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.     

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.     

Modeling Groundwater Flow and Seawater Intrusion in the Coastal Aquifer of Wadi Ham,UAE

Groundwater pumping from Kalbha and Fujairah coastal aquifer of the United Arab Emirates (UAE) has increased significantly during the last two decades to meet the agriculture water demands. Due to the lack of natural replenishment from rainfall and the excessive pumping, groundwater levels have declined significantly causing an intrusion of seawater in the coastal aquifer of Wadi Ham. As a result, many pumping wells in the coastal zone have been terminated and a number of farms have been abandoned. In this paper, MODFLOW was used to simulate the groundwater flow and assess the seawater intrusion in the coastal aquifer of Wadi Ham. The model was calibrated against a five-year dataset of historical groundwater levels and validated against another eleven-year dataset. The effects of pumping on groundwater levels and seawater intrusion were investigated. Results showed that reducing the pumping from Khalbha well field will help to reduce the seawater intrusion into the southeastern part of the aquifer. Under the current groundwater pumping rates, the seawater will continue to migrate inland.     

Monitoring and Predicting Saltwater Intrusion via Temporal Aquifer Vulnerability Maps and Surrogate Models

This article proposes a methodology to accurately monitor seawater intrusion (SWI) using time-varied GALDIT vulnerability maps. The properly produced samples are then used as input–output patterns for the approximate SWI simulation. As a novelty, the specific area of high susceptibility to SWI is proposed as the dynamic saltwater wedge position to suitably select the monitoring locations (MLs) from a narrowed area. It is observed that varied initial conditions over time periods have more influence than variable pumping rates on salinity at MLs far from the production wells. Support Vector Regression (SVR), Artificial Neural Network (ANN) and Gaussian Process Regression (GPR) models have been substituted for the numerical model of SWI. Input training patterns of the surrogate models are initial salinity concentrations at selected MLs plus transient pumping values via Latin hypercube sampling. The final salinity at MLs constitutes the output patterns. The paper applies this new methodology to a small study area subject to the SWI problem. The generalization ability of surrogate models for predicting new initial conditions-pumping datasets was evaluated using performance criteria considering the ML locations. All surrogates offered good results for predicting SWI at specified MLs. The SVR model had poor performance compared to ANN and GPR models in MLs near the pumping wells, due to their salinity fluctuations over time.

     

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.     

Simulation Modeling for Efficient Groundwater Management in Balasore Coastal Basin,India

The Balasore coastal groundwater basin in Orissa, India is under a serious threat of overdraft and seawater intrusion. The overexploitation resulted in abandoning many shallow tubewells in the basin. The main intent of this study is the development of a 2-D groundwater flow and transport model of the basin using the Visual MODFLOW package for analyzing the aquifer response to various pumping strategies. The simulation model was calibrated and validated satisfactorily. Using the validated model, the groundwater response to five pumping scenarios under existing cropping conditions was simulated. The results of the sensitivity analysis indicated that the Balasore aquifer system is more susceptible to the river seepage, recharge from rainfall and interflow than the horizontal and vertical hydraulic conductivities and specific storage. Finally, based on the modeling results, salient management strategies are suggested for the long-term sustainability of vital groundwater resources of the Balasore groundwater basin. The most promising management strategy for the Balasore basin could be: a reduction in the pumpage from the second aquifer by 50% in the downstream region and an increase in the pumpage to 150% from the first and second aquifer at potential locations.     

Identifying Major Factors Controlling Groundwater Quality in Semiarid Area Using Advanced Statistical Techniques

There are many factors controlling groundwater pollution and vulnerability. However, the factors’ weights are still not reasonably investigated. In order to assess groundwater quality and the controlling factors in semiarid region, 178 groundwater samples were collected and analyzed for salinity and nitrate content. New statistical techniques of prediction profiler and hierarchical cluster combined with geographic information systems (GIS) were used to assess the groundwater quality based on three categorical controlling factors; landuse/ land cover (LULC), soil texture, and aquifer type. It is hypothesized these factors are controlling groundwater quality with various weights. Groundwater salinity across the study area varied from 327.0 to 9110.0 mg/L, while nitrate ranged from 0.2 mg/L to 339.6 mg/L. Both prediction profiler and cluster analyses provided excellent tools for quantifying the pollution magnitudes, weighing the controlling factors, and visualizing the pollution zones. Prediction profiler showed high capability to predict groundwater pollution (P?<?0.0001 and 0.0038 for salinity and nitrate, respectively) where LULC was the most effective factor, followed by aquifer type and soil texture class. According to desirability function analysis, maximum salinity and nitrate pollution was predicted to be associated with irrigated agriculture lands at shallow aquifers with silty clay loam soils. Hierarchical cluster analysis combined with GIS mapping was able to group the controlling factors into six vulnerability zones. The generated groundwater spatial pollution map allowed for potential pollution sources identification (e.g. fertilizer use, treated waste water, overdrafting). This paper also offers detailed mapping for decision makers to allow further ecosystem restoration and rehabilitation planning.     

Prediction and simulation of monthly groundwater levels by genetic programming

Groundwater level is an effective parameter in the determination of accuracy in groundwater modeling. Thus, application of simple tools to predict future groundwater levels and fill-in gaps in data sets are important issues in groundwater hydrology. Prediction and simulation are two approaches that use previous and previous-current data sets to complete time series. Artificial intelligence is a computing method that is capable to predict and simulate different system states without using complex relations. This paper investigates the capability of an adaptive neural fuzzy inference system (ANFIS) and genetic programming (GP) as two artificial intelligence tools to predict and simulate groundwater levels in three observation wells in the Karaj plain of Iran. Precipitation and evaporation from a surface water body and water levels in observation wells penetrating an aquifer system are used to fill-in gaps in data sets and estimate monthly groundwater level series. Results show that GP decreases the average value of root mean squared error (RMSE) as the error criterion for the observation wells in the training and testing data sets 8.35 and 11.33 percent, respectively, compared to the average of RMSE by ANFIS in prediction. Similarly, the average value of RMSE for different observation wells used in simulation improves the accuracy of prediction 9.89 and 8.40 percent in the training and testing data sets, respectively. These results indicate that the proposed prediction and simulation approach, based on GP, is an effective tool in determining groundwater levels.     

Brackish groundwater resources in Bahrain: Current exploitation,numerical evaluation and prospect for utilization

In Bahrain, where water resources available for direct use are finite and the best of its quality has a salinity of over 2.5 g L^–1, utilization of brackish groundwater is an essential part in the management of the country's water resources. Bahrain's brackish water occurs in the Rus-Umm Er Radhuma formations in the form of a lens of a finite lateral extent, with a salinity ranges between 8 and 15 g L^–1. Planning for utilization of brackish groundwater for desalination purposes in Bahrain was based on simulation modeling of the aquifer system using a mixing cell model developed originally in 1983. The model was used to predict the aquifer response to pumping from the proposed wellfield in terms of changes of TDS over a period of 20 years. Construction and operation of the wellfield in 1984 was based on the predicted salinity changes. Over the past 9 uears of wellfield operation (1984–1993), and through continuous monitoring of the aquifer response to pumping, the collected data is used to post-audit the original model by history matching. The calibration process adopted has resulted in a statisfactory agreement between the model output and the observed data. The model is then used to predict the wellfield salinity changes and the aquifer potentiometric levels. The expected life span for the brackish groundwater utilization by the wellfield is redefined through constrained utilization that takes into account salinity deterioration coupled with the effect of head decline on hydraulic interaction between the brackish water and the upper fresh water aquifer. The results suggest that the operation of the wellfield should cease by the year 2007. Construction of a new model that enables testing and evaluating different development scenarios is recommended to aid future management decisions regarding the utilization of brackish groundwater.     

Simulation-Optimization Modeling for Sustainable Groundwater Development: A Moroccan Coastal Aquifer Case Study

A transient simulation model characterizing groundwater flow in the coastal aquifer of Rhis-Nekor was constructed and calibrated. The flow model was then used in conjunction with a genetic algorithm based optimization model to explore the optimal pumping schemes that meet current and future water demands while minimizing the risks for several adverse environmental impacts, such as saltwater intrusion prevention, avoiding excessive drawdown, as well as controlling waterlogging and salinity problems. Modeling results demonstrate the importance of this combined simulation-optimization methodology for solving groundwater management problems associated with the Rhis-Nekor plain.     

Groundwater Vulnerability Modeling to Assess Seawater Intrusion: a Methodological Comparison with Geospatial Interpolation

Seawater intrusion has become a growing threat in coastal urban cities due to overexploitation of groundwater. This study examines the accuracy of the commonly used geospatial quality assessment models (GQA) and groundwater vulnerability assessment models (GVA) in determining the extent of seawater intrusion in urban coastal aquifers. For that purpose, interpolation methods (kriging, IDW and co-kriging) and vulnerability assessment models (DRASTIC, EPIK) were compared using groundwater salinity criteria (TDS, Cl^?) collected at three pilot areas along the eastern Mediterranean (Beirut, Tripoli, Jal el Dib). The results showed that while the GIS-based interpolation methods and the vulnerability assessment models captured elements of the groundwater quality deterioration, both had a limited ability to accurately delineate saltwater intrusion. This emphasizes that while interpolation methods and conventional vulnerability models may give general information about groundwater quality, they fail to capture the status of the aquifer at a finer spatial resolution.

     

3D Variable Density Flow Simulation to Evaluate Pumping Schemes in Coastal Aquifers

A three dimensional model is presented for the simulation of seawater intrusion in coastal aquifers by considering the development of a transition zone and thus the variable density flow approach. The model is applied to a heterogeneous coastal aquifer to study the effects of the pumping rate, the salinity of freshwater inflow and the thickness of the aquifer, on the degradation of pumped water quality through wells in certain location. Even for an optimum pumping scheme solution based on a simple two-dimensional flow model, we simulate freshwater degradation in pumped water which depends on the salinity of freshwater inflow and aquifer thickness.     

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.     

滨海盆地变密度地下水流与溶质运移三维耦合数值模型研究

针对滨海含水层的复杂性,以北部湾经济区合浦盆地地下水资源应急潜力评价为例,通过概化出合浦盆地水文地质概念模型的基础上,采用SEAWAT模块建立了合浦盆地变密度地下水流与溶质运移三维耦合数值模型。在对模型进行识别、验证的基础上,假设2025年10月出现极端干旱,在保证不发生海水入侵的条件下,获得了度过整个枯水期各水源地地下水资源应急潜力。结果表明:合浦盆地地下水资源应急潜力为83.13万m~3/d,集中开采区中心地下水水头下降3～8 m, 2 a后水位基本恢复;集中开采区降落漏斗远离海岸线,不受到海水入侵的影响。该方法将滨海水源地地下水应急供水预测和盆地的水文地质结构及当地发展规划紧密结合起来,为合浦盆地地下水资源的合理开发利用和应急能力建设提供了可靠依据。     

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.     

Modeling of Ground Water Salinity on the Caspian Southern Coasts

Water salinity is one of the main restrictive factors in water exploiting. Also, unsuitable management and exploitation of water resources has led to an increase of surface and groundwater salinity. Thus, in view of human needs to water resources, it is necessary to study and define water salinity factors in order to weaken these factors. This research has been conducted to investigate the factors of groundwater salinity and also, to provide a model for estimating groundwater salinity on the Caspian southern coasts. Data included in the model are: water qualitative examinations in the area, annual precipitation and evaporation, water table depth, surface water salinity, aquifer formation (Transmissivity) and distance from Caspian Sea. Surface and groundwater salinity was estimated by sampling in different sites on the Caspian southern coasts. Then, Multivariate Regression method was used by using SPSS software. In this stage, groundwater EC has been used as a variable for water salinity or dependent variable and groundwater salinity factors have been used as independent variables. A linear model and a non-linear model were presented. The models efficiency was evaluated by applying them in the sites that their data were not used for presenting the models. Finally, groundwater EC average map was provided by using the presented non-linear model and Geographic Information System in the Eastern part of Mazandaran province. In view of salinity hazard increases in the coastal terrains and agricultural areas, the places with high hazard salinity must be defined and managed to decrease water resources salinity.     

Dynamic Optimal Monitoring Network Design for Transient Transport of Pollutants in Groundwater Aquifers

Optimal groundwater pollution monitoring network design models are developed to prescribe optimal and efficient sampling locations for detecting pollution in groundwater aquifers. The developed methodology incorporates a two dimensional flow and transport simulation model to simulate the pollutant concentrations in the study area. Different realizations of the pollutant plume are randomly generated by incorporating the uncertainty in both source and aquifer parameters. These concentration realizations are incorporated in the optimal monitoring network design models. Two different objectives are considered separately. The first objective function minimizes the summation of unmonitored concentrations at different potential monitoring locations. This objective function in effect minimizes the probability of not monitoring the pollutant concentrations at those locations where the probable concentration value is large. Although this probability is not explicitly incorporated in the model, a surrogate form of this objective is included as the objective function. The second objective function considered is the minimization of estimation variances of pollutant concentrations at various unmonitored locations. This objective results in a design that chooses optimal monitoring locations where the uncertainties in simulated concentrations are large. The developed optimization models are solved using Genetic Algorithm. The variances of estimated concentrations at potential monitoring locations are computed using the geostatistical tool, kriging. The designed monitoring network is dynamic in nature, as it provides time varying network designs for different management periods, to account for the transient pollutant plumes. Such a design can eliminate temporal redundancy and is therefore, economically more efficient. The optimal design incorporates budgetary constraints in the form of limits on the number of monitoring wells installed in any particular management period. The solution results are evaluated for an illustrative study area comprising of a hypothetical aquifer. The performance evaluation results establish the potential applicability of the proposed methodology for optimal design of the dynamic monitoring network for detection and monitoring of pollutant plumes in contaminated aquifers.     

Quantifying a Sustainable Management Space for Human Use of Coastal Groundwater under Multiple Change Pressures

In the densely populated coastal regions of the world, loss of groundwater due to seawater intrusion, driven by changes of climate, sea level, land use and water use, may critically impact many people. We analytically investigate and quantify the limits constraining a coastal aquifer’s sustainable management space, in order to avoid critical loss of the coastal groundwater resource by seawater intrusion. Limiting conditions occur when the intrusion toe reaches the pumping wells, well intrusion, or the marine-side groundwater divide, complete intrusion; in both cases the limits are functions of the seaward groundwater flow remaining after the human groundwater extractions. The study presents a screening-level approach to the quantification of the key natural and human-determined controls and sustainability limits for the human use of coastal groundwater. The physical and geometrical characteristics of the coastal aquifer along with the natural conditions for recharge and replenishment of the coastal groundwater are the key natural controls of the sustainable management space for the latter. The groundwater pumping rates and locations are the key human-determined controls of this space. The present approach to combining and accounting for both of these types of controls is simple, yet general. The approach is applicable across different scales and regions, and for historic, current and projected future conditions of changing hydro-climate, sea level, and human freshwater use. The use of this approach is also concretely demonstrated for the natural and human-determined controls and limits of the sustainable management space for two specific Mediterranean aquifers.     

Season Algorithm-Multigene Genetic Programming: A New Approach for Rainfall-Runoff Modelling

Genetic programming (GP) is recognized as a robust machine learning method for rainfall-runoff modelling. However, it may produce lagged forecasts if autocorrelation feature of runoff series is not taken carefully into account. To enhance timing accuracy of GP-based rainfall-runoff models, the paper proposes a new rainfall-runoff model that integrates season algorithm (SA) with multigene-GP (MGGP). The proposed SA-MGGP model was trained and validated for single- and two- and three-day ahead streamflow forecasts at Haldizen Catchment, Trabzon, Turkey. Timing and prediction accuracy of the proposed model were assessed in terms of different efficiency criteria. In addition, the efficiency results were compared to those of monolithic GP, MGGP, and SA-GP forecasting models developed in the present study as the benchmarks. The outcomes indicated that SA augments timing accuracy of GP-based models in the range 250% to 500%. It is also found that MGGP may identify underlying structure of the rainfall-runoff process slightly better than monolithic GP at the study catchment.     

A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions

Prediction of long-term rainfall patterns is a highly challenging task in the hydrological field due to random nature of rainfall events. The contribution of monthly rainfall is important in agriculture and hydrological tasks. This paper proposes two data-driven models, namely biogeography-based extreme learning machine (BBO-ELM) and deep neural network (DNN), to predict one, two, and three month-ahead rainfall over India (All-India and six other homogeneous regions). Three other data-driven models called ELM, genetic algorithm (GA)-based ELM, and particle swarm optimization (PSO)-based ELM are used to compare the performance of the proposed models. Firstly, partial autocorrelation function (PACF) is applied in all datasets to select the optimal number of lags for input to the models. Secondly, the wavelet-based data pre-processing technique is applied in selected optimal lags and feed to the proposed models for achieving higher prediction performance. To investigate the performance of proposed models, a non-parametric statistical test, Anderson–Darling’ Normality test, is performed in all India dataset. The wavelet-based proposed hybrid models show better prediction capability compared to optimal lag-based proposed models. This study shows the successful application of time-series data using proposed techniques (optimal lags-based BBO-ELM and wavelet-based DNN) in the hydrological field which may be used for risk mitigation from dreadful natural events.