Conjunctive Use of Surface Water and Groundwater: Application of Support Vector Machines (SVMs) and Genetic Algorithms

Combined simulation-optimization models have been widely used to address the management of water resources issues. This paper presents a simulation-optimization model for conjunctive use of surface water and groundwater at a basin-wide scale, the Zayandehrood river basin in west central Iran. In the Zayandehrood basin, in the past 10 years, a historical low rainfall in the head of the basin, combined with growing demand for water, has triggered great changes in water management at basin and irrigation system level. The conjunctive use model that coupled numerical simulation with nonlinear optimization is used to minimize shortages of water in meeting irrigation demands for four irrigation systems. Constraints guarantee the maximum/minimum cumulative groundwater drawdown and maximum capacity of irrigation systems. A support vector machines (SVMs) model is developed as a simulator of surface water and groundwater interaction model while a genetic algorithm (GA) is used as the optimization model. Conjunctive use model runs for three scenarios. Results show that the accuracy of SVMs as a simulator for surface water and groundwater interaction model is good and that it is possible to decrease the water shortage for irrigation systems with application of proposed SVMs-GA model.     

Multi-Objective Optimal Model for Conjunctive Use Management Using SGAs and NSGA-II Models

The widespread investigations on water resources management has become an essential issue because due to lack of sufficient research and inattention to planning and management of conjunctive use of surface and groundwater. The conjunctive management is a suitable alternative for imbalanced water resources distribution and related constraints in using of surface water. In this paper, a multi-objective model is developed to maximize the minimum reliability of system as well as minimize the costs due to water supply, aquifer reclamation and violation of the reservoir capacity in operation and allocation priority. The non-dominated sorting genetic algorithm (NSGA-II) is used to present the optimal trade-off between the objectives. The sequential genetic algorithms is also applied (SGA) in order to be compared with the NSGA-II model. The results show that the NSGA-II model can considerably reduce the computation burden of the conjunctive use models in comparison with the SGA optimization model. The obtained trade-off curve shows that a little increase in reliability leads to much more system costs. The weighted single objective SGA model results verify optimal trade-off obtained from NSGA-II model and show the optimality of allocated discharges.     

Rules for Optimal Operation of Reservoir-River-Groundwater Systems Considering Water Quality Targets: Application of M5P Model

The aim of this paper is to develop rules for optimal reservoir operation and water withdrawal from river and aquifer considering water supply and pollution control targets. The general approach is making use of an integrated water quantity-quality management (IWQM) modeling in conjunction with accurate data mining techniques. The IWQM model generates data, including; optimal releases and water withdrawal from river and aquifer for different conditions, and M5P and Support Vector Regression (SVR) data mining models utilize the results of the IWQM model for the derivation of rules. The IWQM model minimizes the deviation from water supply and water quality targets during the planning horizon. This method for derivation of operating rules is applied to a real world case study, Zayandehrood system, in Iran, with serious water supply and water pollution problems. The IWQM model is analyzed for different hydrologic and water demands scenarios with total dissolved solids (TDS) as the water quality indicator. Results show that an integrated approach to reservoir-river-aquifer operation in the study area can reduce the TDS by 43 % in the downstream river.     

Distributed Simulation‐optimization Model for Conjunctive Use of Groundwater and Surface Water Under Environmental and Sustainability Restrictions

Evolving optimal management strategies are essential for the sustainable development of water resources. A coupled simulation-optimization model that links the simulation and optimization models internally through a response matrix approach is developed for the conjunctive use of groundwater and surface water in meeting irrigation water demand and municipal water supply, while ensuring groundwater sustainability and maintaining environmental flow in river. It incorporates the stream-aquifer interactions, and the aquifer response matrix is generated from a numerical groundwater model. The optimization model is solved by using MATLAB. The developed model has been applied to the Hormat-Golina valley alluvial stream-aquifer system, Ethiopia, and the optimal pumping schedules were obtained for the existing 43 wells under two different scenarios representing with and without restrictions on stream flow depletion, and satisfying the physical, operational and managerial constraints arising due to hydrological configuration, sustainability and ecological services. The study reveals that the total annual optimal pumping is reduced by 19.75?% due to restrictions on stream flow depletion. It is observed that the groundwater pumping from the aquifer has a significant effect on the stream flow depletion and the optimal conjunctive water use plays a great role in preventing groundwater depletion caused by the extensive pumping for various purposes. The groundwater contribution in optimal conjunctive water use is very high having a value of 92?% because of limited capacity of canal. The findings would be useful to the planners and decision makers for ensuring long-term water sustainability.

     

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.     

Water Policies and Conflict Resolution of Public Participation Decision-Making Processes Using Prioritized Ordered Weighted Averaging (OWA) Operators

There is a growing interest in environmental policies about how to implement public participation engagement in the context of water resources management. This paper presents a robust methodology, based on ordered weighted averaging (OWA) operators, to conflict resolution decision-making problems under uncertain environments due to both information and stakeholders’ preferences. The methodology allows integrating heterogeneous interests of the general public and stakeholders on account of their different degree of acceptance or preference and level of influence or power regarding the measures and policies to be adopted, and also of their level of involvement (i.e., information supply, consultation and active involvement). These considerations lead to different environmental and socio-economic outcomes, and levels of stakeholders’ satisfaction. The methodology establishes a prioritization relationship over the stakeholders. The individual stakeholders’ preferences are aggregated through their associated weights, which depend on the satisfaction of the higher priority decision maker. The methodology ranks the optimal management strategies to maximize the stakeholders’ satisfaction. It has been successfully applied to a real case study, providing greater fairness, transparency, social equity and consensus among actors. Furthermore, it provides support to environmental policies, such as the EU Water Framework Directive (WFD), improving integrated water management while covering a wide range of objectives, management alternatives and stakeholders.     

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.     

Design of optimal environmental flow regime at downstream of reservoirs using wetted perimeter-optimization method

Conflict between water demand and environmental flow requirements is a challenging aspect in the reservoir management. Hence, optimizing environmental flow regime is one of the most important tasks at downstream of the large dams. The present study proposes a coupled simulation–optimization method based on the wetted perimeter method as an assessment method of the environmental flow and optimization of the reservoir operation to minimize difference between habitat loss and water demand loss using different metaheuristic algorithms. Moreover, the fuzzy TOPSIS as the decision-making system was applied for ranking the optimization algorithms. Indices including reliability index, vulnerability index, root mean square error and mean absolute error were utilized as criteria to measure the system performance and to select the best algorithm. Based on the results, gravity search algorithm (GSA) was the best method to optimize environmental flow regime at downstream of the reservoir in the case study. The proposed method is able to optimize environmental flow to minimize conflicts between human’s needs and aquatic’s needs considering storage constraints in the reservoir management. The proposed method might minimize negotiations between environmental managers and stakeholders. Furthermore, it should be noted that original wetted perimeter method is not able to provide optimal environmental flow regime based on a balance between users and constraints in the reservoir management such as storage constraints. The proposed method converts wetted perimeter method from an assessment method to a simulation–optimization method for optimizing environmental flow at downstream of the reservoirs.     

大型灌区骨干水库分期旱限水位研究

水库作为常见的重要水利工程设施,在区域水资源承载力水量、水质、水生态和水流更新等要素调控中具有举足轻重的地位和作用,在降水径流减少的枯水季节显得尤其重要。旱限水位是水库在低水位调度运行的控制性特征水位,其合理设置与科学运用对区域水资源高效利用意义重大。为此以淠史杭灌区梅山水库的来水特性和用户需水特性为基础,首先采用Hausdorff维数分形方法划分了梅山水库入库径流干旱预警时段,进而通过不同水平年各预警期内来水、供水调节计算了初始旱限水位:旱警水位和旱枯水位,并以水库灌区水资源系统长系列模拟模型及优化模型为依托,优化确定了梅山水库不同预警期的旱限水位,最后分别从水库运行、灌区灌溉、生产生活供水、生态用水以及水力发电等多角度评估了旱限水位的合理性,为水利部门指导水库灌区抗旱运行,制订相应抗旱预案提供技术参考。     

Development of Priority-Based Policies for Conjunctive Use of Surface and Groundwater

Abstract

A combined optimization-simulation approach was used to develop and evaluate the alternate priority-based policies for operation of surface and groundwater systems and is demonstrated with a case study. An optimization model was used to find optimal cropping pattern with and without the conjunctive use of surface and groundwater, as well as with and without socio-economic constraints. The optimization model, based on linear programming, maximizes the net benefit from irrigation activities subject to various physical, economical, and social constraints. A simulation model was used to evaluate the conjunctive operation of the system using the optimal cropping pattern derived from the optimization model. The developed policies have been verified with long-generated stream flow sequences. Three alternate priority-based policies differing in level of groundwater pumping and area of cultivation of rice crop have been evaluated: (1) irrigation with surface water only (Policy-1); (2) irrigation with conjunctive use of surface and groundwater, without socio-economic constraints (Policy-2); and (3) irrigation with conjunctive use operation and with socio-economic constraints (Policy-3). It was found that the use of available groundwater within three meters below ground level (Policy-2) to be optimal, and these results were used in simulation for further evaluation of policies. It was also found that the policy-3 of conjunctive use operation with a priority of 75 percent of maximum possible rice area (using groundwater available within four meters below ground level) resulted in a better scenario. Thus the conjunctive use Policy-2 and Policy-3 with 75 percent of maximum possible rice crop area can be used as better policies for the system studied.     

Multi-objective stochastic groundwater management of nonuniform,homogeneous aquifers

Like any other resources planning and management, groundwater management is performed in a stochastic environment in which the system itself involves a number of random elements. Consequences as a result of decisions made based on analyses are not certain. This paper presents a management model using the chance-constrained framework which explicitly considers the random nature of aquifer properties. The model enables the derivation of an optimal groundwater management policy that would satisfy required operation performance reliability. Furthermore, the chance-constrained model is extended to the multi-objective optimization framework in which a tradeoff between total water supply pumpage and system performance reliability is explicitly considered. The models are applied to a hypothetical example of a steady, nonuniform, homogeneous confined aquifer.     

Simulation-Optimization Modeling of Conjunctive Use of Surface Water and Groundwater

Water resources management in semiarid regions with low precipitation and high potential of evapotranspiration is a great challenge for managers and decision makers. In those regions, both sources of water should be managed conjunctively so as to minimize shortages of water in dry seasons. In conjunctive use, the difficulty increases as one must represent the response of both systems interactions, and develop management strategies that simultaneously address surface water and aquifer regulation. This paper focuses on the simulation-optimization for conjunctive use of surface water and groundwater on a basin-wide scale, the Najafabad plain in west-central Iran. A trained artificial neural network model is developed as a simulator of surface water and groundwater interaction while a genetic algorithm is developed as the optimization model. The main goal of the simulation-optimization model is to minimize shortages in meeting irrigation demands for three irrigation systems subject to constraints on the control of cumulative drawdown of the underlying water table and maximum capacity of surface irrigation systems. To achieve the main goal, three scenarios are presented. Results of the proposed model demonstrate the importance of the conjunctive use approach for planning the management of water resources in semiarid regions.     

Optimal reservoir operation using Nash bargaining solution and evolutionary algorithms

Optimizing reservoir operation is critical to ongoing sustainable water resources management. However, different stakeholders in reservoir management often have different interests and resource competition may provoke conflicts. Resource competition warrants the use of bargaining solution approaches to develop an optimal operational scheme. In this study, the Nash bargaining solution method was used to formulate an objective function for water allocation in a reservoir. Additionally, the genetic and ant colony optimization algorithms were used to achieve optimal solutions of the objective function. The Mahabad Dam in West Azerbaijan, Iran, was used as a case study site due to its complex water allocation requirements for multiple stakeholders, including agricultural, domestic, industrial, and environmental sectors. The relative weights of different sectors in the objective function were determined using a discrete kernel based on the priorities stipulated by the government (the Lake Urmia National Restoration Program). According to the policies for the agricultural sector, water allocation optimization for different sectors was carried out using three scenarios: (1) the current situation, (2) optimization of the cultivation pattern, and (3) changes to the irrigation system. The results showed that the objective function and the Nash bargaining solution method led to a water utility for all stakeholders of 98%. Furthermore, the two optimization algorithms were used to achieve the global optimal solution of the objective function, and reduced the failure of the domestic sector by 10% while meeting the required objective in water-limited periods. As the conflicts among stakeholders may become more common with a changing climate and an increase in water demand, these results have implications for reservoir operation and associated policies.     

Optimal Operation of Artificial Groundwater Recharge Systems Considering Water Quality Transformations

In water limited areas as water demand increases alternative sustainable water sources must be identified. One supply augmentation practice, that is already being applied in the arid southwest U.S., is artificial groundwater recharge usingwastewater effluent. The objective of a recharge facility is to supplement the available groundwater resources by storing water for the future. The resulting reclaimed water is used primarily for non-potable purposes but under increasing stressesshifting to potable use is likely to happen. Water quality thenbecomes a more pressing concern. Water quality improvements during infiltration and groundwater transport are significant and are collectively described as soil-aquifer treatment (SAT). To meet user needs, the recharge operation must be efficiently managed considering monetary, water quality and environmental concerns. In this paper, a SAT management model is developed that considers all of these concerns. Within the SAT management model, the shuffled complex evolution algorithm (SCE) is used as the optimization tool. SCEis a relatively new meta-heuristic search technique for continuousproblems that has been used extensively for hydrologic model calibration. In this application, SCE is integrated with the simulation models (MODFLOW, MT3D, and MODPATH) to represent movement and quality transformations. Two steady state case studies on a general hypothetical aquifer (modeled after a field site) were examined using the management model.     

基于模糊集对分析的灌区水库旱限水位及供水策略优化研究

随着经济社会发展和气候条件改变,我国区域干旱问题日益突出,水库在抗旱减灾过程中的作用越来越凸显,旱限水位的提出与应用对有效发挥大中型水库在抗旱中的调控作用、缓解流域旱情具有重要意义。针对旱限水位研究现状,本文以史河灌区梅山水库为例,首先考虑城乡供水、农业灌溉、水力发电和生态环境等诸多因素,构建基于模糊集对分析法的水库灌区水资源系统运行效果综合评价模型,并以水资源系统综合运行等级最优为目标,以分期分级旱限水位、灌溉限供措施(基于水库来水特征和作物生长特性)为优化变量,运用免疫遗传算法进行系统优化求解,最后对比有无旱限水位下水库水资源系统综合运行效果,并分析了不同控制方式下水库供水变化和灌区作物因旱减产风险。结果显示：该优化方法相较于以水库供水经济效益为目标旱限水位确定方法,多项年均指标都有较大改善,其中梅山水库经济效益提高了46万元,充反调节水库水量增加了555万m³,而水库弃水量减少了566万m³;史河灌区塘坝供水量增加了253万m³,而作物减产率下降了3.32%,同时作物因旱减产风险降低了3.15%。可见,运用系统综合...     

Conjunctive Operation of Reservoirs and Ponds Using a Simulation-Optimization Model of Irrigation Systems

Water resource management in arid agricultural irrigation regions is a great challenge for managers and decision makers. In some of those regions, many ponds have been built to ensure an adequate water supply for irrigation. Therefore, reservoirs and ponds should be managed conjunctively to minimize shortages of water. In this study, a new integrated mathematical model of conjunctive, or integrated, operation of reservoirs and ponds to maximize the water supply has been proposed for a reservoir-pond irrigation system. This objective has been achieved via the use of two models: an optimal model, which is used to determine the optimal discharge of reservoirs, and a simulation model, which considers the regulatory role of ponds and reservoirs and simulates their water supply to the irrigation system. An adaptive genetic algorithm has been employed in this study to solve the nonlinear and multi-dimensional reservoirs optimization problem. This methodology has been applied to the Yarkant River Basin to demonstrate its applicability, and three scenarios are presented. The main objective of the simulation-optimization model in the Yarkant River Basin is to minimize shortages in meeting irrigation demands for nine sub-irrigation systems subject to the constraint of ecological water transfer to the Tarim River. The optimizing effect of the model was particularly prominent under the third scenario, i.e., the XBD, MMK, and ART Reservoirs and 16 ponds conjunctively operated to meet the water demand of the YKB. The frequency of success (FS) in meeting agricultural water demands reaches up to 75%, and the value for ecological demand is 50.98%. The results demonstrate the importance of the conjunctive combined use approach for management of water resources in irrigation system of arid regions.     

A Neural Networks Approach for Deriving Irrigation Reservoir Operating Rules

A neural networks approach is applied to the derivation of the operating rules of an irrigation supply reservoir. Operating rules are determined as a two step process: first, a dynamic programming technique, which determines the optimal releases byminimizing the sum of squared deficits, assumed as objective function, subject to various constraints is applied. Then, theresulting releases from the reservoir are expressed as a functionof significant variables by neural networks. Neural networks aretrained on a long period, including severe drought events, andthe operation rules so determined are validated on a differentshorter period. The behaviour of different operating rules is assessed by simulating reservoir operation and by computing several performance indices of the reservoir and crop yield through a soil water balance model. Results show that operating rules based on an optimization with constraints resembling real system operation criteria lead to a good performance both in normal and in drought periods, reducing maximum deficits and water spills.     

Optimal Reservoir Operation Based on Conjunctive Use of Surface Water and Groundwater Using Neuro-Fuzzy Systems

Reservoir operation cannot be carried out without due heed to surface water and groundwater resources, since neglecting either will have irreversible consequences. Optimal operation of the Zayandehrood Dam which supplies water into the Zayandehrood River basin in the central plateau of Iran is a case in point which warrants due consideration paid to both dam operation and the climate conditions in the region suffering from a history of successive droughts. The main objective of the present research is to develop operation rules for the Zayandehrood reservoir through a combined perspective of both surface and ground water resources using the fuzzy inference system, and adaptive neuro-fuzzy inference system. The objective is to determine the share of the Zayandehrood reservoir in meeting downstream water demands. For this purpose, the water shortage and the dramatic groundwater drawdown in the Zayandehrood River basin faced with in recent years have been studied in an attempt to develop operation models capable of controlling groundwater drawdown. The models indicate that not only can groundwater drawdown be controlled, but that it is also possible to establish a greater sustainability. Different operation models have been compared in terms of their operation criteria. Results show that the ANFIS model composed of optimal data enjoys a higher sustainability compared to others.     

Participatory Optimization Scenario for Water Resources Management: A Case from Jordan

Participatory optimization scenario process was developed for water resources management of the Zarqa River Basin in Jordan. The basin was selected to represent a case study of semi-arid area in the Mediterranean because of its entire range of prototypical water management problems. The nature of the institutional framework, the severity of the water related problems and concentration of population in the basin have required the need for stakeholders’ involvement in the optimization process. The paper demonstrates a Water Resources Model (WRM) consisting of integrated cascade of modules, embedded in a framework of a participatory approach in water resources optimization. The system includes baseline scenario, identification of constrains and instruments, the optimization scenario and analysis of results. In a participatory approach, stakeholders identified the optimization criteria (constraints) and the management interventions (instruments). Constraints were set to securing high supply/demand ratio of 0.98 and improving reliability of supply to 75 %, while specific eight instruments were suggested and manipulated by the model to achieve the above criteria. The results of the WRM optimization scenario showed that the specified constraints were met so that the supply/demand ratio increased from 0.90 to 0.996 and the reliability of supply improved from 58 % to 84 %. The benefit/cost ratio, water shortfall, and the economic efficiency had responded effectively. The model proved its efficiency in using the full featured basin characteristics towards baseline and optimization scenarios with the support of stakeholders in simulating the basin behavior over time using the model parameters.     

饮用水水源地健康风险评价

综合目前国外健康风险评价的研究成果,建立了水环境健康风险评价模式。以某河流水质的实测资料为背景,研究了待建水库水环境健康风险问题,得出定量的健康风险指标,为饮用水水源风险管理和决策提供重要依据,为构建适合我国实际的饮用水源健康风险评价体系奠定基础。