A systematic approach for estimating water losses in irrigation canals

In order to determine water losses in irrigation canals, a systematic approach was developed, consisting of two main components: a seepage simulation model and a hydraulic simulation model. The SEEP/W module of the Geo-Studio software was used to simulate the seepage rate, and the Hydrologic Engineering Center-River Analysis System (HEC-RAS) hydrodynamic model was used for hydraulic simulation. Different operation scenarios were designed to investigate all possible situations in daily operation of water distribution and delivery systems. The seepage simulation results show that the seepage losses were higher at the bottom and corners of the canal, because the hydraulic gradient was affected by the hydraulic load. The hydraulic simulation results show that due to physical and management infrastructure (using non-automated and operator-based regulation structures), operational losses accounted for a significant volume of losses compared to seepage losses. In most operation scenarios, the maximum seepage loss was 10%, and the remaining 90% was related to operational losses. It is concluded that any factor (decrease or increase of inflow to the canal) that causes an increase or decrease of operational losses is ultimately a determining factor in reducing or increasing total losses. Therefore, management approaches should be adopted to improve performance of the system and reduce losses, especially operational losses, by improving the operation methods of water level regulation and off-take structures.     

Modeling Evaporation-Seepage Losses for Reservoir Water Balance in Semi-arid Regions

In the water balance of reservoir system, evaporation plays a crucial role particularly so for the reservoir systems of smaller size located in the semi-arid or arid regions. Such regions are most often characterized by significant seepage losses from reservoirs, besides evaporation losses. Usually, in the optimization of a reservoir system, it is a common practice to assume evaporation loss either as some constant value or as negligible. Such assumptions, however, may affect the results of reservoir optimization. This is demonstrated in this study by a case study in the optimal scheduling of Pilavakkal reservoir system in Vaipar basin of Tamilnadu, India. For modeling reservoir losses, many models are available, of which, Penman combination model is most commonly used. In this study, an alternative approach based on Genetic Programming (GP) is proposed. The results of GP and Penman model for both evaporation loss estimation and reservoir scheduling are compared. It is found that while GP and Penman combination model performs equally well for estimating evaporation losses, GP is also able to model seepage losses (or other losses from reservoir) to a much better degree. It is also shown the reservoir scheduling does get influenced based on how the reservoir losses are modeled in the reservoir water balance equation.     

Application of Intelligent Water Drops Algorithm in Reservoir Operation

Optimum reservoir operation is a challenging problem in water resources systems. In this paper, Intelligent Water Drops (IWD) algorithm is applied in a reservoir operation problem. IWD is a population based algorithm and is initially proposed for solving combinatorial problems. The algorithm mimics the dynamics of river system and the behavior of water drops in the rivers. For this purpose data from Dez reservoir, located in southwestern Iran, has been used to examine the performance of the model. Moreover, due to similarities between IWD and the Ant Colony Optimization (ACO) algorithms, the results are compared with those of the ACO algorithm. Comparison of the results shows that while the IWD algorithm finds relatively better solutions, it is able to overcome the computational time consumption deficiencies inherited in the ACO methods. This is very important in large models with too many decision variables where run time becomes a limiting factor for optimization model applications.     

A New Method for Simultaneous Calibration of Demand Pattern and Hazen-Williams Coefficients in Water Distribution Systems

Water distribution systems, where flow in some pipes is not measured or storage tanks are connected together, calculation of demand pattern coefficients of the network is difficult. Since, Hazen-Williams coefficients of the network are also unknown; the problem is becoming unintelligible further. The present study proposes a new method for simultaneous calibration of demand pattern and Hazen-Williams coefficients that uses the Ant Colony Optimization (ACO) algorithms coupled with the hydraulic simulator (EPANET2) in a MATLAB code. In this paper demand pattern and Hazen-Williams coefficients are the calibration parameters and measured data consist of nodal pressure heads and pipe flows. The defined objective function minimizes the difference between the measured and simulated values. The new proposed method was tested on a two-loop test example and a real water distribution network. The results show that the new calibration model is able to calibrate demand pattern and Hazen-Williams coefficients simultaneously with high precision and accuracy.     

Optimal planning of water supply system for long-term sustainability

Developing a long-term system plan for sustainable water supply is a challenging task due to system complexity and future uncertainties in water demands and source availability. Here a coupled optimization model is proposed for water supply system design and long-term operations by deciding system component sizes and water flow allocations simultaneously. The objective is to minimize overall system costs (i.e., sum of capital and operation costs) while meeting water demands and operational constraints. The economic costs include initial component construction costs and operation expenditure over pre-defined operation years. The proposed model integrates a genetic algorithm with a linear programming model to optimize water infrastructure investments and annual water transfers satisfying flow constraints. The coupled model was applied to a simplified water supply network composed of multiple water sources and users. For the application network, various qualities of water from different sources could be supplied to different users. Plausible future scenarios with time varying water demands were simulated representing potential future conditions. Application results show that the proposed coupled model is beneficial in decision making process to design structural components in near future and prepare long-term policies for water shortage and water right issues in upcoming years. The model can be tailored to a specific system and various regulations and conditions can be incorporated within the model without adding complexity to the optimization framework.     

Evaluating the Performance of Agricultural Water Distribution Systems Using FIS,ANN and ANFIS Intelligent Models

Increasing water use efficiency in the agricultural sector requires the use of appropriate methods for intelligent performance evaluation of surface water distribution systems in agriculture. Therefore, in this study a systematic approach was developed for operational performance appraisal of the agricultural water distribution systems. For this purpose, Fuzzy Inference System (FIS), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to evaluate the technical performance of irrigation network, considering the uncertainties in the water exploitation process. The performance of the developed models was studied on the Roodasht irrigation canal, located in central Iran, which suffers from severe fluctuations in the inflow, by evaluating the adequacy, efficiency, and equity of surface water distribution. Hydraulic simulation of water distribution system, as well as providing the information required for training and validation of the intelligent models, were performed using the HEC-RAS model. The results showed that compared to the FIS model, ANN and ANFIS models similarly predicted the model outputs with lower errors at almost the same level. The adequacy, efficiency, and equity indicators were predicted by ANFIS model with MAPE of 0.16, 0.01 and 0.23, respectively. Also, FIS model was only able to predict the efficiency and could not predict the adequacy and equity with appropriate performance. The findings of this study reveal that since the ANFIS model uses both FIS and ANN models in its structure, it considers the model uncertainty reliably, and it can be used to evaluate the performance of agricultural water systems.

     

Optimization Models for Layout and Pipe Design for Storm Sewer Systems

Optimization models are developed for simultaneously determining the pipe layout and the pipe design for storm sewer systems. The pipe design process includes computation of commercial diameters, slopes, and crown elevations for the storm sewer pipes. The optimization models aim to minimize the total costs of the layout and the pipe design for most of system elements. The optimization models are formulated as a 0–1 Integer Nonlinear Programming problem and solved using the General Algebraic Modeling System without the use of heuristic models which were characteristic of all previous models for the simultaneous determine of the pipe layout and pipe design of sewer networks. The models are based upon two different optimization approaches: (1) considers one or more commercial diameters of pipe connecting two manholes and (2) considers only one commercial diameter in a pipe connecting two manholes. The commercial diameters, pipe slopes, crown elevations, and total costs of the storm sewer system were compared for the two approaches using an example that illustrates the savings in cost by allowing multiple pipe sizes. The two new optimization modeling approaches developed herein can simultaneously determine the minimum cost pipe design (commercial diameters, slopes, and crown elevations) and pipe layout of storm sewer systems and satisfy all design constraints.

     

Application of the Coupled Simulation–optimization Method for the Optimum Cut-off Design Under a Hydraulic Structure

A Genetic Algorithm model, coupled with Finite Element Programming (GA-FEP), has been developed to create an optimal design for hydraulic structures to address seepage problems. While the objective function of the optimization model was to minimize the construction costs of the hydraulic structure, the main constraints were to satisfy safety factors concerning uplift pressure and exit gradient. The GA-FEP model proposed here meets the requirements of an optimal hydraulic design in two stages. Firstly, a validated numerical model coded using Finite-element Programming (FEP), was used to analyze seepage problems. This was followed by application of Genetic Algorithm (GA) and finite-element programming (FEP) to establish the optimum depth and location for cut-offs. A MATLAB programming code was used to create the link between the numerical and optimization model, creating a simulation–optimization (S–O) model. The effects of hydraulic conductivity and anisotropic ratios on the hydraulic structure design, were also investigated. The results indicate that the proposed GA-FEP model will provide a safe, efficient and economical hydraulic cut-off design. Evaluation of the model revealed acceptable agreement between expected and simulated seepage parameters pertinent to the hydraulic structure design.

     

Topographic Energy Management in Water Distribution Systems

A significant amount of energy is required to operate pressurised water distribution systems, and therefore, improving their efficiency is crucial. Traditionally, more emphasis has been placed on operational losses (pumping inefficiencies, excess leakage or friction in pipes) than on structural (or topographic) losses, which arise because of the irregular (unchangeable) terrain on which the system is located and the network’s layout. Hence, modifying the network to adopt an ecologically friendly layout is the only way to reduce structural losses. With the aim of improving the management of water distribution systems and optimising their energy use, this work audits and classifies water networks’ structural losses (derived from topographic energy), which constitutes the main novelty of this paper. Energy can be recovered with PATs (pumps as turbines) or removed through PRVs (pressure reducing valves). The proposed hydraulic analysis clarifies how that energy is used and identifies the most suitable strategy for improving efficiency as locating the most suitable place to install PRVs or PATs. Two examples are discussed to illustrate the relevance of this analysis.

     

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.     

Basin-wide Water Resources Planning by Integrating PSO Algorithm and MODSIM

Water resources planning and management at basin scale is such a large and complex problem that makes it essential to use effective modeling tools in order to obtain an optimum plan for river basins development. In this paper, a methodology is presented for optimized design and operation of the upstream Sirvan basin in Iran. The model proposed integrates MODSIM generalized river basin network flow model, with the capability of simulating various characteristics and features of water resources in a river basin, and Particle Swarm Optimization (PSO) algorithm. In the developed PSO-MODSIM model, the size of planned dams and water transfer systems, as design variables, and the relative priorities for meeting reservoir target storages, as operational variables, are varied and evolved using PSO algorithm. MODSIM is called to simulate the system performance and to evaluate the fitness of each set of those design and operational variables with respect to the model’s objective function. The PSO objective function is to maximize the total net benefit consisting of benefits due to supplying water to different types of water uses and construction costs of dams and water transfer and/or pumping systems. Varying the design and operational variables in MODSIM 8 is done using the MODSIM’s custom coding feature in VB.NET routine. The PSO-MODSIM model is used to size the planed water storage and transfer components of a river basin system and to allocate water resources optimally over time and space among competing demands, considering coordinated operation of the system components. The model results has been analyzed for different scenarios of water transfer from Sirvan to neighboring basins.     

Application of the Grasshopper Optimization Algorithm (GOA) to the Optimal Operation of Hydropower Reservoir Systems Under Climate Change

Hydropower is a low-carbon energy source, which may be adversely impacted by climate change. This work applies the Grasshopper Optimization Algorithm (GOA) to optimize hydropower multi-reservoir systems. Performance of GOA is compared with that of particle swarm optimization (PSO). GOA is applied to hydropower, three-reservoir system (Seymareh, Sazbon, and Karkheh), located in the Karkheh basin (Iran) for baseline period 1976–2005 and two future periods (2040–2069) and (2070–2099) under greenhouse gases pathway scenarios RCP2.6, RCP4.5, and RCP8.5. GOA minimizes the shortage of hydropower energy generation. Results from GOA optimization of Seymareh reservoir show that average objective function in baseline is 85 and minimum value of average objective function in 2040–2069 would be under RCP2.6 (equal to 0.278). Optimization of Seymareh-reservoir based on PSO shows that average value of objective function in baseline is less (that is, better) than value obtained with GOA (10.953). Optimization results for two-reservoir system (Sazbon and Karkheh) based on GOA optimization show that objective function in baseline is 5.44 times corresponding value obtained with PSO, standard deviation is 2.3 times that calculated with PSO, and run-time is 1.5 times PSO’s. Concerning three-reservoir systems it was determined that objective function based on PSO had the best value (the lowest energy deficit), especially in future. GOA converges close to the best objective function, especially in future-periods optimization, and convergence to solutions is more stable than PSO’s. A comparison of performance of GOA and PSO indicates PSO converges faster to optimal solution, and produces better objective function than GOA.

     

Multi-Colony Ant Algorithm for Continuous Multi-Reservoir Operation Optimization Problem

Ant Colony Optimization (ACO) algorithms are basically developed for discrete optimization and hence their application to continuous optimization problems require the transformation of a continuous search space to a discrete one by discretization of the continuous decision variables. Thus, the allowable continuous range of decision variables is usually discretized into a discrete set of allowable values and a search is then conducted over the resulting discrete search space for the optimum solution. Due to the discretization of the search space on the decision variable, the performance of the ACO algorithms in continuous problems is poor. In this paper a special version of multi-colony algorithm is proposed which helps to generate a non-homogeneous and more or less random mesh in entire search space to minimize the possibility of loosing global optimum domain. The proposed multi-colony algorithm presents a new scheme which is quite different from those used in multi criteria and multi objective problems and parallelization schemes. The proposed algorithm can efficiently handle the combination of discrete and continuous decision variables. To investigate the performance of the proposed algorithm, the well-known multimodal, continuous, nonseparable, nonlinear, and illegal (CNNI) Fletcher–Powell function and complex 10-reservoir problem operation optimization have been considered. It is concluded that the proposed algorithm provides promising and comparable solutions with known global optimum results.     

基于GACO-BP-MC的大坝变形监控模型

建立相应的安全监控模型来分析大坝变形监测资料对保障大坝服役安全意义重大。BP神经网络模型在此方面得到了广泛应用,但采用蚁群算法(ACO)对BP神经网络参数寻优时存在因初期搜索完全随机导致收敛速度慢的问题。将具有快速随机的全局搜索能力的遗传算法(GA)引入蚁群算法中,利用遗传算法指导生成初始信息素分布,再由蚁群算法正反馈寻得最优解来训练BP神经网络,从而得到大坝变形预测值,2种算法优势互补,缩短了蚁群算法的搜索时间并避免陷入局部最优点。在此基础上,为进一步提高预测精度,采用马尔科夫链(MC)对预测结果进行改进,由此建立了应用于大坝变形监控的GACO-BP-MC模型。工程实例分析表明,该模型在参数优化方面具有较快的寻优速率,且具有较高的拟合和预报能力。     

Reservoir Operation Optimization: A Nonstructural Solution for Control of Seepage from Lar Reservoir in Iran

Abstract

In this paper, the results of utilizing a deterministic dynamic programming model for operation of Lar Reservoir in Iran are discussed. This reservoir has experienced extensive seepage from the start of its operation. The optimization model consists of a three-step cycle, which began with the optimization of reservoir operation for a given set of streamflows. The optimal policies are then analyzed in a regression procedure to obtain a set of operating rules. After the first run, operating rules from the previous run were placed as a new constraint on the water releases with some pre-assigned tolerance and the cycle continues. The model also consisted of mathematical functions for modeling the seepage from Lar Reservoir as a function of storage head in the reservoir. The loss function in the model was also modified in order to incorporate parameters that reduce the seepage. Results of different scenarios showed the significant effect of optimal policies on reduction of seepage and increasing the reliability of water supply to Tehran Metropolitan Area. A pumping station was also proposed to utilize the inactive part of the reservoir, in access of over 100 MCM, in order to reduce the seepage. The effectiveness of different pumping capacities to reduce the seepage was also investigated.     

A Branch-and-Bound Algorithm for Optimal Pump Scheduling in Water Distribution Networks

Nowadays water distribution operation systems are accomplished with the aid of qualified professionals who use their experience in order to achieve a satisfactory performance of the several hydromechanical devices, which are part of the system, such as boosters and valves. In general, these operational rules are empirical and the main goal is to assure the availability of water for the population, with no special concerns about saving energy used in pumping systems. Besides, these empirical rules often disregard hours of lower energy rates. There are several research works concerning the developments of operational rules optimization applied to specific water distribution systems. However, in this work, a general optimization routine integrated with EPANET is presented, which allows the determination of strategic optimal rules of operation for any type of water distribution system. Moreover, a Branch-and-Bound algorithm is also used, where finding the global optimal solution is guaranteed, in admissible computational times. The water distribution system used in this work corresponds to a hypothetical network proposed in the specialized literature.     

Application of the Shuffled Frog Leaping Algorithm for the Optimization of a General Large-Scale Water Supply System

A water supply system is a complex network of pipes, canals and storage and treatment facilities that collects, treats, stores, and distributes water to consumers. Increasing population and its associated demands requires systems to be expanded and adapted over time to provide a sustainable water supply. Comprehensive design tools are needed to assist managers determine how to plan for future growth. In this study, a general large-scale water supply system model was developed to minimize the total system cost by integrating a mathematical supply system representation and applying an improved shuffled frog leaping algorithm optimization scheme (SFLA). The developed model was applied to two hypothetical water communities. The operational strategies and the capacities for the system components including water transport and treatment facilities are model decision variables. An explicit representation of energy consumption cost for the transporting water in the model assists in determining the efficacy of satellite wastewater treatment facilities. Although the water supply systems studied contained highly nonlinear terms in the formulation as well as several hundred decisions variables, the stochastic search algorithm, SFLA, successfully found solutions that satisfied all the constraints for the studied networks.     

Offline Optimization of Sluice Control Rules in the Urban Water System for Flooding Mitigation

A real-time control (RTC) system can substantially improve the efficiency in urban flooding mitigation by optimizing the capacity of drainage and storage in existing drainage systems. However, few studies have investigated the RTC for an Urban Water System (UWS) in cities with a high water surface ratio for flooding mitigation. In this study, control rules of the actuators in an UWS were developed by an offline optimization system that combines a hydraulic model, the stormwater management model (SWMM) with an optimization model solved by the differential evolution (DE) algorithm. Iteratively simulating only the downstream UWS hydraulic model, the objectives of this study were to (i) minimize the flooding volume from the UWS and (ii) minimize the cumulative water depths above the control lines in real-time. Results showed that the optimal control rules outperformed the current fully open and closed rules, indicating the UWS’s retaining and draining capacity was effectively utilized through the offline optimization. This study also found that the robust control rules might be biased and have little effect under most rainfalls, especially mild storms, as they are derived from the system’s average performance under various rainfall conditions.

     

Optimization of Irrigation Scheduling Using Soil Water Balance and Genetic Algorithms

In arid and semi-arid countries, the use of irrigation is essential to ensure agricultural production. Irrigation water use is expected to increase in the near future due to several factors such as the growing demand of food and biofuel under a probable climate change scenario. For this reason, the improvement of irrigation water use efficiency has been one of the main drivers of the upgrading process of irrigation systems in countries like Spain, where irrigation water use is around 70 % of its total water use. Pressurized networks have replaced the obsolete open-channel distribution systems and on farm irrigation systems have been also upgraded incorporating more efficient water emitters like drippers or sprinklers. Although pressurized networks have significant energy requirements, increasing operational costs. In these circumstances farmers may be unable to afford such expense if their production is devoted to low-value crops. Thus, in this work, a new approach of sustainable management of pressurized irrigation networks has been developed using multiobjective genetic algorithms. The model establishes the optimal sectoring operation during the irrigation season that maximize farmer’s profit and minimize energy cost at the pumping station whilst satisfying water demand of crops at hydrant level taking into account the soil water balance at farm scale. This methodology has been applied to a real irrigation network in Southern Spain. The results show that it is possible to reduce energy cost and improve water use efficiency simultaneously by a comprehensive irrigation management leading, in the studied case, to energy cost savings close to 15 % without significant reduction of crop yield.     

玛纳斯河灌区地表水与地下水联合调度模型

通过分析新疆农八师玛纳斯河灌区水资源供需状况,结合灌区的灌溉系统、农作物种类及灌溉制度,建立了以灌区地下水抽水总量最小为主要目标,以灌区农业经济总效益最大为次要目标的地表水与地下水联合调度模型,并采用目标规划法对模型进行了求解,得到了在不同保证率下灌区地表水和地下水联合调度方案及农作物种植面积。结果表明:灌区供需水高峰期不一致和灌溉系统的蓄水、输配水能力不足是产生灌区水资源供需矛盾的主要原因,应适当调整灌区农业种植结构,改造灌区水利工程。