Application of Genetic Algorithms and Artificial Neural Networks in Conjunctive Use of Surface and Groundwater Resources

Abstract

In this paper, a methodology for conjunctive use of surface and groundwater resources is developed using the combination of the Genetic Algorithms (GAs) and the Artificial Neural Networks (ANN). Water supply to agricultural demands, reduction of pumping costs and control of groundwater table fluctuations are considered in the objective function of the model. In the proposed model, the results of MODFLOW groundwater simulation model are used to train an ANN. The ANN as groundwater response functions is then linked to the GA based optimization model to develop the monthly conjunctive use operating policies. The model is applied to the surface and groundwater allocation for irrigation purposes in the southern part of Tehran. A new ANN is also trained and checked for developing the real-time conjunctive use operating rules.

The results show the significance of an integrated approach to surface and groundwater allocation in the study area. A simulation of the optimal policies shows that the cumulative groundwater table variation can be reduced to less than 4 meters from the current devastating condition. The results also show that the proposed model can effectively reduce the run time of the conjunctive use models through the composition of a GA-based optimization and a ANN-based simulation model.     

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.     

Application of Neural Networks and Optimization Model in Conjunctive Use of Surface Water and Groundwater

This study develops an optimization model for the large-scale conjunctive use of surface water and groundwater resources. The aim is to maximize public and irrigation water supplies subject to groundwater-level drawdown constraints. Linear programming is used to create the optimization model, which is formulated as a linear constrained objective function. An artificial neural network is trained by a flow modeling program at specific observation wells, and the network is then incorporated into the optimization model. The proposed methodology is applied to the Chou-Shui alluvial fan system, located in central Taiwan. People living in this region rely on large quantities of pumped water for their public and irrigation demands. This considerable dependency on groundwater has resulted in severe land subsidence in many coastal regions of the alluvial fan. Consequently, an efficient means of implementing large-scale conjunctive use of surface water and groundwater is needed to prevent further overuse of groundwater. Two different optimization scenarios are considered. The results given by the proposed model show that water-usage can be balanced with a stable groundwater level. Our findings may assist officials and researchers in establishing plans to alleviate land subsidence problems.     

Groundwater in Water Resources Planning: Conjunctive Use

ABSTRACT

Many water resources planning specialists underrate or overlook groundwater resources. In many cases, the reasons which are usually expressed belie a lack of confidence in their potential and in the ability of today's technology to predict their behavior. The area occupied by aquifers and the fact that they account for 30% of stream discharges, are strong arguments in favor of utilizinggroundwater resources. Likewise, with present groundwater evaluation techniques and the availability of models and simulation methods, the impact of long-term groundwater exploitation can be evaluated with as great a degree of accuracy as the methods used for surface water. Very often groundwater resources are economically more favorable than surface water; however, in regions where water resources are scarce, the problem should not be one of competition but collaboration. The correct approach is the conjunctive use of surface and groundwater resources. Through conjunctive use additional water resources can be obtained, additional means of water storage, distribution and treatment can be made available, as well as achieving greater efficiency in water resources system management.     

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.     

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 Planning for Conjunctive Use of Surface and Ground Water Resources Using Genetic Programming

Water Resources Management - In arid and semi-arid regions, climate change causes a drastic decline in the volume of water resources as water demands increase. Thus, the present study is aimed at...     

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.     

Water Resources Management in the Ganges Basin: A Comparison of Three Strategies for Conjunctive Use of Groundwater and Surface Water

The most difficult water resources management challenge in the Ganges Basin is the imbalance between water demand and seasonal availability. More than 80 % of the annual flow in the Ganges River occurs during the 4-month monsoon, resulting in widespread flooding. During the rest of the year, irrigation, navigation, and ecosystems suffer because of water scarcity. Storage of monsoonal flow for utilization during the dry season is one approach to mitigating these problems. Three conjunctive use management strategies involving subsurface water storage are evaluated in this study: Ganges Water Machine (GWM), Pumping Along Canals (PAC), and Distributed Pumping and Recharge (DPR). Numerical models are used to determine the efficacy of these strategies. Results for the Indian State of Uttar Pradesh (UP) indicate that these strategies create seasonal subsurface storage from 6 to 37 % of the yearly average monsoonal flow in the Ganges exiting UP over the considered range of conditions. This has clear implications for flood reduction, and each strategy has the potential to provide irrigation water and to reduce soil waterlogging. However, GWM and PAC require significant public investment in infrastructure and management, as well as major shifts in existing water use practices; these also involve spatially-concentrated pumping, which may induce land subsidence. DPR also requires investment and management, but the distributed pumping is less costly and can be more easily implemented via adaptation of existing water use practices in the basin.     

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.     

Decision Tree-Based Conditional Operation Rules for Optimal Conjunctive Use of Surface and Groundwater

Water Resources Management - Conjunctive use (CU) of surface water (SW) and groundwater (GW) implies the optimal operation of water resources to reduce the negative effects compared to when each of...     

Multi-objective Planning for Conjunctive Use of Surface and Subsurface Water Using Genetic Algorithm and Dynamics Programming

The consideration of fixed cost and time-varying operating cost associated with the simultaneous conjunctive use of surface and subsurface water should be treated as a multi-objective problem due to the conflicting characteristics of these two objectives. In order to solve this multi-objective problem, a novel approach is developed herein by integrating the multi-objective genetic algorithm (MOGA), constrained differential dynamic programming (CDDP) and the groundwater simulation model ISOQUAD. A MOGA is used to generate the various fixed costs of reservoirs’ scale, generate a pattern of pumping/recharge, and estimate the non-inferior solutions set. A groundwater simulation model ISOQUAD is directly embedded to handle the complex dynamic relationship between the groundwater level and the generated pumping/recharge pattern. The CDDP optimization model is then adopted to distribute the optimal releases among reservoirs provided that reservoir capacities are known. Finally, the effectiveness of our proposed integrated model is verified by solving a water resources planning problem for the conjunctive use of surface and subsurface water in southern Taiwan.     

Water Quality Zoning Using Probabilistic Support Vector Machines and Self-Organizing Maps

Water quality zoning is an important step for studying and evaluating surface and groundwater quality variations with time and space. It can also provide important information for developing efficient water quality management strategies. Most common methods for water quality zoning do not consider the uncertainties associated with water resources systems. Also, these methods only categorize the water quality monitoring stations into some classes based on existing water quality but do not provide any information about the quality of water which is categorized in a class. In this paper, a new methodology for probabilistic water quality zoning is developed which utilizes the capabilities of Probabilistic Support Vector Machines (PSVMs) and Fuzzy Inference System (FIS). The required data for training the PSVM is generated by using the FIS in a Monte Carlo Analysis. The trained PSVM-based water quality index provides the probabilities of belonging quality of water to different water quality classes. The applicability of the proposed methodology is investigated by applying it to two surface and groundwater resources systems in Iran. Also, for more evaluating the efficiency of the methodology, the results are compared with those obtained from two clustering techniques, namely Fuzzy Clustering Technique (FCT) and Self-Organizing Map (SOM). The results of surface and ground water quality zoning are depicted in maps by utilizing the Geographic Information System (GIS).     

A Comparative Assessment of Support Vector Machines,Probabilistic Neural Networks,and K-Nearest Neighbor Algorithms for Water Quality Classification

Water quality is one of the major criteria for determining the planning and operation policies of water resources systems. In order to classify the quality of a water resource such as an aquifer, it is necessary that the quality of a large number of water samples be determined, which might be a very time consuming process. The goal of this paper is to classify the water quality using classification algorithms in order to reduce the computational time. The question is whether and to what extent the results of the classification algorithms are different. Another question is what method provides the most accurate results. In this regard, this paper investigates and compares the performance of three supervised methods of classification including support vector machine (SVM), probabilistic neural network (PNN), and k-nearest neighbor (KNN) for water quality classification. Using two performance evaluation statistics including error rate and error value, the efficiency of the algorithms is investigated. Furthermore, a 5-fold cross validation is performed to assess the effect of data value on the performance of the applied algorithms. Results demonstrate that the SVM algorithm presents the best performance with no errors in calibration and validation phases. The KNN algorithm, having the most total number and total value of errors, is the weakest one for classification of water quality data.     

Conjunctive Use of Surface and Underground Water in the Istra Peninsula,Yugoslavia

ABSTRACT

Based on an analysis of jive types of water issues in China, this article presents a picture of the present condition of water problems in China, which are worsening with social and economic development. From the viewpoint of water resources, four important concepts about water issues have been formulated and a policy for coordinating water issues and sustainable social and economic development has been proposed.     

Conjunctive Management of Large-Scale Pressurized Water Distribution and Groundwater Systems in Semi-Arid Area with Parallel Genetic Algorithm

This study develops a production well management model for the conjunctive management of water resources in semi-arid areas. The management model integrates a large-scale pressurized water distribution system and a three-dimensional groundwater model under an optimization framework. The well pump operations optimization problem is formulated as a Boolean integer nonlinear programming (BINLP) problem to optimize the periodic 24-h pump on/off operations over a 1-week operation horizon. The management model considers multiple objectives and is solved by a parallel genetic algorithm (PGA) to overcome the difficulty of solving the BINLP problem. The PGA significantly reduces computation time for a case study in Chandler, Arizona. The Chandler water distribution model is built based on EPANET, and the Chandler three-dimension groundwater model is developed using MODFLOW. The high performance computing (HPC) of the genetic algorithm makes it possible to obtain 24-h real-time operations in the 7-day forecast model. The tank reliability, resilience, and vulnerability (R-R-V) are evaluated to infer the system reliability. The Pareto curve provides compromise solutions between the two competing objectives of energy reduction and pressure violation reduction.     

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。     

基于多种优化支持向量机及V/S分析法的隧道变形预测及趋势判断

隧道变形具有明显的非线性特征,为实现其准确预测,基于卡尔曼滤波和多种优化的支持向量机模型对隧道变形进行预测,以探讨不同预测模型的适用性,并进一步进行组合预测;同时,利用V/S分析法计算变形序列的Hurst指数,以判断隧道的变形趋势,并与预测结果进行对比,综合判断隧道的变形规律。结果表明:最小二乘支持向量机的优化效果最好,且预测隧道后4个周期均为增长变形;同时,变形序列和速率序列V/S分析的Hurst指数分别为0.845和0.602,均>0.5,得出隧道后期变形呈持续增长趋势,与预测分析一致,验证了思路的有效性。     

Conserving One Water Source at the Expense of Another: The Role of Surface Water Price in Adoption of Wells in a Conjunctive Use System

One potential side-effect of irrigation water rate reform is groundwater substitution. As surface water prices rise, irrigators may find it cheaper to rely on on-farm wells than a regional irrigation district. The impact of surface water price on well adoption is examined in a conjunctive use system where both surface water and groundwater are used to meet irrigator demand. Results indicate that as the price of surface water approaches 62% of the marginal cost of pumping groundwater, irrigators are more likely to have on-farm pumping capabilities. This result suggests that proposed water rate reforms by the United States Bureau of Reclamation may result in irrigators substituting groundwater for surface water by adopting on-farm wells.     

MATLAB遗传算法工具箱(GAOT)在水资源优化计算中的应用

阐述了遗传算法的基本原理,对MATLAB遗传算法工具箱（GAOT）的参数使用进行了详细介绍,探讨了MATLAB遗传算法工具箱（GAOT）在水资源非线性规划和多目标规划时的应用,并用简单实例证明了这种应用具有良好的通用性、可行性和简便性,可以得到较满意的优化计算结果。