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.     

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.     

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.     

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.     

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.     

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...     

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.     

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.     

Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources

Ground management problems are typically solved by the simulation-optimization approach where complex numerical models are used to simulate the groundwater flow and/or contamination transport. These numerical models take a lot of time to solve the management problems and hence become computationally expensive. In this study, Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) models were developed and coupled for the management of groundwater of Dore river basin in France. The Analytic Element Method (AEM) based flow model was developed and used to generate the dataset for the training and testing of the ANN model. This developed ANN-PSO model was applied to minimize the pumping cost of the wells, including cost of the pipe line. The discharge and location of the pumping wells were taken as the decision variable and the ANN-PSO model was applied to find out the optimal location of the wells. The results of the ANN-PSO model are found similar to the results obtained by AEM-PSO model. The results show that the ANN model can reduce the computational burden significantly as it is able to analyze different scenarios, and the ANN-PSO model is capable of identifying the optimal location of wells efficiently.     

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。     

The Use of NARX Neural Networks to Forecast Daily Groundwater Levels

The lack of information to manage groundwater for irrigation is one of the biggest concerns for farmers and stakeholders in agricultural areas of Mississippi. In this study, we present a novel implementation of a nonlinear autoregressive with exogenous inputs (NARX) network to simulate daily groundwater levels at a local scale in the Mississippi River Valley Alluvial (MRVA) aquifer, located in the southeastern United States. The NARX network was trained using the Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms, and the results were compared to identify an optimal architecture for the forecasting of daily groundwater levels over time. The training algorithms were implemented using different hidden node combinations and delays (5, 25, 50, 75, and 100) until the optimal network was found. Eight years of daily historical input time series including precipitation and groundwater levels were used to forecast groundwater levels up to three months ahead. The comparison between LM and BR showed that NARX-BR is superior in forecasting daily levels based on the Mean Squared Error (MSE), coefficient of determination (R²), and Nash-Sutcliffe coefficient of efficiency. The results showed that BR with two hidden nodes and 100 time delays provided the most accurate prediction of groundwater levels with an error of ± 0.00119 m. This innovative study is the first of its kind and will provide significant contributions for the implementation of data-based models (DBMs) in the prediction and management of groundwater for agricultural use.     

Sustainable Conjunctive Water Use Modeling Using Dual Fitness Particle Swarm Optimization Algorithm

Water Resources Management - In any meta-heuristic algorithm, each search agent must move to the high-fitness areas in the search space while preserving its diversity. At first glance, there is no...     

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...     

傍河水源地地表水地下水转化SFR模型的应用

傍河水源地开采地下水是一个地表水与地下水转化的复杂过程。目前,模拟傍河开采量的数值模型常把河流概化为第一类定水头边界或河流边界。第一类定水头边界处理把河流概化为无限补给水源,对季节性河流来说明显不切实际,而河流边界不具备SFR模型的汇流功能。为弥补第一类定水头边界模型及河流边界模型对地表水与地下水转化量计算精度不够的缺陷,采用SFR模型概化白马河-吉利河,设置河床与地下水之间的河床水力传导系数、河床厚度、水深及断面形状等,将SFR模型与MODFLOW模型进行耦合,计算丰、平、枯水期河流流量与上下游水位,建立SFR地表水与地下水耦合模型。分别使用第一类定水头边界模型和河流边界模型概化河流,将SFR耦合模型、第一类定水头边界模型及河流边界模型的模拟结果进行对比,结果表明SFR耦合模型在表达丰、平、枯水期地表水与地下水转化关系及转化量计算方面更为准确。按SFR耦合模型、第一类定水头边界模型和河流边界模型计算的研究区地下水可开采量分别为2 779万、3 511万、2 576万m~3/a,SFR耦合模型模拟结果与研究区已知的地下水可开采量2 870万m~3/a更为接近,所得结论更为合理。     

Conjunctive Water Use Optimization for Watershed-Lake Water Distribution System under Uncertainty: a Case Study

Water Resources Management - In this research, a large-scale inexact optimization method was developed for the conjunctive use management of a watershed-lake water distribution system. The modeling...     

A Numerical Simulation Model for Conjunctive Water Use in Basin Irrigated Canal Command Areas

Basin irrigation is a common practice for growing water intensive crops like paddy. Irrigation water, when supplied through a network of canal, is often found to be inadequate to meet the crop water requirement uniformly throughout the irrigated command area. The most deprived are the cultivators of the lower end of the command, who resort to supplementing the crop water requirement by extractions from the ground. This practice is noticeable in irrigation system without a proper canal water distribution schedule and often result in water logging in the upper command regions contrasted with excessively depleted groundwater table in the lower commands. The present contribution attempts to model the conjunctive water use of such a canal irrigated command using physically based numerical sub-models for simulating surface flow, groundwater flow and the interlinking process of moisture movement through the unsaturated zone for a given quantum of supplied water and crop water demand. Individual models are validated to demonstrate their applicability in an integrated framework. Various plausible conjunctive water use scenarios are tested on a hypothetical command area practising basin irrigation to identify the best possible water distribution strategy under given constraints.