Application of Artificial Neural Networks for Identifying Optimal Groundwater Pumping and Piping Network Layout

The simulation-optimization approach is often used to solve water resource management problem although repeated use of the simulation model enhances the computational load. In this study, Artificial Neural Network (ANN) and Bagged Decision Trees (BDT) models were developed as an approximator for Analytic Element Method (AEM) based groundwater flow model. Developed ANN and BDT models were coupled with Particle Swarm Optimization (PSO) model to solve the well-field management problem. The groundwater flow model was developed for the study area and used to generate the dataset for the training and testing of the ANN & BDT models. These coupled ANN-PSO & BDT-PSO models were employed to find the optimal design and cost of the new well-field system by optimizing discharge & co-ordinate of wells along with the cost effective layout of piping network. The Minimum Spanning Tree (MST) based model was used to find out the optimal piping network layout and checking the hydraulic constraints in the piping network. The results show that the ANN & BDT models are good approximators of AEM model and they can reduce the computational burden significantly although ANN model performs better than BDT model. The results show that the coupling of piping network model with simulation-optimization model is very significant for finding the cost effective and realistic design of the new well-field system.     

Optimal Groundwater Management in Deltaic Regions using Simulated Annealing and Neural Networks

This study deals with the optimal management of groundwater in deltaic aquifer systems with some reference to east coastal hydro-geo-climatic conditions of India. A system of cooperative wells is proposed to supplement surface water sources to meet the demand during the non-monsoon season, without inducing excessive saltwater intrusion. The management models are solved as nonlinear, non-convex, combinatorial problems. The management models are solved by interfacing simulated annealing (SA) algorithm with an existing SHARP interface flow model to determine an optimal policy for location and pumpages of cooperative wells. Computational burden arising from SA algorithm is managed within practical timeframes by replacing the simulator with an artificial neural network (ANN).     

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

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

     

A New Groundwater Management Model by Coupling Analytic Element Method and Reverse Particle Tracking with Cat Swarm Optimization

The performance of groundwater management models mostly depends upon the methodology employed to simulate flow and transport processes and the efficiency of optimization algorithms. The present study examines the effectiveness of cat swarm optimization (CSO) for groundwater management problems, by coupling it with the analytic element method (AEM) and reverse particle tracking (RPT). In this study, we propose two coupled simulation-optimization models, viz. AEM-CSO and AEM-RPT-CSO by combining AEM with RPT and CSO. Both the models utilize the added advantages of AEM, such as precise estimation of hydraulic head at pumping location and generation of continuous velocity throughout the domain. The AEM-CSO model is applied to a hypothetical unconfined aquifer considering two different objectives, i.e., maximization of the total pumping of groundwater from the aquifer and minimization of the total pumping costs. The model performance reflects the superiority of CSO in comparison with other optimization algorithms. Further, the AEM-RPT-CSO model is successfully applied to a hypothetical confined aquifer to minimize the total number of contaminant sources, within the time related capture zone of the wells, while maintaining the required water demand. In this model, RPT gets continuous velocity information directly from the AEM model. The performance evaluation of the proposed methodology, illustrates its ability to solve groundwater management problems.     

Comparative Evaluation of Genetic Programming and Neural Network as Potential Surrogate Models for Coastal Aquifer Management

Determining the optimal rates of groundwater extraction for the sustainable use of coastal aquifers is a complex water resources management problem. It necessitates the application of a 3D simulation model for coupled flow and transport simulation together with an optimization algorithm in a linked simulation-optimization framework. The use of numerical models for aquifer simulation within optimization models is constrained by the huge computational burden involved. Approximation surrogates are widely used to replace the numerical simulation model, the widely used surrogate model being Artificial Neural Networks (ANN). This study evaluates genetic programming (GP) as a potential surrogate modeling tool and compares the advantages and disadvantages with the neural network based surrogate modeling approach. Two linked simulation optimization models based on ANN and GP surrogate models are developed to determine the optimal groundwater extraction rates for an illustrative coastal aquifer. The surrogate models are linked to a genetic algorithm for optimization. The optimal solutions obtained using the two approaches are compared and the advantages of GP over the ANN surrogates evaluated.     

Artificial Neural Network and Grey Wolf Optimizer Based Surrogate Simulation-Optimization Model for Groundwater Remediation

We herein propose a simulation-optimization model for groundwater remediation, using PAT (pump and treat), by coupling artificial neural network (ANN) with the grey wolf optimizer (GWO). The input and output datasets to train and validate the ANN model are generated by repetitively simulating the groundwater flow and solute transport processes using the analytic element method (AEM) and random walk particle tracking (RWPT). The input dataset is the different realization of the pumping strategy and output dataset are hydraulic head and contaminant concentration at predefined locations. The ANN model is used to approximate the flow and transport processes of two unconfined aquifer case studies. The performance evaluation of the ANN model showed that the value of mean squared error (MSE) is close to zero and the value of the correlation coefficient (R) is close to 0.99. These results certainly depict high accuracy of the ANN model in approximating the AEM-RWPT model. Further, the ANN model is coupled with the GWO and it is used for remediation design using PAT. A comparison of the results of the ANN-GWO model with solutions of ANN-PSO (ANN-Particle Swarm Optimization) and ANN-DE (ANN-Differential Evolution) models illustrates the better stability and convergence behaviour of the proposed methodology for groundwater remediation.

     

Artificial Neural Network (ANN) Based Modeling for Karstic Groundwater Level Simulation

A relatively new method of addressing different hydrological problems is the use of artificial neural networks (ANN). In groundwater management ANNs are usually used to predict the hydraulic head at a well location. ANNs can prove to be very useful because, unlike numerical groundwater models, they are very easy to implement in karstic regions without the need of explicit knowledge of the exact flow conduit geometry and they avoid the creation of extremely complex models in the rare cases when all the necessary information is available. With hydrological parameters like rainfall and temperature, as well as with hydrogeological parameters like pumping rates from nearby wells as input, the ANN applies a black box approach and yields the simulated hydraulic head. During the calibration process the network is trained using a set of available field data and its performance is evaluated with a different set. Available measured data from Edward??s aquifer in Texas, USA are used in this work to train and evaluate the proposed ANN. The Edwards Aquifer is a unique groundwater system and one of the most prolific artesian aquifers in the world. The present work focuses on simulation of hydraulic head change at an observation well in the area. The adopted ANN is a classic fully connected multilayer perceptron, with two hidden layers. All input parameters are directly or indirectly connected to the aquatic equilibrium and the ANN is treated as a sophisticated analogue to empirical models of the past. A correlation analysis of the measured data is used to determine the time lag between the current day and the day used for input of the measured rainfall levels. After the calibration process the testing data were used in order to check the ability of the ANN to interpolate or extrapolate in other regions, not used in the training procedure. The results show that there is a need for exact knowledge of pumping from each well in karstic aquifers as it is difficult to simulate the sudden drops and rises, which in this case can be more than 6 ft (approx. 2 m). That aside, the ANN is still a useful way to simulate karstic aquifers that are difficult to be simulated by numerical groundwater models.     

Integration of Optimal Dynamic Control and Neural Network for Groundwater Quality Management

This study integrates an artificial neural network (ANN) and constrained differential dynamic programming (CDDP) to search for optimal solutions to a nonlinear time-varying groundwater remediation-planning problem. The proposed model (ANN-CDDP) determines optimal dynamic pumping schemes to minimize operating costs and meet water quality requirements. The model uses two embedded ANNs, including groundwater flow and contaminant transport models, as transition functions to predict groundwater levels and contaminant concentrations under time-varying pumping. Results demonstrate that ANN-CDDP is a simplified management model that requires considerably less computation time to solve a fine mesh problem. For example, the ANN-CDDP computing time for a case involving 364 nodes is 1/26.5 that of the conventional optimization model.     

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.     

Planning Groundwater Development in Coastal Deltas with Paleo Channels

In this study, a management model is presented for planning groundwater development in costal deltas with paleo channels. It is demonstrated that paleo channels are the best locations for locating the wells for large-scale pumping. Groundwater flow in these aquifers is simulated using a three-dimensional (3-D) density-dependent flow and transport model SEAWAT, which is suitable for a coastal and deltaic environment. A simulation-optimization model is used to determine the optimal locations and pumpages for groundwater development for a group of production wells, while limiting the salinity below desired levels. The mixed integer problem is solved using the Simulated Annealing algorithm and the SEAWAT simulation model. A trained Artificial Neural Network (ANN) is used as the virtual SEAWAT model to perform the simulations, in order to reduce the computational burden for application of the model on desktop computers. The applicability of the model is demonstrated on a hypothetical, but near-real, delta system.     

A sensitivity analysis and parametric study for the evaluation of the optimal management of a contaminated aquifer

A typical groundwater remedation problem is studied by using a combined simulation-optimization model. The management procedure employs groundwater flow and contaminant transport simulation models in conjunction with linear and quadratic programming techniques. The methodology is applied to the hydrodynamic control of a contaminant plume that has to be stabilized and removed by a system of pumping wells. The paper focuses mainly upon a sensitivity analysis to the aquifer transmissivity. The effect of changes in the transmissivities of a zoned aquifer upon the optimal solutions of the management problem is examined by considering the optimal pumping rates, the time to remediation and the pumped groundwater volume as the key output variables of the remediation strategies. In addition, the influence of the dispersivities and the imposed hydraulic gradient upon the same output variables is critically evaluated. The results of the study illustrate the need for uncertainty reduction in the knowledge of the hydrogeologic parameters.     

Simulation of rainfall-underground outflow responses of a karstic watershed in Southwest China with an artificial neural network

Karstic aquifers in Southwest China are largely located in mountainous areas and groundwater level observation data are usually absent. Therefore, numerical groundwater models are inappropriate for simulation of groundwater flow and rainfall-underground outflow responses. In this study, an artificial neural network （ANN） model was developed to simulate underground stream discharge. The ANN model was applied to the Houzhai subterranean drainage in Guizhou Province of Southwest China, which is representative of karstic geomorphology in the humid areas of China. Correlation analysis between daily rainfall and the outflow series was used to determine the model inputs and time lags. The ANN model was trained using an error backpropagation algorithm and validated at three hydrological stations with different karstic features. Study results show that the ANN model performs well in the modeling of highly non-linear karstic aquifers.     

Groundwater Pumping Cost Minimization – an Analytical Approach

In this paper, a groundwater resources management problem has been studied, namely pumping cost minimization for any number and layout of wells. Steady state flow in infinite and semi-infinite confined aquifers, to which the method of images applies, has been considered. It has been proved analytically that when pumping cost is minimized, hydraulic head is the same at all wells. Based on this proof, an analytical calculation procedure of the respective optimal distribution of the required total flow rate to the individual wells is also presented.     

Planning of Groundwater Supply Systems Subject to Uncertainty Using Stochastic Flow Reduced Models and Multi-Objective Evolutionary Optimization

The typical modeling approach to groundwater management relies on the combination of optimization algorithms and subsurface simulation models. In the case of groundwater supply systems, the management problem may be structured into an optimization problem to identify the pumping scheme that minimizes the total cost of the system while complying with a series of technical, economical, and hydrological constraints. Since lack of data on the subsurface system most often reflects upon the development of groundwater flow models that are inherently uncertain, the solution to the groundwater management problem should explicitly consider the tradeoff between cost optimality and the risk of not meeting the management constraints. This work addresses parameter uncertainty following a stochastic simulation (or Monte Carlo) approach, in which a sufficiently large ensemble of parameter scenarios is used to determine representative values selected from the statistical distribution of the management objectives, that is, minimizing cost while minimizing risk. In particular, the cost of the system is estimated as the expected value of the cost distribution sampled through stochastic simulation, while the risk of not meeting the management constraints is quantified as the expected value of the intensity of constraint violation. The solution to the multi-objective optimization problem is addressed by combining a multi-objective evolutionary algorithm with a stochastic model simulating groundwater flow in confined aquifers. Evolutionary algorithms are particularly appropriate in optimization problems characterized by non-linear and discontinuous objective functions and constraints, although they are also computationally demanding and require intensive analyses to tune input parameters that guarantee optimality to the solutions. In order to drastically reduce the otherwise overwhelming computational cost, a novel stochastic flow reduced model is thus developed, which practically allows for averting the direct inclusion of the full simulation model in the optimization loop. The computational efficiency of the proposed framework is such that it can be applied to problems characterized by large numbers of decision variables.     

Managing Saline Water Intrusion in the Lower Indus Basin Aquifer

In view of the declining surface water sources for irrigated agriculture in Pakistan, farmers are compelled to extract groundwater in order provide to security against uncertain canal supplies during critical crop growth periods. However saline water intrusion can be a major hindrance to the sustainable groundwater development. Against this background, a study was conducted with a three dimensional finite element model (FEMGWST) based on the Galerkin weighted residual method being developed to simulate groundwater flow and the saline water intrusion from underlying poor quality aquifer in response to groundwater pumping through low capacity partially penetrated wells. The model was calibrated with field data collected in the district Khairpur of the Lower Indus Basin. The stability of the model for transient groundwater flow and solute transport against different time marching schemes were evaluated. This study showed that the explicit and the Crank-Nicolson time marching schemes developed the numerical oscillating, the global error and the convergence problem. The calibrated model was applied to predict the impacts of different well configurations on the pumped water quality and on the development of saline water mound at the bottom of the well. It was observed that the saline water intrusion into the fresh groundwater layer was directly related to the well discharge, pumping time and inversely to the thickness of fresh-saline water interface and the number of well strainers installed. The model results suggested that intermittent pumping through multi strainer wells could effectively be used to suppress the saline water intrusion. However multi strainers wells were found to induce saline water intrusion when the thickness of fresh-saline water interface was reduced to 4 m.     

Alternatives to Reduce Pumping Effects in Glacial Stratified Drift Aquifers During Periods of Low Stream Flow

Low stream flows in the Fenton River, part of a hydrogeological setting characterized by glacial stratified drift, forces the University of Connecticut to frequently reduce groundwater withdrawals during the months of June–October. The objective of this study was to investigate stream/aquifer interactions in such a hydrogeologic system in order to increase water withdrawals while minimizing adverse impacts to in-stream flow. A groundwater flow model was developed using MODFLOW to investigate the influence of well location and pumping timing on in-stream flow in the vicinity of the water supply wells. The numerical model comprised detailed geophysical data and decadal hydrologic data (2000–2009) to assess well placement, rest periods and cyclical pumping. The relocation of a water supply well up to 228 m from the river had a positive but minimal improvement to stream flows (<2.83 L/s). When the well field was shut off for more than 45 days, stream flows returned to the no pumping condition with only slight impact at 30 days, whereas a 30 day rest period gave 4 weeks of dampened pumping influence on stream flows. A management scenario of 1 week cyclical pumping between two water supply wells following a 45 day rest period can allow for current restriction thresholds to be reduced by 28.3 L/s with minimal impact to stream flows (7.36 L/s) and would allow additional water to be pumped for all years in which there was a demand for water.     

Groundwater Flow Modeling of the Arlington Basin to Evaluate Management Strategies for Expansion of the Arlington Desalter Water Production

Water supply reliability in Southern California is facing serious problems because of reduction in the availability of water from the State Water Project and Colorado River, drought, and growing concerns about environmental restoration. Groundwater sources supply more than fifty-five percent of domestic demands in the Western Riverside County. Western Municipal Water District is planning to increase water supply reliability by expanding the Arlington Desalter production which requires additional groundwater pumping from the Arlington Basin. Western was concerned that increasing groundwater pumping will cause excessive decline in groundwater levels, leading to decreased yields at existing Desalter wells. Three-dimensional groundwater flow model was developed for the Arlington Basin to investigate different water management strategies. Five groundwater management scenarios were run for a 30-year time period. The five model runs were used to determine the feasibility of the Arlington aquifer system to supply groundwater to the Arlington Desalter over the 30-year life of the facility. Model simulation results showed that long-term groundwater pumping from the existing Desalter wells is not sustainable without artificial recharge. However two of the modeling scenarios which incorporated a combination of artificial recharge and new production wells, were shown to meet the increased Desalter yield requirements as well as minimize adverse impacts.     

Simulation-Optimization Modelling for Sustainable Groundwater Management in a Coastal Basin of Orissa, India

The Balasore coastal groundwater basin of Orissa in eastern India is under a serious threat of overdraft and seawater intrusion. Two optimization models were developed in this study for the efficient utilization of water resources in Balasore basin during non-monsoon periods: (a) a non-linear hydraulic management model for optimal pumpage, and (b) a linear optimization model for optimal cropping pattern in integration with a calibrated and validated groundwater flow simulation model. Based on the simulation-optimization modeling results, optimal pumping schedules, cropping patterns, and corresponding groundwater conditions are presented for three scenarios viz., wet, normal and dry years. It was found that optimal pumping schedules and corresponding cropping patterns differed significantly under the three scenarios, and the groundwater levels improved significantly under the optimal hydraulic conditions compared to the existing condition. In dry years, the groundwater levels under the present pumping pattern and the optimal pumpage indicated that the non-monsoon pumpage should not exceed the optimal pumpage in the absence of remedial measures in the basin. It is concluded that in order to ensure sustainable groundwater utilization in the basin, the optimal cropping pattern and pumping schedule should be adopted by the farmers.     

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.     

Optimal Management of Coastal Aquifers Using Linked Simulation Optimization Approach

Saltwater intrusion management models can be used to derive optimal and efficient management strategies for controlling saltwater intrusion in coastal aquifers. To obtain physically meaningful optimal management strategies, the physical processes involved need to be simulated while deriving the management strategies. The flow and transport processes involved in coastal aquifers are difficult to simulate especially when the density-dependent flow and transport processes need to be modeled. Incorporation of this simulation model within an optimization-based management model is very complex and difficult. However, as an alternative, it is possible to link a simulation model externally with an optimization-based management model. The GA-based optimization approach is especially suitable for externally linking the numerical simulation model within the optimization model. Further efficiency in computational procedure can be achieved for such a linked model, if the simulation process can be simplified by approximation, as very large number of iterations between the optimization and simulation model is generally necessary to evolve an optimal management strategy. A possible approach for approximating the simulation model is to use a trained Artificial Neural Network (ANN) as the approximate simulator. Therefore, an ANN model is trained as an approximator of the three dimensional density-dependent flow and transport processes in a coastal aquifer. A linked simulation – optimization model is then developed to link the trained ANN with the GA-based optimization model for solving saltwater management problems. The performance of the developed optimization model is evaluated using an illustrative study area. The evaluation results show the potential applicability of the developed methodology using a GA- and ANN-based linked optimization – simulation model for optimal management of coastal aquifer.