Performance Assessment of a Coupled Particle Swarm Optimization and Network Flow Programming Model for Optimum Water Allocation

Water resources allocation problems are mainly categorized in two classes of simulation and optimization. In most cases, optimization problems due to the number of variables, constraints and nonlinear feasible search space are known as a challenging subject in the literature. In this research, by coupling particle swarm optimization (PSO) algorithm and a network flow programming (NFP) based river basin simulation model, a PSO-NFP hybrid structure is constructed for optimum water allocation planning. In the PSO-NFP model, the NFP core roles as the fast inner simulation engine for finding optimum values for a large number of water discharges in the network links (rivers and canals) and nodes (reservoirs and demands) while the heuristic PSO algorithm forms the outer optimization cover to search for the optimum values of reservoirs capacities and their storage priorities. In order to assess the performance of the PSO-NFP model, three hypothetical test problems are defined, and their equivalent nonlinear mathematical programs are developed in LINGO and the results are compared. Finally, the PSO-NFP model is applied in solving a real river basin water allocation problem. Results indicate that the applied method of coupling PSO and NFP has an efficient ability for handling river basin-scale water resources optimization problems.     

Optimization of Hedging Rules for Reservoir Operation During Droughts Based on Particle Swarm Optimization

This paper presents a methodology to achieve the identification of optimal hedging rules for operating reservoir systems, seeking to mitigate the drought impacts. The heuristic Particle Swarm Optimization (PSO) method is adopted as the optimization solver. This procedure establishes a two-phase method that combines PSO with the simulation of the water system, representing a system of reservoirs that are jointly operated to satisfy a set of demands with different priorities. The hedging rules are based on monthly storage levels that trigger restrictions on the demands. As model parameters, monthly rule activation thresholds and rationing factors were used for each type of demand. The optimization procedure minimizes an objective function that penalizes large deficits and assigns different weights to different demand types. Since the whole problem is quite complex, its dimensionality is reduced through: i) a set of candidate monthly activation thresholds are selected a priori associated to given risk conditions; and ii) the rationing factors are defined for every demand of each threshold throughout all months. In addition, an effort is made to avoid the trap in local optimums, whilst several other comments considering the application of the PSO method in the examined applications are provided. The procedure has been successfully applied to four water resource systems in Spain. From the application it can be seen that the deficits of the water supply demand are nearly removed, thanks to the larger weight given to the deficits of this demand type. The irrigation deficits are also reduced, since we lead to a sequence of smaller shortages than only one potential catastrophic shortage.     

Application of ANN, Fuzzy Logic and Decision Tree Algorithms for the Development of Reservoir Operating Rules

Optimal use of scarce water resources is the prime objective for water resources development projects in the developing country like India. Optimal releases have been generally expressed as a function of reservoir state variables and hydrologic inputs by a relationship which ultimately allows the policy/water managers to determine the water to be released as a function of available information. Optimal releases were obtained by using optimal control theory with inflow series and revised reservoir characteristics such as elevation area capacity table, zero elevation level as input in this study. Operating rules for reservoir were developed as a function of demand, water level and inflow. Artificial Neural Network (ANN) with back propagation algorithm, Fuzzy Logic and decision tree algorithms such as M5 and REPTree were used for deriving the operating rules using the optimal releases for an irrigation and power supply reservoir, located in northern India. It was found that fuzzy logic model performed well compared to other soft computing techniques such as ANN, M5P and REPTree investigated in this study.     

A Hybrid Fuzzy-Based Multi-Objective PSO Algorithm for Conjunctive Water Use and Optimal Multi-Crop Pattern Planning

This paper focuses on extracting an optimal multi-crop pattern plan through multi-objective conjunctive surface-ground water use management. Minimizing shortages in meeting irrigation demands, maximizing groundwater resources sustainability and maximizing agricultural net benefits are the three main goals of the multi-objective optimization problem solved in this paper. A new robust fuzzy-based multi-objective PSO algorithm called f-MOPSO is adopted and modified to solve a three-objective real-world conjunctive use management problem presented in this paper after testing on standard test problems revealed f-MOPSO superiority as compared to the well-known multi-swarm vector evaluated PSO (VEPSO) algorithm. The f-MOPSO benefits from a well-organized Sugeno fuzzy inference system (SFIS) designed for handling multi-objective nature of the optimization problems. The unique performance of f-MOPSO is not only presenting the better final solutions, but also aggregating the capabilities for measurement of dominance and diversity of the solutions in one stage by one index named comprehensive dominance index, in contrast to a wide range of multi-objective algorithms that evaluate dominance and diversity in two separate stages resulting in excessive computational burden. The optimization model is carried out on a 10-year long-term simulation period, resulting in increasing irrigation efficiency i.e. decreasing water losses, decreasing water consumption per unit cultivated area and increasing water productivity compared to those similar criteria observed in actual operation in the study area. The wheat and rice crops were identified as the dominant crops, while the optimization model was the least interested to onion cultivation, assigning the least average cultivation area to this crop over the whole planning period.     

Economic Costs of Sustaining Water Supplies: Findings from the Rio Grande

Water claims in many of the world??s arid basins exceed reliable supplies. Water demands for irrigation, urban use, the environment, and energy continue to grow, while supplies remain constrained by unsustainable use, drought and impacts of climate change. For example, policymakers in North America??s Upper Rio Grande Basin face the challenge of designing plans for allocating the basin??s water supplies efficiently and fairly to support current uses and current environments. Managers also seek resilient institutions that can ensure adequate supplies for future generations. This paper addresses those challenges by designing and applying an integrated basin-scale framework that accounts for the basin??s most important hydrologic, economic, and institutional constraints. Its unique contribution is a quantitative analysis of three policies for addressing long term goals for the basin??s reservoirs and aquifers: (1) no sustainability for water stocks, (2) sustaining water stocks, and (3) renewing water stocks. It identifies water use and allocation trajectories over time that result from each of these three plans. Findings show that it is hydrologically and institutionally feasible to manage the basin??s water supplies sustainably. The economic cost of protecting the sustainability of the basin??s water stocks can be achieved at 6?C11 percent of the basin??s average annual total economic value of water over a 20?year time horizon.     

Multi-Objective Operating Rules for Danjiangkou Reservoir Under Climate Change

The natural variations of climatic system, as well as the potential influence of human activity on global warming, have changed the hydrologic cycle and threatened current water resources management. And the conflicts between different objectives in reservoir operation may become more and more challenging because of the impact of climate change. This study aims at deriving multi-objective operating rules to adapt to climate change and alleviate the conflicts. By combining the reservoir operation function and operating rule curves, an adaptive multi-objective operation model was proposed and developed. The optimal operating rules derived both by dynamic programming and NSGA-II method were compared and discussed. The projection pursuit method was used to select the best operating rules. The results demonstrate that the reservoir operating rules obtained by NSGA-II can increase the power generation and water supply yield and reliability, and the rules focusing on water supply can significantly increase the reservoir annual water supply yield (by 18.7 %). It is shown that the proposed model would be effective in reservoir operation under climate change.     

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.     

Sustainability Assessment of Urban Water Distribution Systems

A methodology is presented for determining sustainability indices for pressure and water age in water distribution systems (WDSs). These sustainability indices are based upon performance criteria including reliability, resiliency, and vulnerability. Pressure and water age are determined for a WDS as a function of operation time using the U.S. Environmental Protection Agency EPANET model. The values of pressure and water age are used to determine reliability, resiliency, and vulnerability performance criteria, which are then combined into the nodal sustainability indices for water age and pressure. In addition, the sustainability index (SI) computations are performed for zones to define the SI for water age and SI for pressure. A combined SI calculation is performed to produce an overall sustainability score for the entire zone in the water distribution network. The proposed methodology can be used to monitor the sustainability of existing WDSs and to help define alternative solutions including changes in pump operation and modifications to WDS to increase the sustainability.     

Scenario Optimisation of Pumping Schedules in a Complex Water Supply System Considering a Cost–Risk Balancing Approach

The optimisation of water pumping plant activation schedules is a significant issue when managing emergency and costly water transfer under a drought risk. This problem needs specific optimisation tools to deal with complex multi-reservoir supply systems and to consider different alternative scenarios. The effectiveness of emergency transfers alleviating droughts requires early warning and activation; on the other hand, the high operating costs of pump stations require system managers to take a robust approach that defines activation rules. The proposed optimisation procedure combines scenario optimisation analysis with a cost-risk balancing approach. The model searches for the identification of optimal decision rules by balancing the risk of water shortages and the operating costs of pumping stations. Scenario optimisation provides ‘barycentric’ values that define the activation threshold by comparing hydrological synthetic series results. A multi-objective approach is also required in order to balance energy cost minimisation requirements and a reduction of damage needs that can be caused by water shortages. Consequently, a scenario optimisation has been developed considering the multi-objective and cost-risk balancing problem. A model application has been developed optimising water management and energy costs in a real water system with shortage risks in the South Sardinia (Italy) region.     

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.     

Evaluation of the Feasibility of Irrigation Storage in a Flood Detention Pond in an Agricultural Catchment in Northern Italy

During recent years, the international attention paid to rational use and saving of water has increased, partly because of frequent water shortages occurring also in countries not usually involved in these problems, and partly as a consequence of rising conflicts on water allocation. Hence it is important to find new surface-water volumes satisfying agricultural water demand, as well as new ways and areas to store them. The simulation model presented by the paper enables evaluation, in a phase of design feasibility analysis, of whether a flood detention pond can be adapted to act as water storage during irrigation periods in order to reduce agricultural water shortages; it simulates detention pond response to floods and droughts under different hydraulic conditions and reservoir management rules. The first policy goal of the model is to maintain the stored volume within the maximum admissible for flood safety. The second goal is to maintain at the same time a minimum flow in the river and to satisfy irrigation water demand. The model, implemented in a purpose-made simulation software, was applied to an Italian river in the Emilia Romagna region: the results demonstrate that the use of a flood detention pond as an irrigation reservoir is not only possible, if it complies with the operating rules that emerge from this study, but also it brings tangible benefits in reducing agricultural water deficit.     

Flavia Rocha Loures and Alistair Rieu-Clarke (eds), The UN Watercourses Convention in force: strengthening international law for transboundary water management

Abstract

The MODSIM 8.0 decision support system (DSS) for integrated river basin management (IRBM) has been adapted from a prior appropriation rights-based system to one found in Korea and in much of Asia where water deficits are shared among water use sectors, taking into account priorities established by water policy and institutional frameworks. The Korean version called KModSim is applied to the Geum River basin for evaluation of long-term sustainability of existing and new water infrastructure and facilities under integrated, basin-wide water resources management. KModSim is calibrated to the physical and hydrologic characteristics of the basin, as well as to operational and administrative water allocation policies for municipal and industrial water supply, irrigation, hydropower, transbasin diversions, and low-flow augmentation for environmental purposes. Conditional reservoir operational rules that adapt to changing river basin hydrologic conditions are developed from an implicit stochastic optimization algorithm and incorporated using the extensive user-customization capabilities of KModSim. Results demonstrate that decision guidance under KModSim enhances beneficial water uses in the Geum River system through fully integrated, basin-wide management.     

Multi-Objective Optimization Model for the Allocation of Water Resources in Arid Regions Based on the Maximization of Socioeconomic Efficiency

The escalating world population has led to a drastic increase in water demand in the municipal and drinking water, agriculture and industry sectors. This situation necessitates application of effective measures for the optimal and efficient management of water resources. With this respect, a two-objective socioeconomic model (aimed at job creation) has been presented in this study for the optimum allocation of water resources to industry, agriculture and municipal water sectors. In the agriculture sector, the production function of each product has been determined and then, based on the production functions, areas under cultivation, product yield and the income obtained from each product, the combined objective function has been specified. In the industry sector, since water demand is a function of the amount of produced products, price of supplied water and the price of other supplies, the demand function of this sector was determined regionally. Also, considering the existing necessity in meeting the municipal water requirement, the total amount of water needed by this sector was fully allocated. Then by using two meta-heuristic algorithms, i.e. genetic algorithm (GA) and particle swarm optimization (PSO), the objective functions were maximized and the water resources were optimally allocated between agriculture and industry sectors and the results were compared. Ultimately, comparing the results gained by PSO and GA algorithms, PSO with an economic and profit growth of 54 % and a 13 % rise in employment relative to the base condition, turned out to be more efficient in this application.     

Optimization of Water Resources Utilization by PSO-GA

The objective of this paper is to present an optimal model to address the water resources utilization of the Tao River basin in China. The Tao River water diversion project has been proposed to alleviate the problem of water shortages in Gansu Province in China. A multi reservoir system is under consideration with multiple objectives including water diversion, ecological water demand, irrigation, hydropower generation, industrial requirements, and domestic uses in the Tao River basin. A multi-objective model for the minimization of water shortages and the maximization of hydro-power production is proposed to manage the utilization of Tao River water resources. An adjustable PSO-GA (particle swarm optimization – genetic algorithm) hybrid algorithm is proposed that combines the strengths of PSO and GA to balance natural selection and good knowledge sharing to enable a robust and efficient search of the solution space. Two driving parameters are used in the adjustable hybrid model to optimize the performance of the PSO-GA hybrid algorithm by assigning a preference to either PSO or GA. The results show that the proposed hybrid algorithm can simultaneously obtain a promising solution and speed up the convergence.     

Simulation-optimization model of reservoir operation based on target storage curves

This paper proposes a new storage allocation rule based on target storage curves. Joint operating rules are also proposed to solve the operation problems of a multi-reservoir system with joint demands and water transfer-supply projects. The joint operating rules include a water diversion rule to determine the amount of diverted water in a period, a hedging rule based on an aggregated reservoir to determine the total release from the system, and a storage allocation rule to specify the release from each reservoir. A simulation-optimization model was established to optimize the key points of the water diversion curves, the hedging rule curves, and the target storage curves using the improved particle swarm optimization （IPSO） algorithm. The multi-reservoir water supply system located in Liaoning Province, China, including a water transfer-supply project, was employed as a case study to verify the effectiveness of the proposed join operating rules and target storage curves. The results indicate that the proposed operating rules are suitable for the complex system. The storage allocation rule based on target storage curves shows an improved performance with regard to system storage distribution.     

Reservoir Optimization-Simulation with a Sediment Evacuation Model to Minimize Irrigation Deficits

Importance of existing reservoirs for supplying fresh water has increased significantly due to population increase and enhanced living standards, while the reduced development of new reservoirs in recent decades has made it even more pertinent that the current battery of reservoirs be operated in a sustainable and efficient manner. In order to move a step towards the goal of sustainability, sediment evacuation must be considered when optimizing a reservoir??s operations. The Reservoir Optimization-Simulation with Sediment Evacuation (ROSSE) model is a recently developed tool which internalizes sediment evacuation routines and the simulation module in a newly developed GA-based optimization module. This article applies the ROSSE model with the aim of minimizing irrigation shortages in the Tarbela Reservoir, Pakistan. The article also calculates the suitable values of various GA parameters required to run the model through a sensitivity analysis. Simulation results of three sets of rule curves??one existing and two optimized sets??are compared with each other for parameters like irrigation shortage, power generation, sediment evacuation and flood dis-benefits (damages). It is found that the optimized rule curves of scenario 1 reduce the irrigation shortages by 39?% while the optimized rule curves of scenario 2 can reduce the irrigation shortages by 24?% of that of the shortages by existing rule curves. The optimized rule curves of scenario 2 also ensure the current level of hydropower generation and sediment evacuation for the Tarbela reservoir. The study recommends a change in the reservoir??s existing rule curves in order to reduce irrigation shortages. The incorporation of the sediment evacuation routine and availability of economic and hydro based objective functions in the optimization model will help achieving the goal of sustainability.     

Optimal Water Allocation for an Alpine Hydropower System Under Changing Scenarios

The operating rules of water allocation in the alpine OFIMA hydropower network of the Maggia River basin (Canton Tessin, Switzerland) are investigated in response to changes in the production policy and environmental and climatic factors. The study was carried out by means of a nonlinear programming approach where the objective function is approximated to a quadratic form with linear constraints, and implemented on a monthly time scale. Two systemís configurations with different details were accordingly investigated and compared to assess the response of the hydropower network to changes in the production policy, in the magnitude of the inflows and to different environmental requests. The optimal solution of water allocation corresponding to the new hypothetical production policy shows marked differences but similar benefits when compared to the one of the present operating rules, thus suggesting the good flexibility of the real network under such change. In its whole, this paper therefore highlights the importance of supporting strategic decisions by means of informatics tools and, in addiction, it provides a useful case study to test the performances of the software AQUARIUS implementing such a nonlinear programming technique.     

An Optimal Reservoir Operation Model Based on Ecological Requirement and Its Effect on Electricity Generation

Rather than optimizing water regimes for one or a few species, a better approach is to approximate the natural flow regime that maintains the entire panoply of species. RVA method generally describes flow regimes through 32 hydrologic indicators, quantifies the changes of the indicators post-dam, and distinguishes the highly changed indicators. Based on this function, an ecological objective which takes natural flow regimes into consideration is established. The objective synthesize highly changed indicators selected from the 32 hydrologic indicators and minimize their degree of change to approximate the natural status. The function of the hydrologic indicators are quantified through fuzziology according to their effect on river environment when changed. The model is applied to Xiangyang section of the Han River downstream the Danjiangkou reservoir, which maximizes the ecological objective to close to a natural flow regime. By optimization, model results indicate that the maximum synthetical membership of the selected indicators is 0.5, which means that the post-regulation optimum release is 50?% closer to the natural flow regime. It is much better than the traditional power generation regulation model, the membership of which is only 0.2. The result indicates that the ecological model is better in improving the river ecosystem, but with reduced power generation because of excessive abandon water. However, when deeply evaluate the effect of the new model, it shows the possibility of a win-win scenario between maintaining ecosystem health and power generation.     

Integrated Artificial Neural Network (ANN) and Stochastic Dynamic Programming (SDP) Model for Optimal Release Policy

Complexicity in reservoir operation poses serious challenges to water resources planners and managers. These challenges of water reservoir operation are illustrated using a simulation to aid the development of an optimal operation policy for dam and reservoir. To achieve this, a Comprehensive Stochastic Dynamic Programming with Artificial Neural Network (SDP-ANN) model were developed and tested at Sg. Langat Reservoir in Malaysia. The nonlinearity of the natural physical processes was a major problem in determining the simulation of the reservoir parameters (elevation, surface-area, storage). To overcome water shortages resulting from uncertainty, the SDP-ANN model was used to evaluate the input variable and the performance outcome of the Model were compared with the Stochastic Dynamic Programming integrated with auto-regression (SDP-AR) model. The objective function of the models was set to minimize the sum of squared deviation from the desired targeted supply. Comparison result on the performance between SDP-AR model policy with SDP-ANN model found that the SDP-ANN model is a reliable and resilience model with a lesser supply deficit. The study concludes that the SDP-ANN model performs better than the SDP-AR model in deriving an optimal operating policy for the reservoir.     

Development of integrated watershed management schemes for an intensively urbanized region in Korea

A systematic, seven-step approach to integrated watershed management for sustainability was proposed and applied to upstream watershed of the Anyangcheon in Korea, which experiences streamflow depletion, frequent flood damages, and poor water quality due to rapid urbanization. To understand watershed components and processes, static and dynamic data were collected and synthetic hydrologic cycles generated by HSPF (Hydrologic Simulation Program – FORTRAN) were simulated (STEP 1). To identify and quantify problems within the watershed, three indices (following the pressure–state–response model) were employed: Potential Flood Damage (PFD), Potential Streamflow Depletion (PSD), and Potential Water Quality Deterioration (PWQD). Composite programming, a method of multi-criteria decision-making, was employed to estimate all indices and analytic hierarchy process are introduced to quantify the weighting values of all indicators (STEP 2). The primary goal of managers is to maintain certain minimum levels of water for instreamflow requirement and total maximum daily load (TMDL). Therefore target water quality and, instreamflow requirements (including low flow and fish flow) were specifically set (STEP 3). All possible management alternatives were listed (STEP 4) and a few specific management options which are technically, economically, and environmentally feasible, were selected (STEP 5). The ability of each feasible option to achieve the desired water quantity and quality criteria was analyzed and quantified using the HSPF (STEP 6). Finally, an evaluation index was calculated using each of the proposed alternatives in order to rank the sustainability and priority of alternatives (STEP 7).