proposed and provided to forecast the monthly inflow of Dez Reservoir. Based on some different performance indicators the best scheme is achieved comparing to the observed data. The best forecasting model is coupled with a simulation-optimization framework, in which the performance of five different reservoir rule curves can be compared. Three applied rules are based on conventional Standard operation policy, Regression rules, and Hedging rule, and two others are forecasting-based regression and hedging rules. The results indicate that forecasting-based operating rule curves are superior to the conventional rules if the forecasting scheme provides results accurately. Moreover, it can be concluded that the time series decomposition of the observed data enhances the accuracy of the forecasting results efficiently.     

Intelligent control for modeling of real‐time reservoir operation,part II: artificial neural network with operating rule curves

To bridge the gap between academic research and actual operation, we propose an intelligent control system for reservoir operation. The methodology includes two major processes, the knowledge acquired and implemented, and the inference system. In this study, a genetic algorithm (GA) and a fuzzy rule base (FRB) are used to extract knowledge based on the historical inflow data with a design objective function and on the operating rule curves respectively. The adaptive network‐based fuzzy inference system (ANFIS) is then used to implement the knowledge, to create the fuzzy inference system, and then to estimate the optimal reservoir operation. To investigate its applicability and practicability, the Shihmen reservoir, Taiwan, is used as a case study. For the purpose of comparison, a simulation of the currently used M‐5 operating rule curve is also performed. The results demonstrate that (1) the GA is an efficient way to search the optimal input–output patterns, (2) the FRB can extract the knowledge from the operating rule curves, and (3) the ANFIS models built on different types of knowledge can produce much better performance than the traditional M‐5 curves in real‐time reservoir operation. Moreover, we show that the model can be more intelligent for reservoir operation if more information (or knowledge) is involved. Copyright © 2004 John Wiley & Sons, Ltd.     

辽宁省大伙房水库及入库河流水质空间特征与河库水质关系

入库河流与水库存在空间上的连续性,河流污染物输入是水库水质恶化的主要原因,对大伙房水库及其入库支流61个采样点的水质状况进行调查,并运用聚类分析和主成分分析对大伙房水库及入库支流的水质空间特性和主要污染物进行分析.聚类分析显示,按照水质相似性将大伙房水库及入库支流水质可分为上游区、下游区和库区3个典型空间区域.分别对3个区域进行主成分分析,结果显示:入库支流上游区和下游区水质主要影响因素为氨氮、总氮和化学需氧量,库区影响水质的主要因素为温度、p H值、浊度、溶解氧、电导率、氨氮和总氮.对上游、下游和库区水质均有显著影响的因子为氨氮和总氮,上游区、下游区和库区氨氮浓度均值分别为0.06、0.10和0.19 mg/L,总氮浓度均值分别为0.13、0.16和0.26 mg/L.入库河流下游区对水库水质影响较大,受社河和浑河污染物输入的影响,大伙房水库水质在空间上呈现社河入库区水质优于浑河入库区水质.并且库区氨氮和总氮浓度均与距岸边距离呈负相关,溶解氧和p H值均与距入库口距离呈负相关,表明入库河流污染物输入和环库区面源污染均对大伙房水库水质产生一定影响.     

基于湖库水质目标的流域氮、磷减排与分区管理——以天目湖沙河水库为例

湖库水环境保护在保障生产与生活用水、维系生态平衡、发展旅游等方面发挥着重要的作用.水质目标管理是保护湖库水质的最佳管理办法.本文以天目湖地区沙河水库及其流域为研究区域,建立模型模拟沙河水库流域的水文与水质,评估入库污染通量和主要来源;依据水质目标测算氮、磷污染的容量和减排量,结合土地的生态保护与开发适宜性评估,提出氮、磷污染分区减排和土地管控的对策和措施.研究结果表明,沙河水库氮、磷污染物入库通量分别为206.01和3.29 t/a,面源总氮和总磷分别占总入库量的85.7%和67.5%.不同土地利用类型氮、磷输出强度有显著差异,总氮输出强度依次为茶园 >耕地 >建筑用地 >裸地 >草地 >退耕地 >林地 >河湖漫滩,总磷输出强度与地表覆盖度有关,依次为裸地 >建筑用地 >茶园 >耕地 >草地 >退耕地 >林地和河湖漫滩.从氮、磷输移过程来看,沙河水库流域总氮排放量为321.64 t/a,进入河流的为255.53 t/a,在河道输送过程中损失19.4%,最终有206.01 t/a进入水库;沙河水库流域总磷排放量为13.42 t/a,进入河流的为7.90 t/a,在河道输送过程中损失58.3%,最终有3.29 t/a进入水库.不同分区河流氮、磷滞留降解率有很大的差异,中田河总氮、总磷滞留降解能力最强,分别为34.71%和84.31%.2009年的通量计算结果显示,沙河水库总氮达到Ⅳ类水质目标需要的入湖减少量为32.01 t/a,入湖削减比例为15.50%,总氮达到Ⅲ类水质目标需要的入湖减少量为59.66 t/a,入湖削减比例为29.00%;总磷达到Ⅲ类水需要的入湖减少量为0.682 t/a,入湖削减比例为20.70%,总磷达到Ⅱ类水需要的入湖减少量为1.479 t/a,入湖削减比例为44.90%.为了实现基于土地利用的面源污染减排管控,选定植被覆盖度、水源涵养能力、地形坡度、土地利用、氮磷分区贡献量、与道路和村落距离等指标综合评估生态保护价值和开发适宜性,并划定禁止开发区、限制开发区和保护性开发区3个管理分区,最终确定各分区的开发强度限制和管控方式.