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Water quality experiments are difficult, costly, and time-consuming. Therefore, different modeling methods can be used as an alternative for these experiments. To achieve the research objective, geospatial artificial intelligence approaches such as the self-organizing map (SOM), artificial neural network (ANN), and co-active neuro-fuzzy inference system (CANFIS) were used to simulate groundwater quality in the Mazandaran plain in the north of Iran. Geographical information system (GIS) techniques were used as a pre-processer and post-processer. Data from 85 drinking water wells was used as secondary data and were separated into two splits of (a) 70 percent for training (60% for training and 10% for cross-validation), and (b) 30 percent for the test stage. The groundwater quality index (GWQI) and the effective water quality factors (distance from industries, groundwater depth, and transmissivity of aquifer formations) were implemented as output and input variables, respectively. Statistical indices (i.e., R squared (R-sqr) and the mean squared error (MSE)) were utilized to compare the performance of three methods. The results demonstrate the high performance of the three methods in groundwater quality simulation. However, in the test stage, CANFIS (R-sqr = 0.89) had a higher performance than the SOM (R-sqr = 0.8) and ANN (R-sqr = 0.73) methods. The tested CANFIS model was used to estimate GWQI values on the area of the plain. Finally, the groundwater quality was mapped in a GIS environment associated with CANFIS simulation. The results can be used to manage groundwater quality as well as support and contribute to the sustainable development goal (SDG)-6, SDG-11, and SDG-13.

Groundwater is an important water source for agricultural irrigation in Penyang County. Some traditional methods such as irrigation coefficient, sodium adsorption ratio, total alkalinity, total salinity and total dissolved solids were employed to assess groundwater quality in this area. In addition, an improved technique for order preference by similarity to ideal solution model was applied for comprehensive assessment. The origin of major ions and groundwater hydrogeochemical evolution was also discussed. Groundwater in Penyang County contains relative concentrations of dominant constituents in the following order: Na +  > Ca 2+  > Mg 2+  > K + for cations and HCO 3 −  > SO 4 2−  > Cl −  > CO 3 2− for anions. Groundwater quality is largely excellent and/or good, suggesting general suitability for agricultural use. Calcite and dolomite are found saturated in groundwater and thus tend to precipitate out, while halite, fluorite and gypsum are unsaturated and will dissolve into groundwater during flow. Groundwater in the study area is weathering-dominated, and mineral weathering (carbonate and silicate minerals) and ion exchange are the most important factors controlling groundwater chemistry.

Groundwater is a vital source of water for domestic and agricultural activities in the Tefenni plain. Therefore, groundwater quality, seasonal variations and its suitability for drinking, irrigation and industrial usage were evaluated. In this study, 56 water samples were collected from springs, wells, and lake in dry and wet seasons. Ca–Mg–HCO₃, Mg–Ca–HCO₃, Na–CO₃–Cl, and Na–HCO₃–Cl water types are the dominant water types in the investigation area. Parameters, which are controlled to chemical variations of groundwater, were analyzed with R-mode factor and correlation analysis. According to R-mode factor analysis, total dissolved solids, Na, Cl, HCO₃, and NH₃are the most important parameters. In addition, Water Quality Index (WQI) was applied to suitability for drinking purpose and to investigation of groundwater quality. Quality of groundwaters are suitable for drinkable both dry and wet season in study area. In terms of the irrigation and industrial usage, generally groundwater is suitable in dry season but is not suitable in wet season. Groundwater chemistry is affected with water–rock interaction and densely agricultural activities.

Hydogrochemical investigation of groundwater resources of Paragraph district has been carried out to assess the solute acquisition processes and water quality for domestic and irrigation uses. Fifty-five groundwater samples were collected and analyzed for pH, electrical conductivity, total dissolved solids, hardness, major anions (F-, Cl-, NO3, HCO3-, SO42-) and cations (Ca2+, Mg2+, Na+, K+). Study results reveal that groundwater of the area is alkaline in nature and HCO3-, Cl-, Mg2+, Na+ and Ca2+ are the major contributing ions to the dissolved solids. The hydrogeochemical data suggest that weathering of rock forming minerals along with secondary contributions from agricultural and anthropogenic sources are mainly controlling the groundwater composition of Pratapgarh district. Alkaline earth metals (Ca2++Mg2+) exceed alkalis (Na++K+) and weak acid (HCO3-) dominate over strong acids (Cl-+SO42-) in majority of the groundwater samples. Ca-Mg-HCO3 and Ca-Mg-Cl-HCO3 are the dominant hydrogeochemical facies in the groundwater of the area. The computed saturation indices demonstrate oversaturated condition with respect to dolomite and calcite and undersaturated with gypsum and fluorite. A comparison of groundwater quality parameters in relation to specified limits for drinking water shows that concentrations of TDS, F-, NO3- and total hardness exceed the desirable limits in many water samples. Quality assessment for irrigation uses reveal that the groundwater is good for irrigation. However, values of salinity, sodium adsorption ratio (SAR), residual sodium carbonate (RSC), %Na and Kelley index are exceeding the prescribed limit at some sites, demanding adequate drainage and water management plan for the area. Copyright 2014 Geological Society of India

Multivariate statistical analysis, geostatistical techniques and structural equation modeling were used to determine the main factors and mechanisms controlling the spatial variation of groundwater quality in the Ain Azel plain, Algeria. Cluster analysis grouped the sampling wells into two statistically significant clusters based on similarities of groundwater quality characteristics. Principal component and factor analyses (PCA/ FA) revealed that two factors explained around 85 % of the total variance, which water-rock interaction and anthropogenic impact as the dominant factors affecting the groundwater quality. The distribution of factor score one represents high loading for EC, Ca, Mg, Na, K, and SO 4 in the western side and south eastern side of the plain, where water-rock interactions are dominate factors influence groundwater quality. Spatial distribution map of factor score 2 indicate that NO 3 , NO 2 , NH 4 , and COD show high concentration in central and southern side of the plain, where anthropogenic impact reduce groundwater quality. Further, one-way analysis of variance (one-way ANOVA) showed that the mean differences between cluster one and two show significantly differences for some water quality parameters including EC, Ca, Mg, Na, K, Cl, and SO 4 . Structural equation modeling (SEM) confirmed the finding of multivariate analysis. This study provides a new technique of confirming exploratory data analysis using SEM in groundwater quality.

The present work aims at assessing the impact of MSW on the groundwater quality around dumping yard site, located near the Sangamner city by water quality index (WQI) and its integration in geographical information system (GIS). Groundwater samples ( n  = 15) around the dumping yard were collected using Garmin GPS device in October 2013 and October 2014. Physico-chemical analysis of same samples was carried out for pH, EC, TDS, Na + , K + ,Ca 2+ , Mg 2+ , TH, Cl − , HCO 3 − , SO 4 2− and NO 3 − along with the heavy metals like Fe, Zn, Cd and Cr by using standard methods. Similarly, SAR, KRs, RSC and SSP were also calculated to know the groundwater quality into irrigation perspective. WQI for 15 samples were calculated using physico-chemical results/data of 12 parameters and its desirable limit of BIS standard. Generated WQI ( z ) for October 2013 and October 2014 were integrated with latitude ( y ) and longitude ( x ) values, collected using GPS during the field work. Integrated xyz data were then interpolated in Surfer-10 GIS software using inverse distance weight (IDW) method to estimate the groundwater quality of the study area. Study revealed that the groundwater quality around the dumping yard area does not confirm to drinking and domestic purposes as per the WQI and BIS standard. However, the groundwater quality is marginally suitable for irrigation as per SAR, KRs, RSC and SSP. The influence of leachate from MSW dumping site to surrounding groundwater is creating a serious concern and susceptible to potential health hazards. Thus, continuous monitoring of groundwater is desperately required in order to minimize the groundwater pollution for control the pollution-caused MSW.

Surface water and groundwater samples were collected from 20 locations, situated in the vicinity of the abandoned coal mine fields south of Bochum. The main objective of this research is to assess the environmental impacts of these mines on the surface water and groundwater quality as well as to determine the factors controlling these impacts. The water samples were collected from stream sources, groundwater, surface water and engineered channels during April 2011. Physicochemical parameters were measured during fieldwork. Water samples were analysed for major ions and aluminium, iron, manganese, ferrous iron, zinc and hydrogen sulphide. The hydrochemistry of the surface water and the groundwater of this area is characterized by near-neutral to alkaline conditions, represented by predominance of calcium bicarbonate and sometimes calcium sulphate water types. Hence, the surface water and the groundwater quality in this region is significantly affected by abandoned coal mines. These effects resulted from oxidation of iron disulphide minerals that release iron, sulphate and hydrogen. The presence of carbonate-rich materials, which contained within the landfilling materials, has led the releasing of calcium, magnesium and bicarbonate. These materials could be the main source responsible for raising the alkalinity of the affected water. The environmental hazard of the abandoned coal mines in this area is related to the high concentration of Fe especially in the groundwater that possess the highest Fe concentration compared to other water sources: the Fe is 18 times larger than the allowed value in drinking water. Significant spatial variations of the water pollution were noted in this study. For this reason, the environmental hazards of the abandoned coal mines in Germany should be considered at closure of coal mines in the near future. Otherwise, these mines will be sources of environmental threats unless all necessary measures are taken to reduce their impact.

The water quality index (WQI) is an important parameter for determining the drinking water quality for the end users. The study for the same has been carried on the groundwater by collecting 47 groundwater samples from 25 blocks of Greater Noida city, India. In order to develop WQI the samples were subjected to a comprehensive physicochemical and biological analysis of 11 parameters such as pH, calcium, magnesium, chloride, nitrate, sulphate, total dissolved solids, fluorides, bicarbonate, sodium and potassium. Geographical information system has been used to map the sampling area. The coordinates in terms of latitude and longitude of the sampling locations were recorded with the help of global positioning system. Piper plots and cation-anion correlation matrix were plotted from the values obtained by the analysis of various parameters. The WQI index for the same has been calculated and the values ranged from 53.69 to 267.85. The WQI values from present study indicate the very poor quality water in the area dominated by industrial and construction activities. Poor water quality has been observed in commercial zone of the study area. The analysis reveals the fact that the ground water of the Greater Noida needs a degree of treatment before consumption and needs to be protected from further contamination.

In this paper, a support vector machine (SVM) model was developed to predict nitrate concentration in groundwater of Arak plain, Iran. The model provided a tool for prediction of nitrate concentration using a set of easily measurable groundwater quality variables including water temperature, electrical conductivity, groundwater depth, total dissolved solids, dissolved oxygen, pH, land use, and season of the year as input variables. The data set comprised of 160 water samples representing 40 different wells monitored for 1 year. The associated parameters for the optimum SVM model were obtained using a combination of 4-fold cross-validation and grid search technique. The optimum model was used to predict nitrate concentration in Arak plain aquifer. The SVM model predicted nitrate concentration in training and test stage data sets with reasonably high correlation (0.92 and 0.87, respectively) with the measured values and low root mean squared errors of 0.086 and 0.111, respectively. Finally, the map of nitrate concentration in groundwater was prepared for all four seasons using the trained SVM model and a geographic information system (GIS) interpolation scheme and compared with the results with a physics-based (flow and contaminant) model. Overall, the results showed that SVM model could be used as a fast, reliable, and cost-effective method for assessment and predicting groundwater quality.

The objectives of this study are to analyze the underground water quality of Greater Noida region by water quality index. Nine physico-chemical parameters such as Calcium, Magnesium, Chloride, Sulphate, Total Hardness, Fluoride, Nitrate, Total Dissolved Solids, Alkalinity collected from 10 different locations since a period of 2015. In this study 90% water samples were found good quality and only 10% water samples falls under moderately poor category. The water quality index ranges from 16.49 to 64.65. Therefore there is a need of some treatment before usage and also required to protect that area from contamination.