Optimal identification of unknown groundwater contaminant sources in conjunction with designed monitoring networks

Human activities and improper management practices have resulted in widespread deterioration of groundwater quality worldwide. Groundwater contamination has seriously threatened its beneficial use in recent decades. Remediation processes are necessary for groundwater management. In the remediation of contaminated aquifer sites, identification of unknown groundwater contaminant sources has a crucial role. In other words, an effective groundwater remediation process needs an accurate identification of contaminant sources in terms of contaminant source locations, magnitudes and time-release. On the other hand, the efficiency and reliability of contaminant source identification depend on the availability, adequacy, and accuracy of hydrogeologic information and contaminant concentration measurements data. Whereas, generally when groundwater contaminations are detected, only limited and sparse measured contaminant concentration values are available. Usually, groundwater contaminations are detected after a long time, years or even decades after the starting of contaminant source activities or even after their extinction. Therefore, usually, there is not enough information regarding the number of contaminant sources, the duration of sources' activities and the contaminant magnitudes, as well as the hydrogeologic parameters of the contaminated aquifers. Simulations of groundwater flow and solute transport involve intrinsic uncertainties due to this sparse information or lack of enough hydrogeologic information of the porous medium. Therefore, for groundwater management, developing and applying an efficient procedure for identification of unknown contaminant sources is essential. Moreover, available observed contaminant concentration values are usually erroneous and this erroneous data could cause instability in the solution results. Various combinations of source characteristics can result in similar effects at observation locations and cause non-uniqueness in the solution. Due to these instabilities and non-uniqueness in solution (Datta, 2002), the source identification problem is known as an "ill-posed problem" (Yeh, 1986). The non-uniqueness and uncertainties involved in this ill-posed problem make this problem a difficult and complex task. Suggested methodologies to tackle this task are not completely efficient. For instance, the crux of previous approaches is highly vulnerable to the accuracy and adequacy of contaminant concentration measurements and hydrogeologic data. As a result, many of the previously suggested approaches are not applicable to real-world cases and application of relevant approaches to real-world contaminant aquifer sites is usually tedious and time-consuming. The suggested methodologies involve enormous computational time and cost due to repeated runs of the numerical simulation models within the optimisation algorithms. Therefore, to identify the unknown characteristics of contaminant sources, different surrogate models were developed. Three different algorithms were utilized for developing the surrogate models: Self-Organising Maps (SOM), Gaussian Process Regression (GPR), and Multivariate Adaptive Regression Splines (MARS). Performance of the developed procedures was assessed for potential applicability in two hypothetical, an experimental, and a real-world contaminated aquifer sites. In the used contaminated aquifer sites, only limited contaminant concentrations data were assumed to be available. In three cases, it was also assumed that the contaminant concentrations data were collected a long time after the start of the first potential contaminant source activities. The performance evaluations of the developed surrogate models show that these models could accurately mimic the behaviour of simulation models of groundwater flow and solute transport. These surrogate models solutions showed acceptable errors in comparison to the more robust numerical model solutions. These surrogate models were also used for identification of unknown groundwater contaminant sources when utilized to solve the inverse problem. The SOM algorithm was chosen as the surrogate model type in this study for directly addressing the source identification problem as well. The SOM algorithm was chosen for its classification capabilities. In source identification problems, the number of actual contaminant sources is uncertain and usually, a set of a larger number of potential contaminant sources are assumed. Therefore, screening the active sources by SOM-based Surrogate Models (SOM-based SMs) may simplify the source identification problems. The performance of the developed SOM-based SMs was assessed for different scenarios. Results indicate that the developed models could also accurately screen the active sources among all potential contaminant sources with sparse contaminant concentrations data and uncertain hydrogeologic information. For comparison purposes, MARS and GPR algorithms that are precise prediction tools were also utilized for developing MARS and GPR-based Surrogate Models (MARS and GPR-based SM) for source identification. Performance of the developed surrogate models for source identification was evaluated in terms of Normalized Absolute Error of Estimation (NAEE). For example, the performance of the developed SOM, MARS and GPR-based SMs was assessed in an illustrative hypothetical contaminated aquifer site. The results for testing data in terms of NAEE were equal to 16.3, 4.9 and 6.6%, respectively. Performance of the developed SOM, MARS and GPR-based SMs was also evaluated in an experimental contaminated aquifer site. The results for testing data in terms of NAEE were equal to 15.8, 14.1 and 16.2%. These performance evaluation results of the developed surrogate models indicate that the MARS-based SMs can be more accurate models than the SOM and GPR-based SMs in source identification problems. The most important advantage of the developed methodologies is their direct application for source identification in an inverse mode without linking to an optimisation model. Surrogate Model-Based Optimisation (SMO) was also developed and utilized for source identification. In this developed SMO, MARS and Genetic Algorithm (GA) were utilized as the surrogate model and the optimisation model types, respectively. MARS-based SMOs performance was assessed in an illustrative hypothetical contaminated aquifer site and in a real-world contaminated aquifer site. The result of the developed MARS-based SMO for testing data in the illustrative hypothetical contaminated aquifer site in terms of Root Mean Square Error (RMSE) was equal to 0.92. Obtained solution results of the developed MARS-based SM in the real contaminated study area for testing data in terms of RMSE was equal to 42.5. The performance evaluation results of the developed methodologies in different hypothetical and real contaminated study areas demonstrate the capabilities of the constructed SOM, GPR, and MARS-based SMs and MARS-based SMO for source identification. Also, in order to increase the accuracy of source identification results, and based on the preliminary solution results of the developed SOM-based SMs, a sequential sampling method can be applied adaptively for updating the developed surrogate models. Information from a hypothetical contaminated aquifer site was used to assess the performance of this procedure. Performance evaluation results of adaptively developed MARS and GPR-based SMs in terms of NAEE were equal to 1.9 and 2.1%, respectively. The results show 3 and 4.5% improvements for source identification results by applying adaptively developed MARS and GPR-based SMs, respectively. Another difficulty with source identification problems has been the limitation and sparsity of observed contaminant concentrations data. Previously suggested methodologies usually need long-term observation data at numerous locations which can involve large costs. Therefore, developing an effective monitoring network design procedure was one of the main goals of this study. In designing the monitoring networks, two main objectives were considered: 1. Maximizing the accuracy of source identification results, and 2. Limiting the number of monitoring locations. It was supposed that by implementing obtained results from the designed monitoring networks for developing surrogate models, the source identification results would significantly improve. In this study, different algorithms were utilized to identify potentially important and effective monitoring locations which probably could improve source identification results. These algorithms are Random Forests (RF), Tree Net (TN) and CART. The performance of these algorithms was evaluated in different scenarios. Results indicate the potential applicability of these algorithms in recognising the most important components of prediction models. As a result, these algorithms could apply for designing monitoring networks for improving the source identification efficiency and accuracy. Concentration measurement information from a designed monitoring network and from a set of arbitrary monitoring sites was utilized to develop MARS-based surrogate models for source identification. The solution results for these two scenarios of designed monitoring and arbitrary measurements were compared for a hypothetical study area for evaluation purpose. Performance evaluation results of the developed surrogate model using information from the designed monitoring network showed improvement in source identification error in terms of RMSE for testing data by 0.7. The obtained information from the designed monitoring network was used to develop MARSbased SM for source identification of testing data in a real contaminated aquifer site. Source identification results of the developed MARS-based SM with testing data for the real contaminated aquifer site showed improvement by 35.3 in terms of RMSE compared to the solution results of MARS-based SM, which was developed by using obtained information from arbitrary monitoring locations. Performance evaluation results for the developed monitoring network procedure demonstrate the potential applicability of this procedure for source identification

Development and evaluation of models for assessing geochemical pollution sources with multiple reactive chemical species for sustainable use of aquifer systems: source characterization and monitoring network design

Michael designed a groundwater flow and reactive transport optimization model. He applied this model to characterize contaminant sources in Australia's first large scale uranium mine site in the Northern Territory. He identified the contamination sources to the groundwater system in the area. His findings will assist planning actions and steps needed to implement the mitigation strategy of this contaminated aquifer

On the Optimal Spatial Design for Groundwater Level Monitoring Networks

Effective groundwater monitoring networks are important, as systematic data collected at observation wells provide a crucial understanding of the dynamics of hydrogeological systems as well as the basis for many other applications. This study investigates the influence of six groundwater level monitoring network (GLMN) sampling designs (random, grid, spatial coverage, and geostatistical) with varying densities on the accuracy of spatially interpolated groundwater surfaces. To obtain spatially continuous prediction errors (in contrast to point cross‐validation errors), we used nine potentiometric groundwater surfaces from three regional MODFLOW groundwater flow models with different resolutions as a priori references. To assess the suitability of frequently‐used cross‐validation error statistics (MAE, RMSE, RMSSE, ASE, and NSE), we compared them with the actual prediction errors (APE). Additionally, we defined upper and lower thresholds for an appropriate spatial density of monitoring wells. Below the lower threshold, the observation density appears insufficient, and additional wells lead to a significant improvement of the results. Above the upper threshold, additional wells lead to only minor and inefficient improvements. According to the APE, systematic sampling lead to the best results but is often not suited for GLMN due to its nonprogressive characteristic. Geostatistical and spatial coverage sampling are considerable alternatives, which are in contrast progressive and allow evenly spaced and, in the case of spatial coverage sampling, yet reproducible coverage with accurate results. We found that the global cross‐validation error statistics are not suitable to compare the performance of different sampling designs, although they allow rough conclusions about the quality of the GLMN

Mapping Groundwater Levels in Erbil Basin

In the Erbil Basin, which is located in Kurdistan Region at northern part of Iraq, several production wells have been selected for monthly monitoring of groundwater levels. The continuous depletion of groundwater levels has been recorded due to uncontrolled exploitation from both legal and illegal wells that poses a major problem in selected Basin, which is classified as arid and semi-arid regions. Accurate prediction of groundwater depth and elevation maps is crucial for the development of effective groundwater management strategies in the aquifer system of the area. Depth to groundwater measures for each of the 55 wells that distributed across the North, Central, and South sub-basins of Erbil, also mapped and compared with wells data that are recorded periodically by the Directorate of Erbil Groundwater. The methodology of this study is involved mapping groundwater tables for the measured wells in (2022) with surveying wells coordinates as field observations, and compare with data of groundwater tables in (2004) that archived by groundwater directorate. This study employs high-accuracy surveying techniques for the selected wells and utilizes geographic information systems (GIS) as a successful tool for mapping groundwater levels using both Kriging and IDW interpolation methods. The results are indicated successfully that groundwater tables have sudden drawdown during these (18) years, the main reasons behind that is drilling numerous wells without planning and lack of management of the wells system in the study area. Meanwhile, Erbil basin required better planning and management of groundwater resources. The study concludes that there is lack in groundwater management need to keep the sustainability of this vital resources, and observing monthly groundwater levels need to be connected with high accuracy sensor inside observation wells not manually measuring groundwater levels by damaged sounders. Unfortunately, there is no observation wells inside Erbil basin, the recorded data are within production wells that cannot represent as actual levels of the groundwater. The main objective of this study is to present the actual problems in the study area by create the maps of the groundwater table for the selected basin and to be used as a basic plan in developing strategies for effective management and planning of Erbil groundwater resources. And also employed to protect aquifer storage and prevent depletion of groundwater resources

Hydraulic head and groundwater 111Cd content interpolations using empirical Bayesian kriging (EBK) and geo-adaptive neuro-fuzzy inference system (geo-ANFIS)

In this study, hydraulic head and 111Cd interpolations based on the geo-adaptive neuro-fuzzy inference system (Geo-ANFIS) and empirical Bayesian kriging (EBK) were performed for the alluvium unit of Karabağlar Polje in Muğla, Turkey. Hydraulic head measurements and 111Cd analyses were done for 42 water wells during a snapshot campaign in April 2013. The main objective of this study was to compare Geo-ANFIS and EBK to interpolate hydraulic head and 111Cd content of groundwater. Both models were applied on the same case study: alluvium of Karabağlar Polje, which covers an area of 25 km2 in Muğla basin, in the southwest of Turkey. The ANFIS method (called ANFISXY) uses two reduced centred pre-processed inputs, which are cartesian coordinates (XY). Geo-ANFIS is tested on a 100-random-data subset of 8 data among 42, with the remaining data used to train and validate the models. ANFISXY and EBK were then used to interpolate hydraulic head and heavy metal distribution, on a 50 m2 grid covering the study area for ANFISXY, while a 100 m2 grid was used for EBK. Both EBK- and ANFISXY-simulated hydraulic head and 111Cd distributions exhibit realistic patterns, with RMSE < 9 m and RMSE < 8 μg/L, respectively. In conclusion, EBK can be considered as a better interpolation method than ANFISXY for both parameters.Keywords: ANFIS, EBK, interpolation, hydraulic head, metal, 111Cd, alluvium, Muğl

Spatio-temporal models for the analysis and optimisation of groundwater quality monitoring networks

Commonly groundwater quality data are modelled using temporally independent spatial models. However, primarily due to cost constraints, data of this type can be sparse resulting in some sampling events only recording a few observations. With data of this nature, spatial models struggle to capture the true underlying state of the groundwater and building models with such small spatial datasets can result in unreliable predictions. This highlights the need for spatio-temporal models which `borrow strength' from earlier sampling events and which allow interpolations of groundwater concentrations between sampling points. To compare the relative merits of analysing groundwater quality data using spatial compared to spatio-temporal statistical models, a comparison study is presented using data from a hypothetical contaminant plume along with a real life dataset. In this study, the estimation accuracy of spatial p-spline and Kriging models are compared with spatio-temporal p-spline models. The results show that spatio-temporal methods can increase prediction efficiency markedly so that, in comparison with repeated spatial analysis, spatio-temporal methods can achieve the same level of performance but with smaller sample sizes. For the comparison study, in the spatio-temporal p-splines model, differing levels of variability over space and time were controlled using different numbers of basis functions rather than separate smoothing parameters due to the computational expense of their optimisation. However, deciding on the number of basis functions for each dimension is subjective due to space and time being measured on different scales, and thus methodology is developed to efficiently tune two smoothing parameters. The proposed methodology exploits lower resolution models to determine starting points for the optimisation procedure allowing for each parameter to be tuned separately. Working with spatio-temporal models can, however, pose their own problems. Due to the sporadic layout of many monitoring well networks, due to built-up urban areas and transport infrastructure, ballooning can be experienced in the predictions of these models. `Ballooning' is a term used to describe the event where high or low predictions are made in regions with little data support. To determine when this has occurred a measure is developed to highlight when ballooning may be present in the models predictions. In addition to the measure, to try to eliminate ballooning from happening in the first place, a penalty based on the idea that the total contaminant mass should not change significantly over time is proposed. However, the preliminary results presented here indicate that further work is needed to make this effective. It is shown that by adopting a spatio-temporal modelling framework a smoother, clearer and more accurate prediction through time can be achieved, compared to spatial modelling of individual time steps, whilst using fewer samples. This was shown using existing sampling schemes where the choice of sampling locations was made by someone with little knowledge or experience in sampling design. Sampling designs on fixed monitoring well networks are then explored and optimised through the minimisation two objective functions; the variance of the predicted plume mass (VM) and the integrated prediction variance (IV). Sampling design optimisations, using spatial and spatio-temporal p-spline models, are carried out, using a variety of numbers of wells and at various future sampling time points. The effects of well-specific sampling frequency are also investigated and it is found that both objective functions tend to propose wells for the next sampling design which have not been sampled recently. Often, an existing monitoring well network will need to be changed, either by adding new wells or by down-scaling and removing wells. The decision to add wells to the network comes at a financial expense, so it is of paramount importance that wells are added into areas where the gain in knowledge of the region is maximised. The decision to remove a well from the network is equally important and involves a trade-off between costs saved and information lost. The design objective functions suggest a well should be added in an area where the distance to the nearest neighbouring wells is greatest. Finally, consideration is given to optimal sampling designs when it is assumed the recorded data has multiplicative error - a common assumption in groundwater quality data. When modelling with this type of data, the response is normally log transformed prior to modelling and the predictions are then transformed back onto the original scale for interpretation. Assuming a log transformed response, the objective functions, initially presented, can be used if computation of the objective function is also on the log scale. However, if the desired scale of interpretation of the objective functions is the original scale but modelling was performed on the log scale, the resulting objective function values cannot simply be exponentiated to give an interpretation on the original scale. Modelling on the log scale while interpreting the objective function on the original scale can be achieved by adopting a lognormal distribution for the predicted response and subsequently numerically integrating its variance to compute the IV objective function. The results indicate that the designs do differ depending on which scale interpretation of the objective function is to be made. When interpreting on the original scale the objective function favours sampling from wells where higher values were previously estimated. Unfortunately, computation of the VM objective function when assuming a lognormal distribution has not been achieved so far

TWINLATIN: Twinning European and Latin-American river basins for research enabling sustainable water resources management. Combined Report D3.1 Hydrological modelling report and D3.2 Evaluation report

Water use has almost tripled over the past 50 years and in some regions the water demand already exceeds supply (Vorosmarty et al., 2000). The world is facing a “global water crisis”; in many countries, current levels of water use are unsustainable, with systems vulnerable to collapse from even small changes in water availability. The need for a scientifically-based assessment of the potential impacts on water resources of future changes, as a basis for society to adapt to such changes, is strong for most parts of the world. Although the focus of such assessments has tended to be climate change, socio-economic changes can have as significant an impact on water availability across the four main use sectors i.e. domestic, agricultural, industrial (including energy) and environmental. Withdrawal and consumption of water is expected to continue to grow substantially over the next 20-50 years (Cosgrove & Rijsberman, 2002), and consequent changes in availability may drastically affect society and economies. One of the most needed improvements in Latin American river basin management is a higher level of detail in hydrological modelling and erosion risk assessment, as a basis for identification and analysis of mitigation actions, as well as for analysis of global change scenarios. Flow measurements are too costly to be realised at more than a few locations, which means that modelled data are required for the rest of the basin. Hence, TWINLATIN Work Package 3 “Hydrological modelling and extremes” was formulated to provide methods and tools to be used by other WPs, in particular WP6 on “Pollution pressure and impact analysis” and WP8 on “Change effects and vulnerability assessment”. With an emphasis on high and low flows and their impacts, WP3 was originally called “Hydrological modelling, flooding, erosion, water scarcity and water abstraction”. However, at the TWINLATIN kick-off meeting it was agreed that some of these issues resided more appropriately in WP6 and WP8, and so WP3 was renamed to focus on hydrological modelling and hydrological extremes. The specific objectives of WP3 as set out in the Description of Work are

Optimization Strategies for Spatio-temporal Groundwater Dynamics Monitoring

Räumlich kontinuierliche Daten sind in der Hydrogeologie für die wissenschaftliche Forschung, die Risikobewertung und wasserwirtschaftliche Entscheidungsprozesse von wesentlicher Bedeutung. Die meisten dieser Informationen werden allerdings nur punktuell durch Messungen an Grundwassermessstellen erhoben und anschließend regionalisiert. Die Vorhersagegenauigkeit dieser räumlich interpolierten Daten, die in der Regel die Grundlage für weitere Berechnungen und Entscheidungen bilden, ist stark abhängig von der Konzipierung des Grundwassermessnetzes, d.h. von der räumlichen Verteilung und Dichte der Grundwassermessstellen, der Beprobungshäufigkeit, dem Interpolationsverfahren sowie dem Wechselspiel zwischen diesen Faktoren. Daraus ergibt sich ein erhebliches Optimierungspotenzial hinsichtlich des Grundwassermessnetzes und der Regionalisierungstechnik. Geeignetes Grundwassermessnetze sind daher wichtige Instrumente für die nachhaltige Bewirtschaftung und für den Schutz der Grundwasserressourcen. Sie bieten Zugangspunkte für die Überwachung von Grundwasserständen und -proben und ermöglichen so einen Einblick in die Grundwasserverhältnisse. Die Kombination aus hohen Erschließungskosten und einer verhältnismäßig geringen räumlichen Repräsentativität der Brunnen aufgrund der hydrogeologischen Heterogenität machen die Konzeption eines geeigneten Überwachungsnetzes zu einer großen Herausforderung. Diese Arbeit beschäftigt sich mit Techniken zum verbesserten Verständnis der Grundwasserdynamik durch (i) räumliche und (ii) räumliche-zeitliche Optimierung von Grundwasserstands Messnetzen und (iii) verbesserter räumlichere Vorhersage der an diesen Überwachungsbrunnen gewonnenen Daten unter Verwendung von Interpolationstechniken. Zu diesem Zweck wurde im ersten Teil dieser Arbeit eine umfassende Untersuchung der meistgenutzten deterministischen und geostatistischen, uni- und multivariaten Interpolationstechniken für die Erstellung von Grundwassergleichenplänen in einem Untersuchungsgebiet durchgeführt, das durch eine komplexe Interaktion zwischen Karst, einem alluvialen Grundwasserleiter und gering durchlässigen Schichten der alpinen Molasse gekennzeichnet ist. Die untersuchten Methoden wurden durch globale Kreuzvalidierung und öko-hydrogeologische Informationen an Karstquellen, Feuchtgebieten, Oberflächengewässern und Profilschnitten bewertet. Der mögliche Effekt der Methodenwahl auf die weitere Berechnung wurde durch Abschätzung der Austauschprozesse zwischen Karst- und Alluvialgrundwasserleiter auf Basis der geschätzten Potentialunterschiede durchgeführt. Die Ergebnisse zeigen, dass die Verfahrenswahl, insbesondere bei unzureichendem Überwachungskonzept, drastische Auswirkungen auf die nachfolgenden Berechnungen haben kann. Die Studie hat ergeben, dass geostatistische oder Kriging Interpolationsmethoden den deterministischen Interpolationsmethoden überlegen sind. Bei dürftiger Grundwasserdatenlage kann das Co-Kriging mit räumlich kreuzkorrelierten Sekundärvariablen (z. B. Höhenlage, Flusspegel), die häufiger erfasst werden, wertvolle Informationen über die Primärvariable bereitstellen und so die Varianz des Schätzfehlers verringern. Im zweiten Teil dieser Arbeit wurden räumliche Monitoringkonzepte mit unterschiedlichen Messdichten an numerisch modellierter Grundwasseroberflächen mit verschiedenen Skalen und Dynamiken untersucht. Ziel war es, Einblicke in geeignete Monitoringansatze für eine verlässliche räumliche Abschätzung des Grundwasserspiegels zu gewinnen und eine Überwachungsdichte abzuleiten, bei der ein angemessenes Information/Kosten-Verhältnis erreicht wird. Die Interpolationsergebnisse wurden mit globaler Kreuzvalidierung und dem tatsächlichen räumlichen Fehler evaluiert, der anhand der numerischen Modellflächen als A-priori-Referenz errechnet wurde. Überwachungsnetze mit einer regelmäßigen Gitteranordnung boten zwar genaueste räumliche Vorhersagen für das betrachtete Dichtespektrum, sind jedoch aufgrund ihrer Nachteile, wie der mangelnden Erweiterungsfähigkeit, tendenziell ungeeignet. Eine vergleichbar gute Leistung wurde erzielt, wenn der maximale Vorhersage-Standardfehler als Auswahlkriterium für zusätzliche Brunnen für bestehende Messnetze verwendet wurde. In dieser Studie wurde außerdem eine neuartige Optimierungsstrategie für Überwachungsnetze angewandt, die auf mathematischen Quasi-Zufallsfolgen basiert. Der Ansatz liefert ebenfalls überzeugende Ergebnisse und bietet mehrere Vorteile. Er bedarf keinerlei Vorkenntnisse über den Grundwasserleiter durch vorhandene Brunnen und es werden unabhängig von den Ausbaustufen reproduzierbare räumliche Anordnungen erzielt. Im dritten Teil wurde ein datengesteuerter Sparse-Sensing-Algorithmus-Ansatz zur Auswahl von spärlichen Sensorpositionen unter Nutzung von Techniken zur Dimensionsreduktion untersucht und für die zeitliche und räumliche Optimierung eines bestehenden Grundwasserstandsmessnetzes im Oberrheingraben adaptiert. Die Optimierung erfolgt mit einem greedy search (QR)-Algorithmus, der die Überwachungsbrunnen nach ihrem Informationsgehalt über Aquifer-Dynamik selektiert und einordnet. Als Eingangsdaten wurden langjährige Ganglinien-Aufzeichnungen verwendet, um repräsentative Messstellen oder Messstellen mit redundantem oder niedrigem Informationsgehalt zu bestimmen. Des Weiteren wurde eine Optimierung auf der Grundlage regionalisierter, wöchentlicher Grundwassergleichenkarten durchgeführt, um mögliche geeignete Standorte für zusätzliche Messstellen zu identifizieren. Die Suche wurde durch eine räumliche Kostenfunktion gelenkt, bei der weniger geeignete Standorte abgewertet wurden. Der untersuchte Ansatz hat sich als potenziell wertvolles Instrument für die Optimierung der Brunnenanzahl und deren Standorte, für die Reduzierung und den Ausbau des Netzes aber auch für eine kombinierte Nutzung beider Möglichkeiten erwiesen

Prostorna analiza električne vodljivosti podzemnih voda pomoću običnoga kriginga i metoda umjetne inteligencije (slučaj ravnice Tabriz, Iran)

rtificial intelligence (AI) systems have opened a new horizon to analyze water engineering and environmental problems in recent decades. In this study performances of ordinary kriging (OK) as a linear geostatistical estimator and two intelligent methods including artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are investigated. For this purpose, geographical coordinates of 120 observation wells that located in Tabriz plain, north-west of Iran, were defined as inputs and groundwater electrical conductivities (EC) were set as output of models. Eighty percent of data were randomly selected to train and develop mentioned models and twenty percent of data used for testing and validating. Finally, the outputs of models were compared with the corresponding measured values in observation wells. Results indicated that ANFIS model provided the best accuracy among models with the root mean squared error (RMSE) value of 1.69 dS.m–1 and correlation coefficient (R) of 0.84. The RMSE values in ANN and OK were calculated 1.97 and 2.14 dS.m–1 and the R values were determined 0.79 and 0.76, respectively. According to the results, the ANFIS method predicted EC precisely and can be advised for modeling groundwater salinity.u posljednjih nekoliko desetljeća sustavi umjetne inteligencije (AI) su otvorili nove horizonte u analizi problema vodnog inženjeringa te ekoloških problema. U ovoj studiji istražene su performanse običnog kriginga (OK) kao geostatističkog procjenitelja te performanse dvaju naprednih metoda, prva od kojih je umjetna neuronska mreža (ANN), a druga je hibridni sustav ANFIS (Adaptive Neuro-Fuzzy Inference System) koji uz neuronsku mrežu uključuje i neizravnu (fuzzy) logiku. U tu svrhu, zemljopisne koordinate 120 mjernih bunara lociranih u ravnici Tabriz u sjeverozapadnom Iranu definirane su kao ulazi, a električne vodljivosti (EC) podzemnih voda postavljeni su kao izlazi modela. Osamdeset posto podataka nasumce je izabrano za razvoj i obuku (učenje) navedenih modela, a dvadeset posto podataka iskorišteno je za testiranje i provjeru. Na kraju, izlazi modela su uspoređeni s odgovarajućim mjerenim vrijednostima u mjernim bunarima. Rezultati su pokazali da model ANFIS među svim promatranim modelima daje najbolju točnost s korijenom srednje kvadratne pogreške (RMSE) od 1,69 dS.m–1 i koeficijentom korelacije (R) od 0,84. Izračunate vrijednosti RMSE u modelima ANN i OK iznose 1.97, odnosno 2.14 dS.m–1, a koeficijenata korelacije 0,79, odnosno 0,76, respektivno. Prema dobivenim rezultatima ANFIS metoda je precizno predvidjela električnu vodljivost te se stoga može preporučiti za modeliranje saliniteta podzemnih voda