Assessment of groundwater vulnerability using supervised committee ...

Environ Sci Pollut Res DOI 10.1007/s11356-017-8489-4

RESEARCH ARTICLE

Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models Ata Allah Nadiri 1 & Maryam Gharekhani 1 & Rahman Khatibi 2 & Asghar Asghari Moghaddam 1

Received: 18 August 2016 / Accepted: 19 January 2017 # Springer-Verlag Berlin Heidelberg 2017

Abstract Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise to differing values with no theoretical or empirical basis to establish a validated baseline or to develop a comparison basis between the modeling results and baselines, if any. Therefore, this research presents a supervised committee fuzzy logic (SCFL) method, which uses artificial neural networks to overarch and combine a selection of FL models. The indices are expressed by the widely used DRASTIC framework, which include geological, hydrological, and hydrogeological parameters often subject to uncertainty. DRASTIC indices represent collectively intrinsic (or natural) vulnerability and give a sense of contaminants, such as nitrate-N, percolating to aquifers from the surface. The study area is an aquifer in Ardabil plain, the province of Ardabil, northwest Iran. Improvements on vulnerability indices are achieved by FL techniques, which comprise Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL). As the correlation Responsible editor: Marcus Schulz * Ata Allah Nadiri [email protected] Maryam Gharekhani [email protected] Rahman Khatibi [email protected] Asghar Asghari Moghaddam [email protected] 1

Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran

2

GTEV-ReX Limited, Swindon, UK

between estimated DRASTIC vulnerability index values and nitrate-N values is as low as 0.4, it is improved significantly by FL models (SFL, MFL, and LFL), which perform in similar ways but have differences. Their synergy is exploited by SCFL and uses the FL modeling results Bconditioned^ by nitrate-N values to raise their correlation to higher than 0.9. Keywords Ardabil aquifer . Fuzzy logic . Supervised committee fuzzy logic (SCFL) . Vulnerability index

Introduction Research overview The focus of this paper is on assessing aquifer vulnerability, which is liable to impacts of anthropogenic contamination on aquifers from agricultural or industrial sources. Vulnerability treats collectively intrinsic factors within a system susceptible to adverse conditions. Its assessment procedure, first introduced by United States Environmental Protection Agency (US EPA) (Aller et al. 1987), is adopted in this paper as the baseline assessment. The paper builds on existing research on the application of artificial intelligence (AI) techniques to the DRASTIC framework. The DRASTIC framework by Aller et al. (1987) is the most widely used approach to assess groundwater vulnerability to contamination (Kim and Hamm 1999; Panagopoulos et al. 2006; Sener et al. 2009; Misstear et al. 2009; Bai et al. 2012; Anane et al. 2013; Su et al. 2014a; Sadeghfam et al. 2016). The term DRASTIC refers to the acronym of seven geological and hydrogeological parameters, which affect and control contamination in groundwater. These parameters reflect general vulnerability and specific vulnerability, where the latter accounts for the vulnerability of groundwater to certain pollutants, e.g., nitrate-N. Both types of these parameters are

Environ Sci Pollut Res

detailed in due course. In spite of the popularity of the DRASTIC framework, it relies on expert judgment for assigning weights and rates of the contributory parameters, and these expose the output vulnerability maps to further uncertainties but the focus is on their treatments by AI techniques using fuzzy logic (FL). Among different AI techniques, this paper uses two of them, which are FL to treat subjectivity in DRASTIC indices and artificial neural networks (ANNs) for a specific purpose, as discussed later. FL techniques are known to be particularly suitable to imprecise input data. This received view has been underlined by Bárdossy and Disse (1993), stating that fuzzy models tend to be robust to parameter changes and are tolerant to imprecision. The narrative for implementing FL-based models is given in due course, but existing FL-based capabilities outside the DRASTIC framework have evolved as follows: generation 1 models were built on Zadeh’s fuzzy set theory (Zadeh 1965) through three steps, which are fuzzification, inference engine (fuzzy rule base), and defuzzification, a procedure which involves prescribed rules. Generation 2 emerged since the 1980s by using clustering methods by systematically seeking rules, e.g., Sugeno fuzzy logic (SFL) model, Mamdani fuzzy logic (MFL) model, and Larsen fuzzy logic (LFL) model. Generation 3 models are topical research for a new capability to combine multiple models for their synergies. To the best knowledge of the authors, combining multiple AI models is being developed as follows: (i) the idea goes back to Naftaly et al. (1997), although ensemble averaging goes back to much earlier times, and Chen and Lin (2006) weighted different data layers using simple averages; (ii) Kadkhodaie-Ilkhchi et al. (2009) and Labani et al. (2010) used weighted averaging identified by genetic algorithm (GA); and (iii) the authors’ research seeks nonlinear implementations for combining models. Table 1 summarizes the past research on FL-based DRASTIC assessments, and they are categorized below. Category 0 refers to as analytic hierarchy process (AHP), which modifies the weights of DRASTIC parameters by a scheme in terms of relative importance of the DRASTIC parameters, e.g., Javadi et al. (2011), Huan et al. (2012), and Neshat and Pardhan (2014). Category 1 includes fuzzy logic tools (FLTs) and fuzzy analytic hierarchy process (FAHP); both of which are limited to generation 1 FL models of fuzzifying the DRASTIC layers as fuzzy overlays in the GIS software by covering input data, treating vulnerability indices as output of the fuzzified data. These capabilities have no optimization and rule definitions facilities, but their main differences are in assigning weights. FLTs assign the weights for each layer as per Aller et al. (1987), e.g., Dixon (2005), and Gemitzi et al. (2006), Mohammadi et al. (2009), whereas FAHP assign weights as in AHP (Sener and Davraz 2013). The above applications were likely to have used prescribed rules, and if they involved rule definitions by data clustering to improve output

results, they probably used manual processing susceptible to expert opinions, subjectivity, and uncertainty. Category 2 FL-based models applied to the DRASTIC framework include Rezaei et al. (2013) using the SFL model, Dixon (2004) using the neuro-fuzzy (NF) model, and Baghapour et al. (2016) using the ANN model. Category 3 FL-based models refer to both linear and nonlinear combinations of multiple AI models, as introduced by Nadiri et al. (2013). The application by Fijani et al. (2013) overarched ANN, NF, SFL, and MFL models of the Maragheh-Bonab plain aquifer, East Azerbaijan, Iran, by committee machine with artificial intelligence (CMAI), as well as by supervised committee machine with artificial intelligence (SCMAI) to assess vulnerability indices. Notably, CMAI models use linear combinations but SCMAI provide nonlinear combinations by using ANN. The results (Nadiri et al. 2013; Fijani et al. 2013; Tayfur et al. 2014; Nadiri et al. 2017) show that their performances may be ranked as SCMAI, CMAI, and the individual models. SCMAI models are the focus of this paper and are explored in a greater detail in this paper. To the best knowledge of the authors, there are gaps in the application of different types of FL-based techniques to assess vulnerability indices by the DRASTIC framework, as the full potentials of the available techniques are yet to be explored and their applications to diverse aquifers are yet to reach the stage of 'working tools’. Thus, three FL models of SFL, MFL, and LFL are developed, which normally provide similar acceptable efficiency but with different characteristic strengths and weaknesses. Special form of SCMAI, to be referred to as supervised committee fuzzy logic (SCFL) models, aims to exploit the synergy inherent in the three FL models. In these SCFL models, ANN is used to identify and seek synergies in the constituent FL models alone unlike SCMAI, which has a broader remit of constituent models. Thus, ANN receives the outputs from the three individual FL models as its input and derives a new predictions model, conditioned by observed nitrate-N values. Each individual FL model has its own way of handling uncertain parameters in the DRASTIC framework, but the SCFL model reaps on the most favorable one fitting the set performance criterion. Objective of the study The objectives of this study are (i) to adopt the DRASTIC framework to assess groundwater vulnerability in the study area; (ii) to evaluate the capability of three individual FL models (SFL, MFL, and LFL models) for improving the DRASTIC framework; (iii) to compare efficiencies of SFL, MFL, and LFL models to assess vulnerability indices; and (iv) to use SCFL to reap advantages of models in order to achieve higher reliability. The Ardabil plain aquifer, the Ardabil province, northwest of Iran, provides water resources for domestic, industrial, and agricultural demands. The nitrate (NO3-N) levels exceeding 10 mg/L


DRASTIC

Javadi et al. (2011) Huan et al. (2012), Neshat and Pardhan (2014)

FLT

Individual AI Modeling

SCMAI

SCFL

ANN NF SFL MFLLFL

Various

Various

FAHP

Category 0

Model References

Categories

Past applications of FL and ANN types of AI to DRASTIC framework Method

Table 1

Dixon et al. (2005)

Afshar et al (2007)

Category 1

Nobre et al.(2007)

Dixon (2004) Rezaei et al. (2013) Baghapour et al. (2016) Fijani et al. (2013) Nadiri et al (2017) Present Study

Category 2

Mohammadi et al. (2009) Şener and Şener (2015)

Category 3

Optimize DRASTIC by AI Techniques

Gemitzi et al. (2006)

ANN, NF, MFL, FL run by ANN SVM, NF, GEP run by ANN SFL, MFL, LFL run by ANN

FAHP fuzzy analytic hierarchy process, ANN artificial neural network, NF neuro-fuzzy, GEP gene expression programming, SVM support vector machine, SFL Sugeno fuzzy logic, MFL Mamdani fuzzy logic, LFL Larsen fuzzy logic, SCMAI supervised committee machine with artificial intelligence, SCFL supervised committee with fuzzy logic

concentration have been detected in several water wells in the aquifer. This value is considered as the maximum concentration limit by US EPA (2009). Between the polluting agricultural and industrial activities, the dominant source of nitrate contamination has been identified as agricultural activities in the study area. Therefore, the assessment of vulnerability in the aquifer is necessary to effectively manage agricultural activities.

Methodology The context for the DRASTIC framework may be captured by the S-P-R-C framework, outlined as follows: in groundwater management, the source of adverse conditions often stems from land use distributing contaminants from agriculture and/or industry; pathways are unsaturated zones through percolation and diffusion of contaminants from the surface to groundwater; recipients are aquifers; and consequences on groundwater resources can be incalculable impacts on

groundwater resources, as these are slow processes and their remediation costs are high. General DRASTIC framework The general DRASTIC framework was developed by the US EPA in the 1980s to evaluate groundwater vulnerability potential to contaminations (Aller et al. 1987). This considers the following seven parameters: Depth to water table (D), net Recharge (R), Aquifer media (A), Soil media (S), Topography or slope (T), Impact of the vadose zone (I), and hydraulic Conductivity (C) of the aquifer. The DRASTIC vulnerability index calculates using the following formula: DRASTIC index ¼ Dr Dw þ Rr Rw þ Ar Aw þ S r S w þ T r T w þ I r I w þ Cr Cw

ð1Þ

where D, R, A, S, T, I, and C are the seven parameters and the subscripts Br^ and Bw^ refer to the rates and weights,


respectively. The procedure is as follows: (i) raw data for each of the seven parameters are prepared; (ii) prescribed rates are assigned between 1 and 10 for each parameter, as recommended by Aller et al. (1987), in which the higher the rate is, the higher the vulnerability of the area is; (iii) designated weights are assigned to each and all reclassified parameters, ranging from 1 to 5 (most significant), see Aller et al. (1987), such that the higher the weight is, the greater the aquifer vulnerability is; and (iv) hence, the evaluated value expressed by relationship (1) above. All DRASTIC parameters and groundwater vulnerability maps are carried out using a commercial GIS package. Two notes are outlined below for clarity. In note 1, the paper refers to the formulation underlying DRASTIC as a framework. This underlines the fact that the formulation is consensual and without a theoretical foundation. The term framework brings the assessment of vulnerability index in line with similar initiatives in different disciplines. For instance, the source, pathway, receptor, and consequences (S-P-R-C) is a descriptive framework in risk management that harmonizes the interdisciplinary activities without any theoretical foundation or reason for the approach. In note 2, DRASTIC is a consensual framework, and one expects it to conform to the distribution of nitrate-N. Thus, nitrate-N values are used to Bcondition^ the modeled values as the best available information instead of more justifiable calibration process. FL modeling The first stage in the evolutionary stage of the uptake of fuzzy logic was FL modeling. This is a process in which the sets of input data are reformulated using fuzzy set theory (Zadeh 1965) and are transformed into sets of output data. Fuzzy set theory can handle problems associated with inherent uncertainty and are more robust to describe parameters with inherent imprecision, as the case is on vulnerability index defined by the DRASTIC framework. Fuzzy sets include partial membership ranging between 0 and 1. Each fuzzy set is represented by a membership function (MF), which has ambiguous boundaries and gradual transitions between the defined sets to overcome the inherent uncertainty (Grande et al. 2010). The membership functions may have different shapes, such as Gaussian, trapezoidal, sigmoid, and triangular. The procedure in FL modeling consists of the following three steps: fuzzification, inference engine (fuzzy rule base), and defuzzification, but for more detail, see Nadiri (2015). The second stage in evolutionary development of the uptake of FL modeling comprises the treatment of possible structures in imprecise datasets and the identification of optimum number of rules. Clustering methods identify possible structures in datasets and their optimum rule numbers. They emerged in the 1980s to deal with the identification of structures (Bezdek and Hathaway 1988; Chiu 1994). The fuzzy

clustering methods identify natural groupings in the data and within large datasets and thereby reveal possible patterns to represent specific parts of the system behavior (Li et al. 2001; Nadiri 2015). The most appropriate clustering methods are the subtractive clustering (SC) (Chiu 1994; Chen and Wang 1999; Li et al. 2001) and fuzzy c-means (FCM) (Bezdec 1981). The important parameter in the SC method is the cluster radius, which controls the number of clusters and fuzzy rules (Chen and Wang 1999). Decreasing the cluster radius will increase the number of clusters and lead to small but numerous clusters (Chiu 1994). This will create more rules and unduly complicate the system behavior with possible impacts leading to poor performances. In contrast, a large cluster radius produces large but few clusters in the data. This will result in few but coarse rules (Nadiri 2015), which may not be sufficient to cover the entire domain. Searching for the optimum cluster radius is through systematically varying the cluster radius from 0 to 1 until meeting its minimum root-mean-square error (RMSE). The FCM method classifies data points that populate multidimensional spaces into a specific number of clusters. The FCM clustering starts with an initial guess for the cluster centers to mark the mean location of each cluster. Additionally, for each cluster, FCM assigns every data point a membership grade (Bezdec 1981). FCM iteratively moves the cluster centers to the correct location within a dataset by iteratively updating the cluster centers and the membership grades for each data point. During this iteration, an objective function is minimized based on the distance from any given data point to a cluster center weighted by the membership grade of that data point. The FCM output is a list of cluster centers and several membership grades for each data point. Therefore, the MFL fuzzy rules are extracted through FCM, and these are used in calculating the model matrices of data by passing through the FCM algorithm and the cluster centers. In the FCM algorithm, the number of clusters is defined by the user. The optimum number of clusters is chosen by measuring the model performance by changing systematically the number of the clusters from 1 to the number of the model data points (Kadkhodaie-Ilkhchi et al. 2009). Fuzzy models Based on the type of the fuzzy operators and the output membership function, FL models may be constructed by the methods proposed by Sugeno, Mamdani, and Larsen (Sugeno 1985; Mamdani 1976; Mamdani and Assilian 1975; Larsen 1980). In the SFL method, the output membership functions are linear or constant that is called zero- or first-order Sugeno Fuzzy model, respectively (Sugeno 1985), but in the MFL method, the output membership functions are fuzzy sets. Therefore, the output variables need defuzzification


(Mamdani 1976). The SC method is adopted by the SFL model construction. Nadiri et al. (2014) and Tayfur et al. (2014) show that data clustering is efficient and effective in

determining the number of membership functions and rules. For groundwater vulnerability prediction by the first-order SFL model, a fuzzy if-then rule i is expressed in this study as

MFiR and A belongs to MFiA Rulei : If D belongs to MFiD and R belongs to i i i and S belongs to MF S and T belongs to MFT and I belongs to MFI and i C belongs to MFC ; then ðCVIi ¼ mi D þ ni R þ pi A þ qi S þ d i T þ k i I þ f i C þ c1i Þ

where mi, ni, pi, qi, di, ki, fi, and ci are the coefficients. The final output (Out j ) is the weighted average of all outputs (aggregation) as follows: ∑ wij Outij Out j ¼

i

∑ wij

ð3Þ

i

where wij is the firing strength of rule i and Outj, which is estimated via the Band^ (minimize) operator. In contrast to the SFL method, the FCM method is the most appropriate clustering method for MFL and LFL model (Newton et al. 1992; Lee 2004). For groundwater vulnerability prediction by MFL model, a fuzzy if-then rule i is expressed in this study as

i i i Rulei : If D belongs to MF and R belongs to MF and A belongs to MF D R A i T belongs to MFiT and I belongs to MFiI and and S belongs to MF S and C belongs to MFiC ; then CVI belongs to MFiCVI

where conditioned vulnerability index (CVI) are is the output, MFiCVI are the corresponding membership function of the output of rule i, MFiD is the membership function of the ith cluster of input D, MFiR is the membership function of the ith cluster of input R, and so forth. The operator among the input membership function is the Band^ (minimize) operator, and the outputs from the rules are aggregated via the Bor^ (maximize) operator. The most popular defuzzification method, centroid calculation, was applied to produce the crisp output. The LFL method is similar to the MFL method with the main difference of using the product operator for the fuzzy implication which scales the output fuzzy set.

SCFL model A further stage in the evolution of fuzzy logic is the development of committee fuzzy logic (CFL) approaches, where the term committee is understood to refer to combining models that are not too different. Driven by CFL, different fuzzy logic models are produced but it is recognized that the results will not be identical thought the efficacy of some clusters of results may be more than others. CFLs compare the performances of different models, but their collective output is selected from the better performing model as the overall model. Therefore, the combined results reap advantages of all FL models under study to produce the final output (Kadkhodaie-Ilkhchi et al. 2009).

ð2Þ

ð4Þ

Previous CFLs used two methods of simple averaging and weighted averaging for the construction of committee machine models (Labani et al. 2010; Tayfur et al. 2014; Nadiri et al. 2015). Chen and Lin (2006) show that it can be guaranteed that CFL will perform better than its individual constituent models. In this study, the linear combination method is replaced by ANN as a nonlinear combiner method and is referred to as the SCFL model. It employs ANN as a supervising or overarching combiner of FL models. The supervision operates over three FL models shown in Fig. 1 and includes the following two steps: step 1 estimates CVI using the SFL, MFL, and LFL models, and step 2 constructs a supervised ANN as a nonlinear and supervised combiner. The combination by SCFL is expressed mathematically as ^ ¼ FLi ðD; R; A; S; T ; I; C Þ CVI h i ^ Oi ¼ f i b j þ ∑ i W ji CVI ^ SCFL ¼ f 2 bk þ ∑i W kj O j Ok ¼ CVI

ð5Þ ð6Þ ð7Þ

^ is the output of each FL model which has been used where CVI as ith input; f1 and f2 are the activation functions for the hidden layer and output layer, respectively; Oj is the jth output of nodes in hidden layer; Wji and Wkj are weights that control the strength of connections between two layers; and the biases bj and bk are used to adjust the mean value for hidden layer and output layer,

Environ Sci Pollut Res Fig. 1 A schematic of SCFL model structure

respectively. The activation functions are hyperbolic tangent sigmoid (tansig) for f1 and linear (purelin) for f2 The output ^ SCFL . In the ANN training step, Ok of the SCFL model is CVI the Levenberg–Marquardt (LM algorithm) was adopted as a learning algorithm to estimate the weights Wji and Wkj and biases (ASCE 2000; Nourani et al. 2008a, b; Asadi et al. 2014; Chitsazan et al. 2014).

Study area Location and physical details The study area is located in the Ardabil province, northwest Iran (Fig. 2). Ardabil plain covers an area of approximately 940 km2 surrounded by Bozqush mountains at its south (3306 m above mean sea level (amsl)), Talesh mountains (normally known as Baghri Daghi of 3197 m amsl) at its southeast and east, and Sabalan mountains to its west (normally known as Savalan Daghi reaching 4850 m amsl, with a permafrost at its summit). Based on de Martonne (1925) and Emberger (1930), the prevailing climate in the study area is semi-arid within a cold climatic zone. The average annual precipitation and mean annual temperature are, respectively, 273.7 mm and 9.7 °C (Ardabil synoptic station 1984–2014). In general, average monthly relative humidity at the Ardabil synoptic station ranges from 68% (June) to 75% (February). The historic city of Ardabil is the provincial capital and overlooks Ardabil plain, which was once known as the breadbasket of the region. Ardabil plain defines the study area, and as such, it relates to the Qarasu basin, a notable river flowing into the River Araz, and this flows to the Caspian Sea. The significant tributaries of Qarasu within this plain include

Balikhlichay (the River Balikhli) and Qarachay (the River Qara), and these rise in surrounding mountains. Geology The study area largely comprises Cenozoic formations, but older formations are encountered in north of the region. The geological map of the Ardabil area is shown in Fig. 3. Hydrogeologically, igneous and pyroclastic rocks are located at the southwest of the plain, with very little contribution to aquifer recharge. Carbonate rocks are exposed in the northern and southwestern plain (Rahimzadeh and Babakhani 1987). Due to a low level of fractures and joints, these rocks have low ability in transferring water, and therefore, their effects on aquifer recharge are expected to be very limited. Marls and sandstone formations are composed of marl, siltstone, and brown to gray conglomerate. These rocks are located in southwestern Ardabil plain. Existence of thin evaporated layers in this formation deteriorates groundwater quality. They have little effects on plain recharge too. Noncarbonate rocks consist of weakly cemented volcanic ashes, and conglomerate formations are common in the northeast of the plain. They are fully effective in aquifer recharge. In the west of Ardabil plain, outcrops comprise noncarbonate rocks and conglomerate. Due to the abundance of springs within the vicinity of the Mount Sabalan, these formations are very effective in Ardabil plain to recharge the underlying aquifer (Kord and Asghari Moghaddam 2013). There are several faults in the Ardabil basin (Rahimzadeh and Babakhani 1987). The most important faults include the Talesh western fault, the Ardabil eastern fault, and the Sabalan faults. The Talesh western fault extends from northeast to southwest. The northsouth trending Ardabil eastern fault is parallel to the Astara fault.


Fig. 2 Location map of study area

Hydrogeology and hydrochemistry The aquifer in Ardabil plain is an unconfined aquifer and composed of clay, silt, sand, and gravel. This group includes a young terrace, which makes up the main material of the aquifer within a young terrace through the erosion of old formations. Groundwater in the aquifer is withdrawn through 2622 water wells, 77 springs, and 36 qanats. Based on 40 pumping tests carried out in the plain by the Ardabil Regional Water Authority, the transmissivity of the Ardabil plain aquifer varies between 38 and 2300 m2/d. The eastern and central parts of the aquifer have high transmissivity owing to the coarse granular sediments and high aquifer thickness. The geoelectrical survey delineated the thickness, where its maximum value is approximately 220 m that is located at the east and southeast of the plain. Fifty-five observation wells were installed in Ardabil plain to monitor groundwater levels (Fig. 2). Their locations are distributed over the entire region shown in Fig. 3. Groundwater levels have been recorded by Ardabil Regional Water Authority since 1970. Groundwater level has declined 12 m during the past 25 years in Ardabil plain. Due to high withdrawal rates in

the south and southeast of the plain, the maximum cone of depression is produced in this area. Maximum seasonal variation was observed from October (the lowest groundwater level) at the end of the irrigation season to March (the highest groundwater level). Figure 3 shows the groundwater level distribution in 2012. The altitude of the groundwater level in the study area varies from 1296 to 1528 m above the mean sea level. Groundwater flow direction in the Ardabil plain aquifer is from other directions towards the northwest of the plain. From hydrochemical points of view, the carbonate is dominant water type in this study area. Due to the ion exchange process in the Miocene formations, the water salinity increases in the north-south direction. The average pH and electric conductivity in the Ardabil aquifer are 8.34 and 1371.74 μ℧/cm, respectively (Kord et al. 2013).

Dataset preparation The data preparation is a time-consuming process in this type of investigations. The best practice procedure is outlined below, followed by the details appropriate to this study.


Fig. 3 Geological map of study area (modified from Rahimzadeh and Babakhani 1987)

Best practice procedure The set of seven layers prepared in this study follows Aller et al. (1987) and outlined as follows: Depth The depths to water table values are calculated as surface levels minus water table levels. Surface levels are available from the Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) providing database for digital elevation model (DEM) data (with the spatial resolution of 30 m and vertical accuracy of 1 m). Water table levels are normally obtained from groundwater level data of observation wells. Recharge The anual net recharge is commonly estimated based on the storage changes of the aquifer groundwater (Scanlon et al. 2002). In unconfined aquifers, groundwater levels change due to recharge from surface when there is no pumping from the aquifer but pumpage has to be allowed for if this is the case. Aquifer This aquifer parameter is initially qualitative and is related to the soil composition of the saturated zone at each grid

cell, but a quantitative assessment is made by the following steps: (i) use the geological logs with the assessment for soil composition, (ii) assign the recommended rates to each of the proportions, (iii) calculate average rated proportions for each well, and (iv) interpolate these values for the whole study area. Soil Published soil maps designate the soil cover at the surface ranging from highly permeable coarse to impermeable layers. Topography The topography parameter captures the information in topographic maps in terms of slope variations within the study area. The ASTER DEM data is suitable for deriving the values of this parameter, and the appropriate rating values are assigned to each grid, as recommended by Aller et al. (1987). Impact The assessment of the impact parameter is identical to that of the aquifer parameter, except that this parameter considers the unsaturated vadose zone. Conductivity The conductivity parameter is estimated by using the pumping test data based on k = T/b, where k is the


hydraulic conductivity of the aquifer (m/d), T is the transmissivity (m2/d), and b is the saturated thickness of the aquifer (m). Nitrate-N Measurements of nitrate-N is available at 65 numbers of wells within the study area (Kord and Asghari Moghaddam 2013). Notably, levels of nitrate-N are expressed either as Bnitrate-N or nitrogen^ (symbol NO3-N) or simply as nitrate (NO3), and both are measured in ppm or mg/L. NO3-N is converted to NO3 by multiplying the amount of NO3-N by 4.42. Based on the US EPA, the maximum specification of 10 mg/L is related to NO3-N, and therefore, that of NO3 is 45 mg/L. Pollution The decline of groundwater level in the last 25 years signifies the introduction of pumpage wells and modern agricultural methods and thereby the risk of pollution from agricultural and industrial sources. Although the Ardabil Regional Water Authority monitors the water quality of Ardabil aquifer based on major ion concentration 2þ − 2− Ca ; Mg2þ ; Naþ ; Kþ ; Cl− ; SO2− 4 ; HCO4 ; CO3 , further data were obtained by Kord and Asghari Moghaddam (2013) including concentrations for minor and trace elements at this aquifer. They indicate that NO3-N is the main concentration, which e xceeds its World Health Organization’s (WHO 2009) allowable limits in this aquifer, and this study uses nitrate-N to condition the estimated vulnerability indices. Dataset processing The study area is divided into 93,965 cells. In the set of seven raster layers, prepared as thematic hydrogeological GIS layers, which comprise seven DRASTIC values for each grid cell, each cell value is classified and assigned with appropriate rating values and weighted as per DRASTIC standards (Aller et al. 1987). An additional thematic layer was prepared related to the distribution of NO3-N values, which were derived using measured values within a sample of wells of the aquifer at the study area. The preparation of these data layers is outlined below. The investigation used the best available information at that time. Depth Surface elevations were obtained from the ASTER DEM data and water table levels from the readings at 55 observation wells within the study area. The water table values were generated as a contour map by using ordinary kriging interpolation method. The depth to water table ranges from 0.8 to 49.5 m in the study area (Fig. 4a). This map was reclassified into seven classes as per recommendations for the DRASTIC framework. Recharge The aquifer in Ardabil plain is unconfined, and pumping is practiced in the aquifer. The relationship for recharge given by Scanlon et al. (2002) is modified by adding

the pumped water volume in a year. The modified annual net recharge was calculated as Δh R ¼ Sy pumpage ð8Þ Δt where R is the net recharge, Sy is the specific yield, Δh is the change in water level, and Δt is the time period. Aquifer Using the geological logs from 39 available wells, the aquifer parameters were assessed for the study area, with the soil composition of gravel, sand, silt, and clay with the recommended rates ranging from 2 to 8. The outcome of this study is shown in Fig. 4c. Soil The soil parameter of the study area was obtained from the soil cover map of Ardabil plain, published by the Ardabil Regional Water Authority (2014) with the designated soil cover at the surface ranging from highly permeable coarse soil media associated with high rates to a fine soil cover associated with low ratings (ranging from 1 to 8 according to their permeability). The output of the assessment is shown in Fig. 4d. Topography The ASTER DEM data were used with 28 m spatial resolution to estimate the slope over the study area, according to which slope varies from 0 to 19%. The value of the parameter (notably the slope value in the most of Ardabil plain is less than 2%) with a rating value of 10 was assigned. The output is shown in Fig. 4e. Impact Following an identical procedure for the aquifer parameter, the impact parameter was assessed with the output shown in Fig. 4f. Conductivity The conductivity parameter was estimated by using the pumping test data from the analysis of 50 pumping tests and estimating their transmissivity values with the hydraulic conductivity varying between 0.25 and 13.6 m/d. The rates were assigned as per recommended values, and the output is shown in Fig. 4g. Nitrate-N Measurements of nitrate-N are available at 65 numbers of wells within the study area (Fig. 5). Preliminary data processing Potential vulnerability index values are calculated for each and all of the cells using the framework given by Aller et al. (1987) or alternatively by the models as presented through training and testing phases. The data are prepared for model fitting by randomly dividing the dataset into training and testing datasets in the proportion of 80 and 20%, respectively. The vulnerability indices calculated for each grid cell during the



Fig. 4

DRASTIC parameters. a Depth to water. b Net recharge. c Aquifer media. d Soil media. e Topography. f Impact of the vadose zone. g Hydraulic conductivity

training and testing phases were conditioned by the NO3-N data, and those for the testing phase were used to assess the performances of the models. In this research, RMSE, the determination coefficient (R2), and the Pearson’s correlation coefficient (r) are used to evaluate the performance of the DRASTIC framework. RMSE represents the discrepancy between observed and calculated values. R2 and r represent the efficiency of the model. Best model predictions would render the lowest RMSE values but R2 and r values closer to 1. Performance measures are quantified in terms of (i) RMSE and R2, using CVI values based on the DRASTIC vulnerability index (VI) and modeled CVI values ðCVIÞ and (ii) the Pearson’s correlation coefficient r to estimate the correlation between NO3-N and VI or CVI. The modeled values comprise SFL, MFL, LFL, and SCFL models. This study is presented in terms of NO3-N, and Fig. 5 depicts its spatial distribution in the study area based on the inverse distance weighting (IDW) of interpolation. The CVI is calculated by Eq. (9) from DRASTIC vulnerability and NO3-N concentration values. CVI ¼

VImax ðNO3 ‐NÞi ðNO3 ‐NÞmax

Fig. 5 Distribution of NO3-N concentration of study area

ð9Þ

where VImax is the maximum vulnerability calculated from DRASTIC framework, (NO3-N)i is the nitrate concentration, and (NO3-N)max is the maximum nitrate concentration.

Results The general DRASTIC parameter layers are organized using appropriate GIS tools. The seven thematic layers are prepared as seven raster layers. Each layer is assigned with ratings and weighted according to general DRASTIC standards (Aller et al. 1987). Figure 4 shows the parameters maps used to obtain DRASTIC vulnerability indices. General DRASTIC The DRASTIC index was calculated based on Eq. (1). The calculated DRASTIC VI ranges from 82 to 151 for the whole plain (Fig. 6a). Its vulnerability is divided into two classes, 1ow vulnerability (82–120) and medium vulnerability (121–151) and these cover 44.4% and 55.6% of Ardabil plain, respectively, as per classification by Aller et al. (1987). This needs to be validated, but due to the absence of a theoretical basis for the DRASTIC framework, the derived values are conditioned using the spatial distribution of nitrate-N concentrations as per Eq. (9). The results show that there is not high correlation between the VI values and NO3-N values (Fig. 6a), and in fact,


Fig. 6 The vulnerability maps using different methods. a DRASTIC framework. b SFL. c MFL. d LFL. e SCFL


this is as low as 0.4. This correlation value signifies the scope for improvements; else, decisions made on such low values can be questionable. This low value of correlation is also indicative of sharp interfaces, possibly due to poor extraction of information from the underlying data. This is improved by fuzzy logic models (SFL, MFL, and LFL) and SCFL model as follows.

LFL model except the fuzzy implication. The MFL model adopted the Band^ operator for the fuzzy implication, instead of the product operator for LFL. The RMSE, R2, and r of MFL and LFL models in training and testing phases have been presented in Table 2. The output from these models are shown in Fig.6b–d and discussed in the next section.

FL modeling

SCFL model

In this study, three different FL models (SFL, MFL, and LFL) are applied for the estimation of groundwater intrinsic vulnerability. These models were constructed for the aquifer, in which the inputs to the three FL models comprise seven DRASTIC parameters and their outputs are the CVI values for each grid cell, respectively.

The SCFL method (Eqs. (5), (6), and (7)), as schematized in Fig. 1, was constructed to determine the overall prediction of groundwater vulnerability, by integrating the results of predicted data from SFL, MFL, and LFL. A simple ANN model is used to integrate these models. The MLP-LM structure is employed for the ANN model, which has three neurons for the input layer and one neuron for the output layer. The number of neurons for the single hidden layer is 4. The transfer function for the hidden layer in all parts is tansig and for the output layer is purelin. The LM algorithm was used to optimize weights and biases in the ANN model, which required 140 epochs. After training, the SCFL model is validated with the 65 data samples. The model performance of SCFL is presented in Table 2. The special distribution of CVI values for this model is displayed in Fig. 6e.

SFL The first step in SFL model construction is data clustering and determination of the number of rules. Using the SC method, the optimum clustering radius was searched by performing the clustering process by gradually increasing the clustering radius from 0 to 1 (with 0.1 intervals). The particular model with the lowest RMSE was selected. The value of 0.6 was chosen as the clustering radius based on the lowest RMSE (a value of 7.67). This, in turn, generated four fuzzy if-then rules. Thus, the SFL model was established by four Gaussian membership functions (clusters) for input and four linear membership functions for the output data resulting in four rules. Table 2 presents the statistics for the performance of the SFL model, the coefficient of determination (R2), and the correlation coefficient (r) for both the training and test stages. MFL and LFL For MFL and LFL models, a FCM clustering method was used for the extraction of clusters and fuzzy if-then rules. Results show that the best performance is achieved when the number of clusters for the input and output of MFL and LFL models is 28 (RMSE = 6.7 and RMSE = 6.6, respectively). The MFL model construction processes are the same as

Table 2 Fitting performance of the FL and SCFL models in the training and testing phases Model

RMSE

Training R2

r

RMSE

Testing R2

r DRASTIC 0.4 6 0.84 0.91 8.4 0.6 0.77 SFL 6.7 0.8 0.89 8.0 0.68 0.79 MFL 6.6 0.8 0.89 11.2 0.56 0.67 LFL 2.9 0.9 0.94 4.1 0.86 0.92 SCFL Color Code Rank 1 Rank 2 Rank 3 Note: Exceptions on Model Ranking: Ranking for the training phases: SCFL, SFL, LFL and MFL Ranking for the testing phase: SCFL, MFL, SFL and LFL

Overview of model performances Individual model performances based on overall performance measures are given in Table 2, which confirms the expectations that their values deteriorate from the training to the testing phase for the FL models. Model performances are ranked as SFL, LFL, and MFL for the training phases, and for the testing phase, they are ranked as MFL, SFL, and LFL. This mixed fortune in their performance seems a feature also associated with each of the performance measures for the three FL models in a background where the differences between individual performance measures are by and large marginal. The results of the three single FL models are compared with those of SCFL in Table 2. It is evident that SCFL improves significantly on the performance of individual FL models both for the training and testing phases. Although the performance measures deteriorate from the training to the testing phase, its performance rank remains consistent. Figure 6a–e shows the vulnerability maps, which displays the map by the original DRASTIC framework; those by SFL, MFL, LFL, and SCFL; and measured nitrate-N values depicted by indicative graduated circle symbols.

Discussion The results of different modeling techniques are compared using the modeled vulnerability index values by employing correlation index (CI). This approach comprises (i) grouping


the NO3-N concentration values into four categories comprising very low, low, moderate, and high; (ii) categorizing the modeled vulnerability values into very low, low, moderate, and high bands; (iii) assigning B4^ to a given model performance at an observation well, if the difference in the categories of NO3-N concentration and vulnerability values is 0 but assigning scores of 3, 2, or 1 when the differences are 1, 2, or 3, respectively; and (iv) adding the scores for each model. The above is illustrated by an example. To obtain the CI values for the prediction of the LFL model, 28 wells had the same vulnerability and NO3-N concentration categories; 21 wells had a difference of 1 in the categories; 4 wells, a difference of 2; and 1 well, a difference of 3 in the categories. The multipliers for these four groups of wells are 4, 3, 2, and 1, respectively. Its CI is calculated as 28 × 4 + 21 × 3 + 4 × 2 + 1 × 1 = 184. Table 3 presents the CI values for DRASTIC, SFL, MFL, LFL, and SCFL models, according to which the SCFL model has highest CI, but the SFL, MFL, and LFL models have practically similar performances, and these perform better than the DRASTIC framework. Attention is drawn to Table 2, which provides evidence that (i) the FL models produce slightly varying results in terms of their RMSE, (ii) the variations in predicted values are suggestive of inherent synergies, and (iii) the identification of the best-performing individual model is problematic as there is a mixed fortune from the training to testing phases. Significant improvement in performance measures by SCFL and its consistency from the training to testing phases is an indication of its ability to exploit the synergy in the three FL models, and this is evidenced by the results in this paper and by those reported by Fijani et al. (2013) and Nadiri et al. (2017). The methodology underlying the DRASTIC framework may be characterized as a heuristic approach, and as such, it is not amenable to calibration and validation. There is no right or wrong values for vulnerability index values derived by the DRASTIC framework. Thus, modeled vulnerability index Table 3 Correlation between vulnerability indices by DRASTIC, SFL, MFL, LFL, and SCFL and categories of nitrate-N at observation wells (unit = number of wells) Model 1. DRASTIC 2. SFL

3. MFL

4. LFL

5. SCFL Color Code

Vulnerability Category Low Moderate Very low Low Moderate Very low Low Moderate High Very low Low Moderate Very low Low Moderate Rank 1

Nitrate-N Concentration Categories High Moderate Low Very low 1 2 2 10 0 9 13 17 0 7 15 26 1 3 0 1 0 1 0 0 0 4 15 27 0 6 0 0 0 0 0 0 1 1 0 0 1 4 15 27 0 6 0 0 0 1 0 0 0 3 8 26 1 7 7 1 0 1 0 0 Rank 2 Rank 3

CI 155 181

186

184

192

values are conditioned, which is similar to the statistical Bayesian approaches conditioning preliminary values by newly emerging set of sample values. In the same way, the modeled vulnerability indices may be regarded as preliminary values and are then conditioned using values based on the NO3-N values. These results show that 50, 38, and 12% of the study area are at very low, low, and moderate risk, respectively. Vulnerability values higher than 160 are regarded high but were not encountered in this study. The results reported may be regarded as the baseline condition for now, and their updates in the future can serve as an indication of impacts of current land use practices. Figure 4a provides an insight into the weakness of the DRASTIC framework in terms of the results subject to sharp interfaces with a correlation coefficient of 0.4, which is estimated using the values directly from the framework and measured NO3-N values (see BPreliminary data processing^ section). As decisions based on such a low value of correlation coefficient are not defensible, a case is made for the application of AI techniques and hence the results in Fig. 5b–e. These show that the FL-based individual techniques (SFL, MFL, and LFL) are seemingly smooth, and the smoothness of CVI values is more so for SCFL (see Fig. 5e). Evidently, the four FL-based techniques extract more information from the data than the framework alone. Thus, FL-based modeling results are more defensible. Since 1987, the DRASTIC framework has been transformed into a working tool in aquifer management practices but criticized for using a procedure based on constant weighting and rating without considering local conditions and uncertainties within the parameters. The treatment of this weakness is already underway using AI techniques, and the ground for applying fuzzy logic has already been set by previous research works through the FLT and FAHP techniques (Dixon 2005; Gemitzi et al. 2006; Nobre et al. 2007; Afshar et al. 2007; Mohammadi et al. 2009; Şener and Şener 2015). These techniques still do not resolve the subjectivity in any significant way, but the application of full FL techniques takes one step forward, as reported by Rezaei et al. (2013) and Dixon (2004), who use just one FL model. Multiple models were also reported by Fijani et al. (2013) and Nadiri et al. (2017) by applying supervised committee machine technique using various AI techniques. This paper fills the gaps identified earlier by a systematic investigation through the results as evidence for the proof of concept towards an improved practical tool on assessing groundwater vulnerability in the study area. It substantiates the capabilities of SFL, MFL, and LFL models by improving the DRASTIC framework in a background of imprecise data, and it exploits the synergy among these models by using SCFL multiple models. Further investigations are underway to study the impacts of different formulations of conditioning the modeled CVI values or taking on board more complex physical systems comprising unconfined and confined aquifers.


Conclusion The assessment of the intrinsic vulnerability of the aquifer in Ardabil plain is investigated by using the DRASTIC framework, for which there is no baseline estimate of vulnerability indices for comparison purposes and the data are noisy. The general DRASTIC framework comprises the following seven raster layers: Depth to water table (D), net Recharge (R), Aquifer media (A), Soil media (S), Topography or slope (T), Impact of the vadose zone (I), and hydraulic Conductivity (C) of the aquifer. The calculated DRASTIC index value ranges from 82 to 151 obtained for the aquifer in Ardabil plain, northwest Iran. These values were conditioned against measured nitrate-N values and served as the baseline for comparison purposes but without regarding them as correct values. The correlation between the vulnerability indices of the DRASTIC framework and measured nitrate-N values is 0.4, and this is low and not particularly defensible as the basis for environmental decision-making. To extract more information from the noisy available data, the following models were constructed: SFL, MFL, and LFL models. These models provide a similar acceptable efficiency but with different characteristic strengths and weaknesses. So, the feasibility of exploiting the synergy among better performing models was tested by developing the SCFL model overarched by ANNs to combine the selected FL models. This model achieved the lowest RMSE in estimating the conditioned vulnerability for the training and testing phases and improved the correlation between the vulnerability indices of the DRASTIC framework and measured nitrate-N values. These results are evidence for proof of concept on the application of supervised committee models for conditioned DRASTIC vulnerability indices. Although SCFL is applicable to other aquifer systems, further studies are underway by the authors to ensure that the developed methodology is a professional tool. Currently, the authors are exploring the applications to of SCFL to a more complex situation of confined and unconfined aquifers. Acknowledgements The authors acknowledge gratefully the provision of data by the Ardabil Regional Water Authority.

References Aller L, Bennett T, Lehr JH, Petty RJ, Hackett G (1987) DRASTIC: a standardized system for evaluating ground water pollution potential using hydrogeologic settings. EPA 600/2–87-035. U.S. Environmental Protection Agency, Ada, Oklahoma Anane M, Abidi B, Lachaal F, Limam A, Jellali S (2013) GIS-based DRASTIC, pesticide DRASTIC and the susceptibility index (SI): comparative study for evaluation of pollution potential in the Nabeul-Hammamet shallow aquifer, Tunisia. Hydrogeol J 21(3): 715–731

ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) Artificial neural network in hydrology. I: preliminary concepts. J Hydrol Eng 5:2(115):115–123. doi:10.1061/(ASCE)1084– 0699(2000) Asadi S, Hassan M, Nadiri A, Heather D (2014) Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification. Environ Sci Pollut Res (2014) 21:8847. doi:10.1007/s11356-014-2821-z Baghapour MA, Nobandegani AF, Talebbeydokhti N, Bagherzadeh S, Nadiri AA, Gharekhani M, Chitsazan N (2016) Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz plain, Iran. J Environ Health Sci Eng 2016 14:13 Bai LP, Wang YY, Meng FS (2012) Application of DRASTIC and extension theory in the groundwater vulnerability evaluation. J. Water Environ 26:381–391 Bárdossy A, Disse M (1993) Fuzzy rule-based models for infiltration. Water Resour Res 29(2):373–382 Bezdec JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York Bezdek KT, Hathaway R (1988) Optimally test for fixed points of the fuzzy c-mean algorithm. Pattern Recogn 21:651–663 Chen CH, Lin ZS (2006) A committee machine with empirical formulas for permeability prediction. J. Comput Geosci 32:485–496 Chen MS, Wang SW (1999) Fuzzy clustering analysis for optimizing fuzzy membership functions. Fuzzy Sets Syst 103(2):239–254 Chitsazan N, Nadiri AA, Tsai F, Moghaddam, A (2014) Bayesian artificial intelligence model averaging for hydraulic conductivity estimation J. Hydrol. Eng., 10.1061/(ASCE) HE.1943–5584.0000824, 520–532. Chiu S (1994) Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems 2:267–278 de Martonne E (1925) Trait’e de G’eographie Physique: 3 tomes, Paris Dixon B (2004) Prediction of groundwater vulnerability using integrated GIS-based neuro-fuzzy techniques. J Spatial Hydrology 4(2):1–38 Dixon B (2005) Groundwater vulnerability mapping: a GIS and fuzzy rule based integrated tool. Appl Geogr 25:327–347 Emberger L (1930) Sur une formule applicable en g’eographie botanique. Cah Herb Seanc Acad Sci 191:389–390 Fijani E, Nadiri AA, Asghari Moghaddam A, Tsai F, Dixon B (2013) Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh-Bonab plain aquifer Iran. J Hydrol l530:89–100 Gemitzi A, Petalas C, Tsihrintzis VA, Pisinaras V (2006) Assessment of groundwater vulnerability to pollution: a combination of GIS, fuzzy logic and decision making techniques. Environ Geol 49:653–673 Grande J, Andújar J, Aroba J, Beltrán R, de la Torre M, Cerón J, Gómez T (2010) Fuzzy modeling of the spatial evolution of the chemistry in the Tinto River (SW Spain). Water Resour Manag 24(12):3219– 3235 Huan H, Wang J, Teng Y (2012) Assessment and validation of groundwater vulnerability to nitrate based on a modified DRASTIC model: a case study in Jilin City of northeast China. Sci Total Environ 440: 14–23 Javadi S, Kavehkar N, Mohammadi K, Khodadi A, Kahawita K (2011) Calibration DRASTIC using field measurements, sensitivity analysis and statistical method to assess groundwater vulnerability. Water Int 36:719–732 Kadkhodaie-Ilkhchi A, Rezaee MR, Rahimpour-Bonab H, Chehrazi A (2009) Petrophysical data prediction from seismic attributes using committee fuzzy interference system. Computer and Geosciences 35:2314–2330 Kim YJ, Hamm S (1999) Assessment of the potential for groundwater contamination using DRASTIC/EGIS technique, Cheongju area, South Korea. Hydrogeol J 7(2):227–235

Environ Sci Pollut Res Kord M, Asghari Moghaddam A (2013) Spatial analysis of Ardabil plain aquifer potable groundwater using fuzzy logic. J. King Saud University – Science. 1–12 Kord M, Asghari Moghaddam A, Nakhaeei M (2013) Investigation of hydrogeological characteristics of Ardabil plain aquifer. ISESCO JOURNAL of Science and Technology 9(15):63–69 Labani MM, Kadkhodaie-Ilkhchi A, Salahshoor K (2010) Estimation of NMR log parameters from conventional well log data using a committee machine with intelligent systems: a case study from the Iranian part of the South Pars Gas Field. Persian Gulf Basin Journal of Petroleum Science and Engineering 72:175–185 Larsen PM (1980) Industrial application of fuzzy logic control. International Journal of Man-Machine Studies. 12:3–10 Lee KH (2004) First course on fuzzy, theory and applications. Springer, Berlin, 335p Li H, Philip CL, Huang HP (2001) Fuzzy neural intelligent systems: mathematical foundation and the applications in engineering. CRC Press, Inc., Boca Raton, FL Mamdani EH (1976) Advances in the linguistic synthesis of fuzzy controllers. International Journal of Man-Machine Studies. 8:669–678 Mamdani EH, Assilian S (1975) An experimental in linguistic synthesis with a fuzzy logic control. International Journal of Man-Machine Studies 7:1–13 Misstear BDR, Brown L, Daly D (2009) A methodology for making initial estimates of groundwater recharge from groundwater vulnerability mapping. Hydrogeol J 17(2):275–285 Mohammadi K, Niknam R, Majd VJ (2009) Aquifer vulnerability assessment using GIS and fuzzy system: a case study in Tehran-Karaj aquifer. Iran Environ Geol 58:437–446 Nadiri AA (2015) Application of artificial intelligence methods in geosciences and hydrology. OMICS International Publications. Nadiri AA, Fijani E, Tsai FT-C, Asghari Moghaddam AA (2013) Supervised committee machine with artificial intelligence for prediction of fluoride concentration. J Hydroinf 15(4):1474–1490 Nadiri AA, Gharekhani M, Khatibi R, Sadeghfam S, Asgari Moghaddam A (2017) Groundwater vulnerability indices conditioned by supervised intelligence committee machine (SICM). Sci Total Environ 574:691–706 (In press) Nadiri AA, Marwa H, Asadi S (2015) Supervised intelligence committee machine to evaluate field performance of photocatalytic asphalt pavement for ambient air purification. Transportation Research Record: Journal of the Transportation Research Board 2528:96–105 Naftaly U, Intrator N, Horn D (1997) Optimal ensemble averaging of neural networks. Comput Neural Syst 8:283–296 Neshat A, Pardhan B (2014) An integrated DRASTIC model using frequency ratio and two new hybrid methods for groundwater vulnerability assessment. Nat Hazards. doi:10.1007/s11069-014-1503-y

Newton SC, Pemmaraju S, Mitra S (1992) Adaptive fuzzy leader clustering of complex data sets in pattern recognition. IEEE Transactions on Neural Networks 5:794–800 Nourani V, Asgharimoghaddam A, Nadiri AA (2008b) Forecasting spatiotemporal water levels of Tabriz aquifer. Trends in Applied Sciences Research 3(4):319–329 Nourani V, Mogaddam AA, Nadiri AA (2008a) An ANN-based model for spatiotemporal groundwater level forecasting. Hydrol Process 22(26):5054–5066 Panagopoulos GP, Antonakos AK, Lambrakis NJ (2006) Optimization of the DRASTIC method for groundwater vulnerability assessment via the use of simple statistical method and GIS. Hydrogeol J 14(6): 894–911 Rahimzadeh F, Babakhani AR (1987) Geological map of Ardabil (1:250, 000). Geological Survey of Iran Rezaei F, Safavi HR, Ahmadi A (2013) Groundwater vulnerability assessment using fuzzy logic: a case study in the Zayandehrood aquifers, Iran. J Environmental Management 51:267–277 Sadeghfam S, Hassanzadeh Y, Nadiri AA, Zarghami M (2016) Localization of groundwater vulnerability assessment using catastrophe theory. Water Resour Manag 30(13):4585–4601 Scanlon B, Healy R, Cook P (2002) Choosing appropriate techniques for quantifying groundwater recharge. Hydrology Journal 10(1):18–39 Sener E, Davraz A (2013) Assessment of groundwater vulnerability based on a modified DRASTIC model, GIS and an analytic hierarchy process (AHP) method: the case of Egirdir Lake basin (Isparta, Turkey). J Hydrogeology 21:701–714 Sener E, Sener S, Davraz A (2009) Assessment of aquifer vulnerability based on GIS and DRASTIC methods: a case study of the SenirkentUluborlu Basin (Isparta, Turkey). Hydrogeol J 17:2023 Şener E, Şener Ş (2015) Evaluation of groundwater vulnerability to pollution using fuzzy analytic hierarchy process method. Environmental Earth Sciences 73:8405–8424 Su XS, Xu W, Du SH (2014a) Responses of groundwater vulnerability to artificial recharge under extreme weather conditions in Shijiazhuang City. China J Water Suppl: Res Technol–Aqua 63:224–238 Sugeno M (1985) Industrial application of fuzzy control. North-Holland, New York Tayfur G, Nadiri A, Moghaddam A (2014) Supervised intelligent committee machine method for hydraulic conductivity estimation. Water Resour Manag 28(4):1173–1184 USEPA (2009) National Primary Drinking Water Regulations. US Environmental Protection Agency. EPA816-F-09-004 WHO (2009). Guidelines for drinking-water quality. Word Health Organization. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353