Groundwater Quality Monitoring Network Research Articles

Spatial design of groundwater quality monitoring network using multicriteria analysis based on pollution risk map

Spatial design of groundwater quality monitoring network using multicriteria analysis based on pollution risk map

Spatial Optimization of the Groundwater Quality Monitoring Network in the Kingdom of Bahrain

Groundwater resource development, management, and planning essentially rely on their quantitative and qualitative monitoring. The information obtained from the monitoring networks of a given groundwater resource is used as a significant indicator for the status of that resource, and management schemes are subsequently made in order to develop and utilize this resource on a sustainable basis. In this study, the performance of the existing monitoring network of the quality of groundwater in Bahrain was evaluated and was spatially optimized using the geostatistical method of kriging. The estimation variance was used as a criterium in the design process, and variance reduction was used to measure the network performance. The process resulted in an increase in the number of observation points from 15 currently monitored wells to 91 wells, with 74% of these being augmented industrial and commercial wells that were to be self-reported to internalize the cost of groundwater management in their users. It is recommended that temporal optimization procedures for the groundwater level are conducted and that monitored groundwater quality data are stored in a dedicated groundwater management information system (MIS) along with the monitored data of groundwater levels and abstraction to effectively support the process of decision making for groundwater planning and management.

Optimal redesign of coastal groundwater quality monitoring networks under uncertainty: application of the theory of belief functions.

This paper presents a new methodology for the optimal redesign of water quality monitoring networks in coastal aquifers. The GALDIT index is used to evaluate the extent and magnitude of seawater intrusion (SWI) in coastal aquifers. The weights of the GALDIT parameters are optimized using the genetic algorithm (GA). A SEAWAT-based simulation model, a spatiotemporal Kriging interpolation technique, and an artificial neural network surrogate model are then implemented to simulate total dissolved solids (TDS) concentration in coastal aquifers. To obtain more precise estimations, an ensemble meta-model is developed using the Dempster-Shafer's belief function theory (D-ST) to combine the results obtained from the three individual simulation models. The combined meta-model is then used for calculating more precise TDS concentration. Some plausible scenarios are defined for variation of water elevation and water salinity at the coastline to incorporate uncertainty through the concept of value of information (VOI). Finally, the potential wells with the highest values of information are taken into consideration to redesign coastal groundwater quality monitoring network under uncertainty. The performance of the proposed methodology is evaluated by applying it to the Qom-Kahak aquifer, north-central Iran, which is threatened by SWI. At first, the individual and ensemble simulation models are developed and validated. Then, several scenarios are defined regarding the plausible changes in TDS concentration and water level at the coastline. In the next step, the scenarios, the GALDIT-GA vulnerability map, and the VOI concept are used for redesigning the existing monitoring network. The results illustrate that the revised groundwater quality monitoring network containing 10 new sampling locations outperforms the existing one based on the VOI criterion.

Optimal Design of Groundwater Quality Monitoring Network Using Aquifer Vulnerability Map

Optimal Design of Groundwater Quality Monitoring Network Using Aquifer Vulnerability Map

Multivariate Time Series Clustering of Groundwater Quality Data to Develop Data-Driven Monitoring Strategies in a Historically Contaminated Urban Area

As groundwater quality monitoring networks have been expanded over the last decades, significant time series are now available. Therefore, a scientific effort is needed to explore innovative techniques for groundwater quality time series exploitation. In this work, time series exploratory analysis and time series cluster analysis are applied to groundwater contamination data with the aim of developing data-driven monitoring strategies. The study area is an urban area characterized by several superimposing historical contamination sources and a complex hydrogeological setting. A multivariate time series cluster analysis was performed on PCE and TCE concentrations data over a 10 years time span. The time series clustering was performed based on the Dynamic Time Warping method. The results of the clustering identified 3 clusters associated with diffuse background contamination and 7 clusters associated with local hotspots, characterized by specific time profiles. Similarly, a univariate time series cluster analysis was applied to Cr(VI) data, identifying 3 background clusters and 7 hotspots, including 4 singletons. The clustering outputs provided the basis for the implementation of data-driven monitoring strategies and early warning systems. For the clusters associated with diffuse background contaminations and those with constant trends, trigger levels were calculated with the 95° percentile, constituting future threshold values for early warnings. For the clusters with pluriannual trends, either oscillatory or monotonous, specific monitoring strategies were proposed based on trends’ directions. Results show that the spatio-temporal overview of the data variability obtained from the time series cluster analysis helped to extract relevant information from the data while neglecting measurements noise and uncertainty, supporting the implementation of a more efficient groundwater quality monitoring.

Combining statistical methods for detecting potential outliers in groundwater quality time series

Quality control of large-scale monitoring networks requires the use of automatic procedures to detect potential outliers in an unambiguous and reproducible manner. This paper describes a methodology that combines existing statistical methods to accommodate for the specific characteristics of measurement data obtained from groundwater quality monitoring networks: the measurement series show a large variety of dynamics and often comprise few (< 25) measurements, the measurement data are not normally distributed, measurement series may contain several outliers, there may be trends in the series, and/or some measurements may be below detection limits. Furthermore, the detection limits may vary in time. The methodology for outlier detection described in this paper uses robust regression on order statistics (ROS) to deal with measured values below the detection limit. In addition, a biweight location estimator is applied to filter out any temporal trends from the series. The subsequent outlier detection is done in z-score space. Tuning parameters are used to attune the robustness and accuracy to the given dataset and the user requirements. The method has been applied to data from the Dutch national groundwater quality monitoring network, which consists of approximately 350 monitoring wells. It proved to work well in general, detecting outliers at the top and bottom of the regular measurement range and around the detection limit. Given the diversity exhibited by measurement series, it is to be expected that the method does not give 100% satisfactory results. Measured values identified by the method as potential outliers will therefore always need to be further assessed on the basis of expert knowledge, consistency with other measurement data and/or additional research.

Development of data-driven models for estimating the probability of high-concentration occurrence of naturally occurring radioactive materials in groundwater

High-concentration occurrence probability estimation models was developed to estimate 238U and 222Rn in groundwater using in-situ monitoring data (i.e., geological rock types, well depth, T, pH, Eh, EC, DO, and HCO3). The estimation models are based on a non-linear data-driven method to improve their effectiveness for applications in different estimation cases using various in-situ monitoring data. When developing the models, most sensitive in-situ monitoring data are selectively utilized to train the models, where various statistical and correlation analyses are applied to overcome challenges, including model overfitting during training, a highly nonlinear correlation between input and target variables, and poor training due to low-quality monitoring data. Based on statistical analysis results, all input variables, except for Eh and well depth, are used for developing the 238U and 222Rn estimation models, respectively. Actual data collected from groundwater quality monitoring networks of South Korea from 2007 to 2019 are used to validate the developed models. Although it is difficult to characterize 222Rn occurrence using the geochemical conditions of groundwater because the gaseous phase behavior of 222Rn highly depends on structural geology, the 222Rn estimation model achieves reasonable performance with more than 70% accuracy. In addition, compared to the 222Rn estimation model, the 238U estimation model achieves higher classification accuracy with approximately 80%. Consequently, we can confirm that the developed estimation models can be effectively used to estimate the probability of high-concentration risk of 238U and 222Rn in groundwater. Conclusively, the practical applicability of the developed models is wide, as the models have been developed using direct observable and real-time data.

Characterization of Groundwater Chemistry and Fluoride in Groundwater Quality Monitoring Network of Korea

This study presents the data analysis results of groundwater chemistry and the occurrence of fluoride in groundwater obtained from the groundwater quality monitoring network of Korea. The groundwater data were collected from the National Groundwater Information Center and censored for erratic values and charge balance (±10%). From the geochemical graphs and various ionic ratios, it was observed that the Ca-HCO3 type was predominant in Korean groundwater. In addition, water-rock interaction was identified as a key chemical process controlling groundwater chemistry, while precipitation and evaporation were found to be less important. According to a non-parametric trend test, at p=0.05, the concentration of fluoride in groundwater did not increase significantly and only 4.3% of the total groundwater exceeded the Korean drinking water standard of 1.5 mg/L. However, student t-tests revealed that the fluoride concentrations were closely associated with the lithologies of tuff, granite porphyry, and metamorphic rocks showing distinctively high levels. This study enhances our understanding of groundwater chemical composition and major controlling factors of fluoride occurrence and distribution in Korean groundwater.

Using intrinsic vulnerability and anthropogenic impacts to evaluate and compare groundwater risk potential at northwestern and western coastal aquifers of Sri Lanka through coupling DRASTIC and GIS approach

Groundwater vulnerability assessment has become a crucial step in successfully protecting groundwater against pollution. An attempt of this study has been made to evaluate groundwater contamination risk using intrinsic vulnerability and land-uses in Vanathavillu, Kalpitiya and Katana area in Sri Lanka, using coupled DRASTIC with GIS as feasible methodology. The findings reveal that the groundwater in the areas under study falls under very low to high contamination risk. The higher risk of contamination has been identified in most of the Kalpitiya (about 82%) with the moderate along the beach in the west and next to Puttalam lagoon in the northeast and southeast. This is mainly due to pollution risk inherent with intense vegetable cultivation, over pumping, shallow groundwater tables and permeable sandy soil. Vanathavillu is under very low to moderate contamination risk, in which the moderate risk (about 13%) has especially been found the center, central southwest and west of the area. The relative less deep groundwater tables, possible seepage from the underlying limestone aquifer and less permeable red earth soil could be cause for the moderate risk in the area. Furthermore, results show that the Katana has low to moderately high groundwater contamination risk. Nitrate has a good agreement with the different pollution risk classes and that nitrate can be used as an indicator of aquifer degradation inherent with land-use activities in the coastal areas. Groundwater quality monitoring network should be set up to minimize the anthropogenic acts, particularly in high and moderate contamination risk zones.

喀斯特含水层的地下水易污性,主要挑战和因素: 在Spain南部Ubrique泉域的应用

Groundwater vulnerability mapping is one of the tools most often applied to analyse the sensitivity of karst aquifers to pollution. These maps aim to support stakeholders in decision-making and to promote land-use management compatible with water protection; however, the validation of these maps is still a challenge in many cases, triggering high uncertainty. For karst media, due to the strong heterogeneity in recharge mechanisms and hydraulic characteristics, validation is a significant stage and it must be inherent within the groundwater vulnerability assessment process. This work aims to assess the implementation of tools used for protecting the quality of water discharging or extracted from the Ubrique karst system in southern Spain, which supplies drinking water that is threatened by periodical pollution/turbidity episodes. A groundwater vulnerability map, attained by application of the COP method and validated by multiple in-situ observations, shows an extremely vulnerable system due to the absence of protective overlayers and the significant development of exokarst landforms, including shallow holes. This map could constitute the basis for defining protection zones for the Ubrique springs; however, their comprehensive protection requires the implementation of monitoring tools and an effective management strategy, through an early warning system that assures stable environmental and hydrogeological conditions and improves operational procedures associated with the drinking water service. This research establishes the strong relationship of the different methods applied to protect the source from contamination events, ranging from classical hydrodynamic and hydrochemical approaches to the implementation of protection zones and early warning groundwater quality monitoring networks.

A GIS-expert-based approach for groundwater quality monitoring network design in an alluvial aquifer: a case study and a practical guide.

Groundwater quality monitoring is a critical part of water management in all groundwater basins. In order to be effective and to meet the required needs, groundwater quality monitoring networks (GQMNs) must be designed to be able to operate long-term and economically without minimal disruption. The analytical hierarchical process (AHP), a multi-criteria decision-making program, was used to design a GQMN for an alluvial aquifer located in the Islam Abad plain west of Kermanshah province, Iran. This semi-arid area is subject to groundwater depletion and water quality changes. The model used 8 primary criteria sub-divided with 5 sub-criteria based on a combination of empirical data and expert opinion. The primary criteria included density of wells, well discharge, well depth, water quality (conductivity), flow direction, annual groundwater extraction, water level declines, and accessibility. The model showed that 59 of 254 production wells in the basin could provide optimal monitoring locations. When a second screening of the wells was used to determine constraints (physical conditions of the wells and pumps, owner permission of use, type of the pump, etc.), the number of wells was reduced to 13 wells. An initial round of water sampling and chemical analysis demonstrated that the design of the GQMN met the goals of the water management agency of the region.

Water Quality Characteristics of the Gyeonggi-do Groundwater Quality Monitoring Network

Water Quality Characteristics of the Gyeonggi-do Groundwater Quality Monitoring Network

Design of the optimal groundwater quality monitoring network using a genetic algorithm based optimization approach

سابقه و هدف: توزیع مکانی و دقت داده­های کیفی در مدیریت منابع آب زیرزمینی ضروری است. معمولا این داده­ها از چاه­های پایش جمع­آوری می­شود که به صورت مکانی در حوضه آبریز یا آبخوان مورد مطالعه توزیع شده است. در طراحی شبکه پایش، حداقل تعداد چاه­های پایش با توزیع مکانی بهینه و مناسب از نظر اقتصادی مورد نیاز است. بنابراین نحوه توزیع چاه­ها و تعداد آن­ها در طراحی شبکه پایش کیفی یکی از موارد مهم در بهینه­سازی مسائل مربوط به آب زیرزمینی است. هدف از این مطالعه، یافتن شبکه پایش بهینه با حداقل تعداد چاه­ها در آبخوان دشت گیلان است، به­طوری­که، توزیع مکانی مناسبی از نظر پارامترهای کیفیت آب زیرزمینی داشته باشد. با توجه به اینکه یکی از مهمترین معیارهای کیفیت آب زیرزمینی شوری است که با پارامترهایی مانند کل جامدات محلول (TDS)، یون کلر (Cl) و هدایت الکتریکی (EC) اندازه­گیری می­شود، در این مطالعه پارامتر EC به عنوان پارامتر کیفی در طراحی شبکه پایش انتخاب گردید. مواد و روش­ها: برای جستجوی بهینه شبکه پایش کیفی از الگوریتم بهینه­سازی ژنتیک (GA) استفاده شد. در این روش، یک شبکه ممکن از چاه­های پایشی که در منطقه واقع شده است به عنوان یک کروموزوم در نظر گرفته می­شود و هر چاه پایش، ژن­های کروموزوم هستند که با ساختار دودویی (صفر و یک) کدگذاری می­شوند. در صورتی­که انتخاب شود یک و در غیر این صورت صفر خواهد بود. در این مطالعه همزمان دو تابع هدف متضاد حل می­شود. هدف اول حداکثر کردن تطابق (برازش) بین توزیع­های EC محاسبه شده در شبکه پایش موجود و شبکه جدید است. که این تطابق با استفاده از کارایی مدل نش ساتکلیف ارزیابی می­شود. هدف دوم نیز با در نظر گرفتن محدودیت مربوط به هزینه، حداقل کردن تعداد چاه­های پایش در شبکه جدید ایجاد شده می­باشد. این دو هدف در یک تابع هدف تعریف می­شود که در آن ترکیب­های مختلفی از دو هدف با اعمال ضریب وزنی w مورد بررسی قرار می­گیرد. نتایج و بحث: نتایج ارزیابی­ها نشان داد که انتخاب جواب بهینه تا حد زیادی به تعیین ضریب وزنی w بستگی دارد. بنابراین باید مقدار w را با توجه به متعادل­ترین جواب انتخاب شود. متعادل­ترین جواب به این معناست که بین هزینه و پراکنش مکانی چاه­ها در منطقه یک رابطه قابل قبولی برقرار باشد. در ادامه، برای انتخاب بهترین جواب بهتر است علاوه بر استفاده از ضریب کارایی نش ساتکلیف از شاخص­های عملکرد دیگری مانند PBIAS، RMSE، ضریب رگرسیون و انحراف معیار نیز استفاده شود. به طور کلی، نتایج بدست آمده در این مطالعه با توجه به این شاخص­ها قابل قبول بوده است. همچنین، بررسی توزیع مکانی و مقایسه میانگین مقادیر EC مشاهده شده در منطقه و EC محاسبه شده در شبکه بهینه با توجه به همه چاه­های پایش انتخاب شده نشان می­دهد که مقادیر بهینه­شده بزرگتر از میانگین مقادیر مشاهده شده در منطقه است. بنابراین به وضوح نتیجه­گیری می­شود که شبکه بهینه­شده داده­های کیفی آب زیرزمینی مناطق آلوده­تر را فراهم می­کند. نتیجه­ گیری: نتایج نشان داد که کاربرد روش بهینه­سازی به طور قابل ملاحظه­ای تعداد چاه­های پایش را با توجه به توزیع مکانی مقادیر EC کاهش می­دهد. علاوه بر این، شبکه پایش طوری طراحی شد که تعداد نقاط نمونه­برداری در مناطق با آلودگی کمتر، حذف شد و در مناطقی با آلودگی بیشتر، اضافه شد. در طراحی شبکه پایش کیفی آب زیرزمینی بهتر است به صورت دوره­ای بهینه­سازی انجام شود، زیرا انتظار می­رود کارایی شبکه پایش با اضافه شدن چاه­های جدید به شبکه تغییر کند. ارزیابی­های پی در پی شبکه پایش به صورت هر چند سال یک بار، در تعیین ارزیابی درازمدت کیفیت آب زیرزمینی و عوامل موثر در آن کمک می­کند که می­تواند در برنامه­ریزی و اعمال روش­هایی برای بهبود کیفیت آب زیرزمینی موثر باشد.

Countrywide Groundwater Quality Trend and Suitability for Use in Key Sectors of Korea

Under changing climate, increasing groundwater use has risen the concern for groundwater quality variations over recent years, to maintain a healthy ecosystem. The objectives were to identify trend of temporal variations in groundwater quality and its suitability for different uses in Republic of Korea. Water quality data were collected from 198 monitoring stations of Groundwater Quality Monitoring Network (GQMN), annually for the period of ten years (2008–2017). Non-parametric trend analysis of a Mann–Kendall test and Theil–Sen’s slope was done on groundwater physico-chemical data of ten years. Groundwater suitability evaluation was done for use in main sectors including domestic (drinking) and agriculture (irrigation). For drinking suitability analysis, results were compared with World Health Organization (WHO) and Korean Ministry of Environment (KME) established guidelines. For irrigation suitability evaluation, electrical conductivity (EC), Sodium Adsorption Ratio (SAR), percent of Na+, Residual Sodium Carbonate (RSC), US Salinity Laboratory (USSL), and Wilcox diagram were used. Most significantly, water type belongs to Ca-HCO3 and Ca-SO4 types, but a small proportion belongs to Na-CO3 and Na-Cl types. Approximately, 96% and 93% of groundwater samples are suitable for drinking, based on WHO and KME guidelines, respectively. Around 98% and 83% of groundwater samples are in suitable range for irrigation use, based on USSL and Wilcox diagrams, respectively.

Surrogate assisted multi-objective robust optimization for groundwater monitoring network design

The robust optimization of groundwater quality monitoring network is subject to many conflicting objectives and high level of uncertainty in hydraulic conductivity. This study develops a two-stage stochastic optimization framework including the uncertainty quantification using a cheap-to-evaluate surrogate model and an improved epsilon multi-objective noisy memetic algorithm (ε-MONMA) for monitoring network design. The surrogate model based on sparse polynomial chaos expansion (PCE) is constructed to replace expensive simulation model in the uncertainty quantification of concentrations at the pre-defined monitoring locations for reducing huge computational cost. Additionally, the scenario discovery strategy using sparse PCE model is applied to filter a typical scenario set and the centroid of contaminant plume is used as the diversity metric, which avoids enumerating all possible contamination plumes caused by the uncertain K-field in the optimization. The proposed algorithm is then employed to solve stochastic management model to achieve robust monitoring design, indicating the insensitivity of monitoring design to plume uncertainty no matter which of the many possible scenarios becomes the true distribution of contamination under the true K-field. A synthetic aquifer considering uncertainty in hydraulic conductivity is designed to optimize monitoring network design. The Pareto-optimal solutions to the synthetic example are achieved under three of plume scenario sets defined at deterministic scenario (Scenario A0), Monte Carlo based scenario discovery (Scenario A1) and surrogate assisted scenario discovery (Scenario A2), respectively. Comprehensive analysis demonstrates that the monitoring design based on Scenario A2 outperforms either of the two designs based on Scenarios A0 and A1 in terms of the improvement of robustness of designs evaluated against the typical scenario set. Meanwhile, the performance of monitoring network deteriorates as the uncertainty of plume (noisy strength) increases, indicating the significance of reducing parameter uncertainty in groundwater monitoring design. The research findings show that the developed stochastic optimization framework is a computationally efficient and promising tool for multi-objective design of groundwater monitoring network under uncertainty.

Quantitative decision making for a groundwater monitoring and subsurface contamination early warning network

With the increased development of oil and gas activities in northern Colorado, public concerns over the environmental impacts associated with well drilling and hydraulic fracturing have continued to rise. Issues such as leakages of “toxic” products from oil and gas operations to the subsurface environment (such as groundwater contamination) have led to community action and state regulations related to the establishment of groundwater quality monitoring sites in oil and gas activity areas, particularly those adjacent to urban development. Colorado Water Watch was a groundwater quality monitoring network comprised of seven monitoring wells in northern Colorado to monitor groundwater quality near oil and gas wells and give early warnings of contamination. Our study is aimed at developing a quantitative methodology to find ideal monitoring locations as well as evaluate them. We utilized hydraulic and geological data to select the most preferred sites to monitor groundwater quality, understand the temporal trends and identify unique anomaly signals in the oil and gas active area (Wattenberg field, northern Colorado). In addition to the site selection methodology, water quality data from Colorado Water Watch over 2 years is used to do evaluate the performance using entropy information and Principal Component Analysis. The analysis indicates that the earliest functional monitoring site (CHILL) is the most informative monitoring well, and the most recently installed monitoring sites (Gilcrest and LaSalle) are the least informative and least important stations due to their low data efficiency.

Groundwater quality monitoring network design and optimisation based on measured contaminant concentration and taking solute transit time into account

Groundwater quality monitoring networks are designed to give the best possible overview of the quality status and trend of aquifers from a limited number of measurement stations. Ideally, the stations of the network should (i) allow the detection of significant changes in water quality, either improvements due to successful mitigation measures, or deteriorations caused for instance by land use change or other increasing pressure and (ii) approximate closely the true unknown mean concentration of the considered aquifer. If these two features are met, the monitoring network is representative of the entire aquifer. In this article, we present a method with which to assess and improve the representativeness of a regional groundwater monitoring network. The method is designed specifically to optimize the selection of monitoring stations from a larger choice of fixed locations such as springs or existing observation wells using one contaminant for the optimisation procedure. Firstly, the empirical distribution of selected solutes (contaminants and age tracers) is calculated from a large dataset consisting of as many sampling points as possible and compared to the distributions obtained solely from the stations of the existing or planned monitoring network. If differences are large, the network is modified by adding new stations or replacing some of them, using one contaminant for the optimisation. Lastly, the distribution of the optimised network is compared to the total distribution for other contaminants and one groundwater age tracer.The use of the method is illustrated for the main aquifer of the country of Luxembourg, where it is shown that the representativeness of the current monitoring network can be improved by replacing two stations. In the case study, the optimisation was based on desethyl atrazine, a ubiquitous pesticide transformation compound which was assumed to be an indicator of diffuse agricultural pollution. Although the largest improvement was obviously obtained for the optimised compound, improvement was also observed for other contaminants, including nitrate, probably showing that at the regional scale, the pattern of diffuse pollution of widely used agrochemicals is similar between compounds. The temporal evolution of the major contaminants could also be reproduced by the optimised network, even though the range of tritium values used as age tracer was narrower for the modified network than for the original network, thus indicating some bias towards faster reaction times.

Groundwater vulnerability assessment using DRASTIC model: a comparative analysis of conventional, AHP, Fuzzy logic and Frequency ratio method

Groundwater quality issues are drawing attention of researchers, developers, practitioners and planners throughout the world wherein besides geogenic sources, the activities such as urbanization, agriculture and industry are the precursors in polluting this precious natural resource. India is one such country where groundwater is the prime drinking water source especially in rural areas. Thus, along with the water scarcity, groundwater quality issues are posing challenge in front of the nation. Therefore, in current scenario, groundwater vulnerability prediction has an utmost importance. Hence it is the prime responsibility of the researchers to demarcate the vulnerable areas using simple and effective tools for establishing groundwater quality monitoring networks and implementing preventive measures. In the present study, a standard DRASTIC methodology has been employed to identify the critical areas for groundwater contamination. Further, the weights assigned to seven DRASTIC parameters were modified by using Analytical Hierarchical Process, Fuzzy logic and Frequency ratio method to understand the best fit method for vulnerability prediction. The reclassified rated thematic layers were integrated using weighted overlay function in GIS. The obtained results were grouped into four vulnerability zones: very low, low, moderate and high. The Standard, AHP, Fuzzy logic and Frequency Ratio based DRASTIC models revealed the area under high groundwater susceptibility as 84.68, 73.01, 89.13 and 82.97 km2 respectively. The resultant vulnerability maps were verified by superimposing groundwater quality (nitrate concentration point values) data.

Development of an Entropy Method for Groundwater Quality Monitoring Network Design

A new approach is presented based on the entropy theory to design an optimal groundwater quality monitoring network. First, the unrecorded values of Total Dissolved Solids (TDS) are estimated in the study area using Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) methods, and then, using Differential Evolution (DE) optimization algorithm aimed at maximizing the pure information gained by stations. The number and location of monitoring stations are selected optimally from the active wells located in the study area. The spatial distribution of chosen wells provides adequate coverage of the whole aquifer and, at the same time, gives the maximum useful information about the water quality status. It also avoids selecting any redundant station considering the operational and temporal costs. The proposed entropy method is compared with “error minimization” and “K-Means clustering” methods and is shown to be superior. The proposed approach is used for optimizing Eshtehard aquifer monitoring network in central part of Iran. As a result of its implementation, 20 wells are selected among 79 active monitoring wells in the study area, so that by sampling the wells, the maximum qualitative information can be obtained without needing to sample all the wells.

Identification of the optimum groundwater quality monitoring network using a genetic algorithm based optimization approach

Management of groundwater requires a sufficient coverage of accurate groundwater quality data. These data are usually collected from monitoring wells which are spatially distributed in the river basin or the groundwater body that is studied. A minimum number of monitoring wells with an optimum spatial distribution is desired to ensure a cost-effective observation of the groundwater body. Therefore, the configuration of groundwater monitoring networks and the number of required wells becomes an important engineering optimization problem. The goal of this study is to find an optimum monitoring network with the fewest wells that provides sufficient spatial coverage on groundwater quality. With the presented method redundant wells in an already existing network are identified. Here, a genetic algorithm (GA) based optimization approach is used in which each monitoring well in the watershed is represented with a binary GA bit to evaluate if the corresponding monitoring well will be selected for the network. The proposed approach can solve the problem by simultaneously optimizing two conflicting objectives. The first objective is the maximization of the match between the interpolated groundwater quality concentration distributions obtained using data from all available monitoring wells and the wells from the newly generated network. The match is primarily evaluated using the Nash-Sutcliffe (NS) model efficiency. Groundwater quality is represented by the water quality index WQI that aggregates several quality parameters. The second objective deals with the minimization of the number of monitoring wells in the newly generated network by considering cost-related constraints. These two objectives are integrated in a single objective function where different combinations of both objectives are investigated by considering two cases. The applicability of the proposed approach is evaluated for the groundwater monitoring network of the Gediz River Basin (GRB) which is one of the most important river basins in Turkey. Findings indicate that the proposed approach significantly reduces the number of monitoring wells with a relatively small deviation of the spatial distribution of the WQI values. Also, the monitoring network is optimized such that sampling points are removed from less polluted areas and selected in areas with higher pollutant concentrations.