A Model for Optimal Allocation of Water to Competing Demands

The present study develops a simple interactive integrated water allocation model (IWAM), which can assist the planners and decision makers in optimal allocation of limited water from a storage reservoir to different user sectors, considering socio-economic, environmental and technical aspects. IWAM comprises three modules—a reservoir operation module (ROM), an economic analysis module (EAM) and a water allocation module (WAM). The model can optimize the water allocation with any of two different objectives or two objectives together. The two individual objectives included in the model are the maximization of satisfaction and the maximization of net economic benefit by the demand sectors. Weighting technique (WT) or simultaneous compromise constraint (SICCON) technique is used to convert the multi-objective decision-making problem into a single objective function. The single objective functions are optimized using linear programming. The model applicability is demonstrated for various cases with a hypothetical example.     

Conjunctive Use of Surface and Groundwater with Inter-Basin Transfer Approach: Case Study Piranshahr

Disregarding water as a key sustainable development has led to the water crisis in Iran. This problem is the biggest factor for marginalizing the planning and long-term management of water. The sustainable development policies in water resources management of IRAN require consideration of the different aspects of management that each of them demands the scientific integrated programs. Optimal use of inter-basin surface and groundwater resources and transfer of surplus water to adjacent basins are important from different aspects. The purpose of this study is to develop an efficient optimization model based on inter-basin water resources and restoration of outer-basin water resources. In the proposed model the three different objectives are as follow supplying inter-basin water demand, reducing the amount of water output of the boundary of IRAN and increasing water transfer to adjacent basins (Urmia Lake basin) are considered. In this model, water allocation is done based on consumption and resources priorities and groundwater table level constraints. In this research, the non-dominate sorting genetic algorithm is used for performing the developed model regarding the complexity and nonlinearity of the objectives and the decision variables. The optimal allocation of each water resources and water transfer to adjacent basin can be determined by using of proposed model. Optimal allocation policy presented based on optimal value and planning horizon. The results show that we can transfer considerable volume of water resources within the basin for restoration the outside basin and prevent the great flow of water by the border rivers applying the optimal operation policy.     

Robust Methods for Identifying Optimal Reservoir Operation Strategies Using Deterministic and Stochastic Formulations

Water allocation in a competing environment is a major social and economic challenge especially in water stressed semi-arid regions. In developing countries the end users are represented by the water sectors in most parts and conflict over water is resolved at the agency level. In this paper, two reservoir operation optimization models for water allocation to different users are presented. The objective functions of both models are based on the Nash Bargaining Theory which can incorporate the utility functions of the water users and the stakeholders as well as their relative authorities on the water allocation process. The first model is called GA–KNN (Genetic Algorithm–K Nearest Neighborhood) optimization model. In this model, in order to expedite the convergence process of GA, a KNN scheme for estimating initial solutions is used. Also KNN is utilized to develop the operating rules in each month based on the derived optimization results. The second model is called the Bayesian Stochastic GA (BSGA) optimization model. This model considers the joint probability distribution of inflow and its forecast to the reservoir. In this way, the intrinsic and forecast uncertainties of inflow to the reservoir are incorporated. In order to test the proposed models, they are applied to the Satarkhan reservoir system in the north-western part of Iran. The models have unique features in incorporating uncertainties, facilitating the convergence process of GA, and handling finer state variable discretization and utilizing reliability based utility functions for water user sectors. They are compared with the alternative models. Comparisons show the significant value of the proposed models in reservoir operation and supplying the demands of different water users.     

基于单元优化配置方法的陕西省关中地区水资源配置研究

水资源问题已经成为制约陕西省关中地区经济社会发展的瓶颈。为此,建立了省内南水北调受水区水资源单元优化配置模型,采用遗传算法求解该模型,并建立了该地区水资源单元优化配置系统。单元优化配置和常规配置的结果对比表明：在保证各供水保证率前提的基础上可以减少总缺水量9 875万m³。这一结论可为陕西省关中地区合理配置水资源及确定引汉济渭工程调水量提供借鉴。     

Development of a Dynamic Long-Term Water Allocation Model for Agriculture and Industry Water Demands

Demands growth and water resources limitation, enforce water sector policy makers to integrate water supply–demand interactions in a coherent framework for efficient water allocation. Water supply–demand interaction, changes long-term trend of water demands, which in turn has a substantial influence on water allocation. Researches on water allocation modeling lack adequate projection of relationship between water supply and demand. Socio-economic factors representing water allocation stakeholders’ benefits, account for the main share of water supply–demand interaction. Identification, representation and consideration of these factors in a water allocation model, is the main limitation of researches on this issue. In this paper a new long-term water allocation model at basin level is developed and introduced. This model considers water supply–demand interaction in agriculture and industry sectors, by use of socio-economic parameters; such as, production, cultivated land area, revenue and employment. The model main advantage is its ability to reflect the interrelationship between essential hydro-system and supply–demand components. It can explore both socio-economic and water allocation consequences of various policy choices. The model is used to assess two different development policies at basin level. The first one is fourth 5-year development plan of Iran, which fixes predefined growth rate for different sectors. The second one assumes the present state continues up to the end of planning horizon. A typical multi-reservoir water basin is modeled and analyzed for two policies. Indices that summarize long-term state of hydro-system and stakeholders are defined and used in policies assessment and decision making. Results of these assessments show fourth 5-year development policy provides opportunities for substantial improvement in water allocation and stakeholders’ benefits.     

不确定性多目标模糊规划在水资源优化配置中的应用

在充分考虑水资源系统中供需水不确定性的基础上,基于多目标规划、模糊规划和区间规划原理,以经济效益、社会效益和环境效益最大为目标,以需水量、可供水量和不同子区用水部门间的用水公平性为主要约束,构建一种适用于多水源、多子区、多用户的考虑供需水不确定性的多目标模糊规划模型,以期通过平衡水资源配置系统中用户配置水量、缺水风险与系统收益三者的关系,实现系统综合效益最大。模型以衡水市历年供水、用水及社会经济等数据确定规划年的水资源参数及经济参数,采用区间参数反映系统中的不确定性,通过引入模糊隶属度函数,利用两步交互式算法,将多目标规划转化为单目标规划进行求解,以2025年为衡水市规划水平年,得到规划水平年下的11个子区、4种水源、4个用水行业的最优配水方案。结果表明：本研究制定的水资源优化配置方案可实现“外调水优先利用、地下水控制利用”,同时考虑用水的公平性约束后,在缺水条件下可有效控制水资源向每立方米水效益高的区域和部门流动,有利于多区域、多部门共担缺水风险;该水资源优化配置方案可有效缓解衡水市水资源供需矛盾,实现多水源和多目标之间的协同互补。研究成果可为河北省其他县域的水资源优化配置提供技术参...     

Applying Genetic Algorithm and Neural Network to the Conjunctive Use of Surface and Subsurface Water

The conjunctive use of surface and subsurface water is one of the most effective ways to increase water supply reliability with minimal cost and environmental impact. This study presents a novel stepwise optimization model for optimizing the conjunctive use of surface and subsurface water resource management. At each time step, the proposed model decomposes the nonlinear conjunctive use problem into a linear surface water allocation sub-problem and a nonlinear groundwater simulation sub-problem. Instead of using a nonlinear algorithm to solve the entire problem, this decomposition approach integrates a linear algorithm with greater computational efficiency. Specifically, this study proposes a hybrid approach consisting of Genetic Algorithm (GA), Artificial Neural Network (ANN), and Linear Programming (LP) to solve the decomposed two-level problem. The top level uses GA to determine the optimal pumping rates and link the lower level sub-problem, while LP determines the optimal surface water allocation, and ANN performs the groundwater simulation. Because the optimization computation requires many groundwater simulations, the ANN instead of traditional numerical simulation greatly reduces the computational burden. The high computing performance of both LP and ANN significantly increase the computational efficiency of entire model. This study examines four case studies to determine the supply efficiencies under different operation models. Unlike the high interaction between climate conditions and surface water resource, groundwater resources are more stable than the surface water resources for water supply. First, results indicate that adding an groundwater system whose supply productivity is just 8.67 % of the entire water requirement with a surface water supply first (SWSF) policy can significantly decrease the shortage index (SI) from 2.93 to 1.54. Second, the proposed model provides a more efficient conjunctive use policy than the SWSF policy, achieving further decrease from 1.54 to 1.13 or 0.79, depending on the groundwater rule curves. Finally, because of the usage of the hybrid framework, GA, LP, and ANN, the computational efficiency of proposed model is higher than other models with a purebred architecture or traditional groundwater numerical simulations. Therefore, the proposed model can be used to solve complicated large field problems. The proposed model is a valuable tool for conjunctive use operation planning.     

Comparison of Policies Derived from Stochastic Dynamic Programming and Genetic Algorithm Models

A comprehensive Genetic Algorithm (GA) model has been developed and applied to derive optimal operational strategies of a multi-purpose reservoir, namely Perunchani Reservoir, in Kodaiyar Basin in Tamil Nadu, India. Most of the water resources problem involves uncertainty, in order to see that the GA model takes care of uncertainty in the input variable, the result of the GA model is compared with the performance of a detailed Stochastic Dynamic Programming (SDP) model. The SDP models are well established and proved that it takes care of uncertainty in-terms of either implicit or explicit approach. In the present study, the objective function of the models is set to minimize the annual sum of squared deviation from desired target release and desired storage volume. In the SDP model the optimal policies are derived by varying the state variables from 3 to 9 representative class intervals, and then the cases are evaluated for their performance using a simulation model for longer length of inflow data, generated using a Thomas–Fiering model. From the performance of the SDP model policies, it is found that the system encountered irrigation deficit, whereas GA model satisfied the demand to a greater extent. The sensitivity analysis of the GA model in selecting optimal population, optimal crossover probability and the optimal number of generations showed the values of 150, 0.76 and 175 respectively. On comparing the performance of SDP model policy with GA model, it is found that GA model has resulted in a lesser irrigation deficit. Thus based on the present case study, it may be concluded that the GA model performs better than the SDP model.     

基于协同学原理的流域水资源合理配置模型和方法研究

水资源的有限性和稀缺性导致水资源配置过程中经济、社会和生态环境目标相互冲突。本文在识别水资源配置系统协同特征基础上，根据协同学理论中有序度概念和支配原理，分别在水资源配置社会、经济和生态环境子系统中设置序参量，并结合信息熵原理，构建了一种基于协同学原理的流域水资源配置模型，部分程度上有效解决了水资源合理配置系统中多目标、多维数求解问题。将此理论运用于我国南方丰水地区－东江流域水资源合理配置，得出了满意结果。     

基于水资源优化配置的多目标决策模型探析

人类正以空前的速度和规模开发利用极其有限的水资源,合理有效的配置水资源,最大程度地提高水资源开发利用效率已成为当今世界的重要议题。水资源优化配置问题是一个多目标决策问题,本文在分析水资源优化配置基本概念及内涵的基础上,依据水资源配置的原则及目标,对基于经济目标、环境目标与社会目标的水资源优化配置多目标决策模型进行了较为深入的分析,并探讨了模型的求解以及决策方案的选择,可为水资源优化配置多目标决策问题提供一定的理论和实践指导。     

区域外调水水权分配模型及应用

提出外调水水权分配的5项原则,以此建立外调水水权分配的多目标优化模型。该模型综合考虑了城镇生活、生产、生态用水,并且体现了水权分配的公平性、高效性、可持续发展等原则。通过确定各目标函数权重,将该多目标模型转化为单目标优化模型进行求解。目标函数权重的确定采用综合赋权法,应用熵权法确定各目标函数的数学权重,再结合决策者意向得出经验权重,最后通过线性加权得出综合决策的权重。该权重确定方法既尊重了客观实际又考虑了决策者的意向,使得模型应用更加灵活。最后,将该模型应用到大连市外调水水权分配中,结果显示该模型具有较好的实用价值,可以用来指导区域调水工程规划及水资源优化配置。     

Development of an Optimal Reservoir Operation Scheme Using Extended Evolutionary Computing Algorithms Based on Conflict Resolution Approach: A Case Study

Optimal reservoir operation and water allocation are critical issues in sustainable water resource management due to increasing water demand. Multiplicity of stockholders with different objectives and utilities makes reservoir operation a complicated problem with a variety of constraints and objectives to be considered. In this case, the conflict resolution models can be efficiently used to determine the optimal water allocation scheme considering the utility and relative authority of different stakeholders. In this study, the Nash product is used for formulation of the objective function of a reservoir water allocation model. The Analytic Hierarchy Process (AHP) is used to determine the importance of each stockholder in bargaining for water. The Particle Swarm Optimization algorithm (PSO) and the Imperialism Competitive Algorithm (ICA) are applied to solve the proposed optimization model. System performance indices including reliability, resiliency and vulnerability are used to evaluate the performance of optimization algorithms. The simplest and most often-used reservoir policy (Standard Operating Policy, SOP) is also used in order to evaluate the performance of the proposed models. The proposed model is applied to the Karkheh River-Reservoir system located in south western part of Iran as a case study. Results show the significance of the application of conflict resolution models, such as the Nash theory and proposed optimization algorithms, for water allocation in the regional scale especially in complicated water supply systems.     

基于多目标不确定性机会约束规划的矿区水资源配置研究

以矿区水资源配置系统为研究对象,针对矿区生态恢复过程水资源配置的多目标、模糊、不确定性的特点,构建多目标不确定性机会约束规划模型对矿区水资源进行优化配置。基于生态优先的配水原则,以碳排放最小化目标代替传统的污染物排放量最小化目标,兼顾碳排放最小化、系统经济效益最大化以及缺水量最小化,建立矿区生态环境-经济社会-水资源协调发展的多目标模型,统筹分配地下水、地表水、矿井水以及再生水。对实际情况中的不确定性因素采用区间参数的方法表示,并通过遗传算法对模型进行求解,得到合理的水资源配置方案。将模型应用于宁夏回族自治区羊场湾矿区,结果表明：以碳排放为目标的不确定性机会约束规划多目标模型能够很好地统筹矿区经济发展目标与水资源节约目标,配置方案可保证各个用水部门的需水满足度达到100%,而以污染物排放量为目标的优化方案存在区域缺水的情况。系统可带来年碳净吸收量(CO₂)为533.7～702.4 t,预期年经济效益为162.3×10⁴～163.7×10⁴元,区域年供水富余量为43.5×10⁴～49.7×10     

Multiobjective management of a contaminated aquifer for agricultural use

Planned utilization of groundwater from a contaminated aquifer requires development of management strategies that determine the spatial distribution of withdrawal for allocation, as well as for control of water quality. Minimization of groundwater allocation for different purposes, and the control of contamination in the aquifer by a specified pumping strategy constitute a management problem with two conflicting objectives. In order to demonstrate possible tradeoffs between water quality control objective and minimum groundwater withdrawal objective, a multiobjective optimization model is formulated. The solution of the model specifies a strategy to control pollution distribution in the aquifer as per agricultural needs, and also evolve an optimal allocation policy to statisfy agricultural demands. Pareto-optimal solutions representing the tradeoff between the two noncommensurate objectives are established. The formulated model uses the embedding technique for simulating the flow and the transport processes in the aquifer. The constraint method is used to transform the multiobjective optimization model into a single objective optimization model. The resulting model is solved using the exterior penalty function method in conjunction with the Hooke-Jeeves method. The proposed model is easily adoptable for various agroclimatic regions and cropping patterns. For illustrative purposes, the model is applied to a specified study area. Although solutions of the model are presented and discussed as per agricultural requirements in terms of both quality and quantity, solutions for other kinds of water demands can be obtained using the same model with minor modifications. Results show that an optimal pumping strategy can be effectively utilized for controlling contamination in the aquifer.     

A Model for Solving the Optimal Water Allocation Problem in River Basins with Network Flow Programming When Introducing Non-Linearities

The allocation of water resources between different users is a traditional problem in many river basins. The objective is to obtain the optimal resource distribution and the associated circulating flows through the system. Network flow programming is a common technique for solving this problem. This optimisation procedure has been used many times for developing applications for concrete water systems, as well as for developing complete decision support systems. As long as many aspects of a river basin are not purely linear, the study of non-linearities will also be of great importance in water resources systems optimisation. This paper presents a generalised model for solving the optimal allocation of water resources in schemes where the objectives are minimising the demand deficits, complying with the required flows in the river and storing water in reservoirs. Evaporation from reservoirs and returns from demands are considered, and an iterative methodology is followed to solve these two non-network constraints. The model was applied to the Duero River basin (Spain). Three different network flow algorithms (Out-of-Kilter, RELAX-IV and NETFLO) were used to solve the allocation problem. Certain convergence issues were detected during the iterative process. There is a need to relate the data from the studied systems with the convergence criterion to be able to find the convergence criterion which yields the best results possible without requiring a long calculation time.     

基于正态云组合赋权的水资源配置方案综合评价方法EI北大核心CSCD

针对水资源配置方案评价过程中指标权重的模糊和随机不确定性问题,引入定性定量转换模型——正态云模型,提出一种基于正态云组合赋权和云相似度TOPSIS法的水资源配置方案综合评价方法。该方法利用逆向云生成算法将多个主客观权重进行信息融合,生成不确定正态云组合权重;通过计算水资源配置方案与理想方案综合云模型间的相对相似度对方案进行排序并确定最佳方案。利用所提出的方法对天津市水资源配置8种方案进行评价,并与其他5种水资源配置方案评价方法进行对比分析,结果表明:基于正态云组合赋权的水资源配置方案综合评价方法是可行且有效的,评价结果与实际相符;该方法可以有效避免对评价指标赋权的不确定性,有效地解决组合权重不确定信息的融合问题。     

Assumption-Simulation-Feedback-Adjustment (ASFA) Framework for Real-Time Correction of Water Resources Allocation: a Case Study of Longgang River Basin in Southern China

Water resources allocation is subject to uncertain future conditions and therefore needs real-time correction. This study develops a framework of “assumption-simulation-feedback-adjustment” (ASFA) for real-time correction of water resources allocation. The assumption component constructs a water resources allocation model and generates initial allocation solution (IAS); the simulation component applies IAS in a real-time hydrological scenario; the performance information is input into the feedback component. Three feedback functions, including gain function, correlation function, and least square function, are employed to deal with the information, and the value of output gain is determined for the adjustment component. The result then is a feedback allocation solution (FAS). This study applied ASFA to Longgang River basin, China, as a case study, compared FASs generated by three different feedback functions as well as IAS. Results showed that FAS generated by the gain function (FAS_GF) performed better with a higher assurance rate and less risk of continuous water shortage. Results also showed that to achieve the same management requirement, FAS_GF had a lower requirement of the amount of diverted water, indicating that the ASFA framework can make better use of water resources and reduce the pressure of diverted water. The ASFA framework builds a feedback mechanism for real-time correction of water resources allocation, provides a novel perspective for addressing the challenge of future uncertainty, which significantly improves the solutions of water allocation.     

A Multi-objective Linear Programming Model with Interval Parameters for Water Resources Allocation in Dalian City

Water resources management has been of concern for many researchers since the contradiction between increased water demand and decreased water supply has become obvious. In the real world, water resources systems usually have complexities among social, economic, natural resources and environmental aspects, which leads to multi-objective problems with significant uncertainties in system parameters, objectives, and their interactions. In this paper, a multi-objective linear programming model with interval parameters has been developed wherein an interactive compromising algorithm has been introduced. Through interactive compromising conflicts among multi-objectives, a feasible solution vector can be obtained. The developed model is then applied to allocation of multi-source water resources with different water qualities to multiple users with different water quality requirements for the Dalian city for 2010, 2015 and 2020 planning years. The model pursues the maximum synthesis benefits of economy, society and the environment. The results indicate that the proportion of reused water to the total water amount is gradually increasing, and the proportion of agricultural water consumption to the total water consumption is gradually decreasing. The allocation of multi-source water resources to multiple users is improved due to incorporation of uncertain factors into the model that provide useful decision support to water management authorities.     

西辽河流域水资源合理配置研究

在现有的水资源配置研究成果基础上,运用运筹学方法和规则模型方法,构建了面向经济耗水与生态耗水总量控制的基于优化技术的水资源配置模型和基于规则的水资源配置模型,结合西辽河流域实际情况,根据水资源＂三次平衡＂的配置思想,通过不同组合方案的长系列逐月调节计算,提出不同规划水平年水资源配置系列成果,实现了优化模型与规则模型相互校验和印证,使水资源配置结果科学合理和简单实用,为编制西辽河流域水资源综合规划和实施最严格的水资源管理制度提供了重要依据。     

Application of an Interior-Point Algorithm For Optimization of a Large-Scale Reservoir System

Optimization of a multi-reservoir system operation is challenging due to the non-linearity, stochasticity, and dimensionality involved in such a problem. In this research, a long-term planning model is presented for optimizing the operation of Iranian Karoon-Dez reservoir system using an interior-point algorithm. The system is the largest multi-purpose reservoir system in Iran with hydropower generation, water supply, and environmental objectives. The focus is on resolving the dimensionality of this problem while considering hydropower generation and water supply objectives. The weighting and constraints methods of multi-objective programming are used to assess the trade-off between water supply and hydropower objectives so as to find noninferior solutions. The computational efficiency of the proposed approach is demonstrated using historical data taken from Karoon-Dez reservoir system.