Efficient Identification of Unknown Groundwater Pollution Sources Using Linked Simulation-Optimization Incorporating Monitoring Location Impact Factor and Frequency Factor

This study aims to improve the accuracy of groundwater pollution source identification using concentration measurements from a heuristically designed optimal monitoring network. The designed network is constrained by the maximum number of permissible monitoring locations. The designed monitoring network improves the results of source identification by choosing monitoring locations that reduces the possibility of missing a pollution source, at the same time decreasing the degree of non uniqueness in the set of possible aquifer responses to subjected geo-chemical stresses. The proposed methodology combines the capability of Genetic Programming (GP), and linked simulation-optimization for recreating the flux history of the unknown conservative pollutant sources with limited number of spatiotemporal pollution concentration measurements. The GP models are trained using large number of simulated realizations of the pollutant plumes for varying input flux scenarios. A selected subset of GP models are used to compute the impact factor and frequency factor of pollutant source fluxes, at candidate monitoring locations, which in turn is used to find the best monitoring locations. The potential application of the developed methodology is demonstrated by evaluating its performance for an illustrative study area. These performance evaluation results show the efficiency in source identification when concentration measurements from the designed monitoring network are utilized.     

Optimal Groundwater Remediation Under Uncertainty Using Multi-objective Optimization

A methodology is developed for optimal remediation of groundwater aquifers under hydraulic conductivity uncertainty. A multi-objective management method based on a pump-and-treat remediation technology, is proposed. The pumping rates and well locations are the decision variables and two objectives are chosen: minimization of contaminated groundwater in the aquifer and minimization of remediation cost. A Monte Carlo simulation method is used to cope with hydraulic conductivity uncertainty. A number of equally probable realizations of hydraulic conductivity are created and a Pareto front is obtained using a modified multi-objective Genetic Algorithm. A penalty function is utilized to maintain the algebraic sum of pumping and recharging rates equal to zero. Since Monte Carlo simulations are CPU time consuming, a method is proposed to identify the few significant realizations which have an effect on the optimal solution (critical realizations). A Pareto front with an assigned probability is derived, so that the decision maker can make decisions with specified reliability. In a case study with 100 realizations, only 11 realizations were found critical and need be considered. The remaining 89 realizations consistently obtain low ranks for all designs considered and do not affect decisions at 95% reliability level. Thus these realizations need not be considered which implies a 89% savings in computer time. The designs obtained using the critical realizations, retain a similar reliability for new realizations not considered in the design process.     

Multiobjective Design of Groundwater Monitoring Network Under Epistemic Uncertainty

A methodology is proposed for optimal design of groundwater quality monitoring networks under epistemic uncertainty. The proposed methodology considers spatiotemporal pollutant concentrations as fuzzy numbers. It incorporates fuzzy ordinary kriging (FOK) within the decision model formulation for spatial estimation of contaminant concentration values. A multiobjective monitoring network design model incorporating the objectives of fuzzy mass estimation error and spatial coverage of the designed network is developed. Nondominated Sorting Genetic Algorithm-II (NSGA-II) is used for solving the monitoring network design model. Performances of the proposed model are evaluated for hypothetical illustrative system. Evaluation results indicate that the proposed methodology perform satisfactorily under uncertain system conditions. These performance evaluation results demonstrate the potential applicability of the proposed methodology for optimal groundwater contaminant monitoring network design under epistemic uncertainty.     

Development and Implementation of Support Vector Machine Regression Surrogate Models for Predicting Groundwater Pumping-Induced Saltwater Intrusion into Coastal Aquifers

Predicting the extent of saltwater intrusion (SWI) into coastal aquifers in response to changing pumping patterns is a prerequisite of any groundwater management framework. This study investigates the feasibility of using support vector machine regression (SVMr), an innovative artificial intelligence-based machine learning algorithm for predicting salinity concentrations at selected monitoring wells in an illustrative aquifer under variable groundwater pumping conditions. For evaluation purpose, the prediction results of SVMr are compared with well-established genetic programming (GP) based surrogate models. SVMr and GP models are trained and validated using identical sets of input (pumping) and output (salinity concentration) datasets. The trained and validated models are then used to predict salinity concentrations at specified monitoring wells in response to new pumping datasets. Prediction capabilities of the two learning machines are evaluated using different proficiency measures to ensure their practicality and generalisation ability. The performance evaluation results suggest that the prediction capability of SVMr is superior to GP models. Also, a sensitivity analysis methodology is proposed for assessing the impact of pumping rates on salt concentrations at monitoring locations. This sensitivity analysis provides a subset of most influential pumping rates, which is used to construct new SVMr surrogate models with improved predictive capabilities. The improved prediction capability and the generalisation ability of the SVMr models together with the ability to improve the accuracy of prediction by refining the input set for training makes the use of proposed SVMr models more attractive. Prediction models with more accurate prediction capability makes it potentially very useful for designing large scale coastal aquifer management strategies.     

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.     

Stochastic and Robust Multi-Objective Optimal Management of Pumping from Coastal Aquifers Under Parameter Uncertainty

Combined simulation-optimization approaches have been used as tools to derive optimal groundwater management strategies to maintain or improve water quality in contaminated or other aquifers. Surrogate models based on neural networks, regression models, support vector machies etc., are used as substitutes for the numerical simulation model in order to reduce the computational burden on the simulation-optimization approach. However, the groundwater flow and transport system itself being characterized by uncertain parameters, using a deterministic surrogate model to substitute it is a gross and unrealistic approximation of the system. Till date, few studies have considered stochastic surrogate modeling to develop groundwater management methodologies. In this study, we utilize genetic programming (GP) based ensemble surrogate models to characterize coastal aquifer water quality responses to pumping, under parameter uncertainty. These surrogates are then coupled with multiple realization optimization for the stochastic and robust optimization of groundwater management in coastal aquifers. The key novelty in the proposed approach is the capability to capture the uncertainty in the physical system, to a certain extent, in the ensemble of surrogate models and using it to constrain the optimization search to derive robust optimal solutions. Uncertainties in hydraulic conductivity and the annual aquifer recharge are incorporated in this study. The results obtained indicate that the methodology is capable of developing reliable and robust strategies for groundwater management.     

A Cost-Effective Method to Control Seawater Intrusion in Coastal Aquifers

Intrusion of seawater into coastal aquifers is considered one of the most important processes that degrade water-quality by raising the salinity to levels exceeding acceptable drinking standards. Therefore saltwater intrusion should be prevented or at least controlled to protect groundwater resources. This paper presents a cost-effective method to control seawater intrusion in coastal aquifers. This methodology ADR (Abstraction, Desalination and Recharge) includes; abstraction of saline water and recharge to the aquifer after desalination. A coupled transient density-dependent finite element model is developed for simulation of fluid flow and solute transport and used to simulate seawater intrusion. The simulation model has been integrated with an optimization model to examine three scenarios to control seawater intrusion including; abstraction, recharge and a combination system, ADR. The main objectives of the models are to determine the optimal depths, locations and abstraction/recharge rates for the wells to minimize the total costs for construction and operation as well as salt concentrations in the aquifer. A comparison between the combined system (ADR) and the individual abstraction or recharge system is made in terms of total cost and total salt concentration in the aquifer and the amount of repulsion of seawater achieved. The results show that the proposed ADR system performs significantly better than using abstraction or recharge wells alone as it gives the least cost and least salt concentration in the aquifer. ADR is considered an effective tool to control seawater intrusion and can be applied in areas where there is a risk of seawater intrusion.     

北运河流域土沟段地下水水力截获效果评估

选择北运河流域土沟段于2016年开展为期69 d的水力截获试验。结果表明:20 m含水层降深为2 m,影响半径为80.2 m;60 m含水层降深为1.54 m,影响半径为150.6 m。试验期间,多数地下水水质指标浓度呈下降趋势,其中硫酸盐浓度下降幅度达57.9%,痕量有机污染物—邻苯二甲酸(2-乙基己基)酯的最大下降幅度达96.3%。污染物浓度的变化与其所在含水层介质中发生的稀释作用、阳离子交换吸附及黏土矿物的吸附作用等密不可分。试验结果表明水力截获试验可有效控制和修复地下水污染。     

Simulation and Multi-Objective Management of Coastal Aquifers in Semi-Arid Regions

Groundwater is the main water resource in many semi-arid coastal regions and water demand, especially in summer months, can be very high. Groundwater withdrawal for meeting this demand often causes seawater intrusion and degradation of water quality of coastal aquifers. In order to satisfy demand, a combined management plan is proposed and is under consideration for the island of Santorini. The plan involves: (1) desalinization (if needed) of pumped water to a potable level using reverse osmosis and (2) injection into the aquifer of biologically-treated waste water. The management plan is formulated in a multi-objective, optimization framework, where simultaneous minimization of economic and environmental costs is desired, subject to a constraint so that cleaned water satisfies demand. The decision variables concern the well locations and the corresponding pumping and recharging rates. The problem is solved using a computationally efficient, multi-objective, genetic algorithm (NSGAII). The constrained multi-objective, optimization problem is transformed to an unconstrained one using a penalty function proportional to constraint violation. This extends the definition of the objective function outside the domain of feasibility. The impact of prolonged droughts on coastal aquifers is investigated by assuming various scenarios of reduced groundwater recharge. Water flow and quality in the coastal aquifer is simulated using a three-dimensional, variable density, finite difference model (SEAWAT). The method is initially applied to a test aquifer and the trade-off curves (Pareto fronts) are determinedl for each drought scenario. The trade-off curves indicate an increase on the economic and environmental cost as groundwater recharge reduces due to climate change.     

A Multiobjective Optimisation Model for Groundwater Remediation Design at Petroleum Contaminated Sites

This study proposes a fuzzy multi-objective model for groundwater remediation in petroleum-contaminated aquifers. The optimisation system is designed based on the PAT technology, and includes two objectives (i.e. total pumping rate and average post-remedial contaminant concentration). The relationship between pumping rates and contamination concentrations at all monitoring wells after remediation are determined by a proxy model, which integrates simulation, inference, and optimisation technologies and is composed of intercept, linear, interactive, and quadratic options. Fuzzy algorithms are used to solve the formulated multi-objective optimisation problem to find optimal solutions. The model is then applied to a petroleum-contaminated aquifer in western Canada. The trade-off and λ analyses of the results indicate that the fuzzy multi-objective model has great potential in groundwater remediation applications as it can: (1) provide reliable groundwater remediation strategies, (2) reduce computational costs in the optimization processes, and (3) balance the trade-off between remediation costs and remediation outcomes.     

Coupled Sharp-interface and Density-dependent Model for Simultaneous Optimization of Production Well Locations and Pumping in Coastal Aquifer

The main management challenge in coastal aquifers is to prevent saltwater intrusion, ensuring ample freshwater supply. Saltwater intrusion happens due to unregulated pumping from production wells. Therefore, it is essential to have an effective management policy, which ensures the requisite amount of freshwater to be withdrawn from coastal aquifers without causing saltwater intrusion. A methodology for optimizing production well locations and maximizing pumping from production wells is presented to achieve these conflicting objectives. The location of production wells directly affects the amount of freshwater pumped out of the coastal aquifer. Simultaneous optimization of production well locations and pumping from the same is achieved by linking mathematical simulation models with the optimization algorithm. A new methodology using coupled sharp-interface and density-dependent simulation models is developed to find optimal well locations and optimize the amount of freshwater pumped from the coastal aquifer. The performance of the developed methodology is evaluated for saltwater intrusion in the coastal city of Puri, India. The performance evaluation results show the developed methodology's applicability for managing saltwater intrusion while maximizing freshwater pumping in coastal aquifers under constraints of well location.

     

Groundwater Response to Tidal Fluctuation in an Inhomogeneous Coastal Aquifer-Aquitard System

This paper investigates tide-induced groundwater fluctuation and submarine groundwater discharge (SGD) in a leaky inhomogeneous coastal aquifer system with an upper unconfined aquifer, a lower confined aquifer, and an aquitard between them. The upper left aquifer is formed due to land reclamation. The SGD defined as the groundwater flow from land into the sea is controlled mainly by the hydraulic gradient between land and sea. An analytical expression is developed to discuss and assess the effect of inhomogeneity on the groundwater head fluctuation in the leaky aquifer system. Joint effects of aquifers’ parameters such as leakage and hydraulic diffusivity on the groundwater head fluctuation and SGD are investigated. The predicted results from the analytical expression indicate that the groundwater head fluctuation in both unconfined and confined aquifers is dependent on dimensionless leakages and increases with dimensionless hydraulic diffusivity.     

Current Practice and Future Challenges in Coastal Aquifer Management: Flux-Based and Trigger-Level Approaches with Application to an Australian Case Study

The control of groundwater abstraction from coastal aquifers is typically aimed at minimizing the risk of seawater intrusion, excessive storage depletion and adverse impacts on groundwater-dependent ecosystems. Published approaches to the operational management of groundwater abstraction from regulated coastal aquifers comprise elements of “trigger-level management” and “flux-based management”. Trigger-level management relies on measured groundwater levels, groundwater salinities and/or ecosystem health indicators, which are compared to objective values (trigger levels), thereby invoking management responses (e.g. pumping cut-backs). Flux-based management apportions groundwater abstraction rates based on estimates of aquifer recharge and discharge (including environmental water requirements). This paper offers a critical evaluation of coastal aquifer management paradigms using published coastal aquifer case studies combined with a simple evaluation of the Uley South coastal aquifer, South Australia. There is evidence that trigger-level management offers advantages over flux-based approaches through the evaluation of real-time resource conditions and trends, allowing for management responses aimed at protecting against water quality deterioration and excessive storage depletion. However, flux-based approaches are critical for planning purposes, and are required to predict aquifer responses to climatic and pumping stresses. A simplified modelling analysis of the Uley South basin responses to different management strategies demonstrates the advantages of considering a hybrid management approach that includes both trigger-level and flux-based controls. It is recommended that where possible, trigger-level and flux-based approaches be adopted conjunctively to minimize the risk of coastal groundwater degradation and to underpin strategies for future aquifer management and well-field operation.     

A Framework for Ground Water Management Based on Bayesian Network and MCDM Techniques

Groundwater resources are steadily subjected to increasing water demands. The aquifers are considered as the most accessible source of fresh water. In recent years, they have been faced with severe water withdrawal in arid and semi-arid countries like Iran and thus some aquifers was considered as forbidden aquifers that it means the water withdrawal from these aquifers is unauthorized. Given a critical situation, groundwater resources management in the form of tools such as monitoring the level of the aquifers and developing the restoring scenarios is essential. Therefore, for this purpose, a framework has been developed based on prediction of groundwater level using Bayesian Networks (BNs) model. Furthermore, Multi Criteria Decision Making methods (MCDM) techniques proposed and employed for ranking of proposed groundwater management scenarios. This framework was evaluated for restoring the Birjand aquifer in Iran in different hydrological conditions. A probabilistic Dynamic BN was proposed for groundwater level prediction under uncertainties. After analyzing the obtained results, the applicable short term scenarios for groundwater management as well as appropriate economic, social and technical criteria were defined for decision making procedure. Then, using elicitation of decision makers’ opinions on the relative importance and performance of criteria, SAW, TOPSIS and PROMETHEE-II techniques were applied to rank the scenarios and the obtained results were aggregated by Borda method for final ranking of the scenarios. Lastly, the final results demonstrates the capability of the proposed framework for groundwater resources planning and management which can be employed for reducing the risk of aquifer level declining.     

Response Matrix Minimization Used in Groundwater Management with Mathematical Programming: A Case Study in a Transboundary Aquifer in Northern Greece

Groundwater has always been considered to be a readily available source of water for domestic, agricultural and industrial use. The last decades, the lack of policymaking for the utilization of groundwater, has led to overexploitation in many areas. The cooperation of a wide range of scientists such as mathematicians, engineers, computer scientists, environmentalists and economists – operation researchers, have led to the design and construction of commercial computer programs concerned on water management and specifically on the optimal distribution of limited water resources using groundwater management models. These combined models, via simulation and optimization algorithms, result in one optimal solution through operations research and mathematical programming methods. The groundwater management models are based on the method of space superposition or the combination of space and time superposition for steady and unsteady state problems, respectively. In the present study, an algorithm is presented, which minimizes the dimension of the response matrix, concerning on two assumptions: the first is the added fixed cost which represents the water supply pumping well and the second is the removal of time superposition. The study area is a transboundary phreatic aquifer in Northern Greece, in the area of Eidomeni, a small Hellenic village just on the borderline with FYROM. The aquifer has a total area of 10,84 km², 26 operating – pumping wells, which the 9 of them consist control points of the hydraulic head. The number of the management periods is 12 months.     

Groundwater Rights and Legislation for Sustainable Development in Arid Regions: The Case of Saudi Arabia

The main objective of this article is to shed light on an important issue of water conservation-groundwater rights and legislation. Principles of groundwater rights are detailed and their applications to major aquifer systems, groundwater basins and wadis are discussed. Legislation and the clarification of water rights are vital for the sustainable development of groundwater. They are urgently needed in Saudi Arabia in particular and in all areas where demand exceeds supply. Legislation is one of the main pillars upon which any conservation policy is built. In this article a model is suggested for groundwater allocation in aquifers shared by more than one state.     

A Suitable Tool for Sustainable Groundwater Management

Artificial recharge is used to increase the availability of groundwater storage and reduce saltwater intrusion in coastal aquifers, where pumping and droughts have severely impaired groundwater quality. The implementation of optimal recharge methods requires knowledge of physical, chemical, and biological phenomena involving water and wastewater filtration in the subsoil, together with engineering aspects related to plant design and maintenance operations. This study uses a novel Decision Support System (DSS), which includes soil aquifer treatment (SAT) evaluation, to design an artificial recharge plant. The DSS helps users make strategic decisions on selecting the most appropriate recharge methods and water treatment technologies at specific sites. This will enable the recovery of safe water using managed aquifer recharge (MAR) practices, and result in reduced recharge costs. The DSS was built using an artificial intelligence technique and knowledge-based technology, related to both quantitative and qualitative aspects of water supply for artificial recharge. The DSS software was implemented using rules based on the cumulative experience of wastewater treatment plant engineers and groundwater modeling. Appropriate model flow simulations were performed in porous and fractured coastal aquifers to evaluate the suitability of this technique for enhancing the integrated water resources management approach. Results obtained from the AQUASTRESS integrated project and DRINKADRIA IPA CBC suggest the most effective strategies for wastewater treatments prior to recharge at specific sites.     

苏丹中部半干旱地区含水层特征的评价

苏丹中部的北杰济拉(Gezira)省的含水层为杰济拉含水层和白垩系含水层.在确定含水层水文地质参数的基础上,绘制了地下水水位等值线图,并通过矿化度的空间分布对地下水的化学特性进行评价.对水文地质参数的计算采用了雅各布方法和泰斯恢复水位法,通过对地下水中主要化学成分的分析来评价地下水水质,并绘制有关的水化学特性图.测得杰济拉含水层的平均导水系数和渗透系数分别为368 7m2/d和34 03m/d,均小于白垩系含水层的407 2m2/d和43 17m/d.杰济拉含水层物理化学成分的浓度明显高于白垩系含水层.白垩系含水层水质较好,适宜用作生活用水及饮用水.     

Optimal Dynamic Monitoring Network Design and Identification of Unknown Groundwater Pollution Sources

The identification of unknown pollution sources is a prerequisite for designing of a remediation strategy. In most of the real world situations, it is difficult to identify the pollution sources without a scientifically designed efficient monitoring network. The locations of the contaminant concentration measurement sites would determine the efficiency of the unknown source identification process to a large extent. Therefore coupled and iterative sequential source identification and dynamic monitoring network design framework is developed. The coupled approach provides a framework for necessary sequential exchange of information between monitoring network and source identification methodology. The preliminary identification of unknown sources, based on limited concentration data from existing arbitrarily located wells provides the initial rough estimate of the source fluxes. These identified source fluxes are then utilized for designing an optimal monitoring network for the first stage. Both the monitoring network and source identification process is repeated by sequential identification of sources and design of monitoring network which provides the feedback information. In the optimal source identification model, the Jacobian matrix which is the determinant for the search direction in the nonlinear optimization model links the groundwater flow-transport simulator and the optimization method. For the optimal monitoring network design, the integer programming based optimal design model requires as input, simulated sets of concentration data. In the proposed methodology, the concentration measurement data from the designed and implemented monitoring network are used as feedback information for sequential identification of unknown pollution sources. The potential applicability of the developed methodology is demonstrated for an illustrative study area.     

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.