RETRACTED CHAPTER: Simulation of Groundwater Quality: Case Study of the Limestone Chain of the Western Rif of Morocco

Groundwater is the main source for water provision in the arid and semi-arid areas such as Iran. The groundwater quality was simulated by using a hybrid model integrating a Self-Organizing Map (SOM) and geographic information system (GIS). SOM and GIS were used as pre-processing and post-processing tools in the Mazandaran Plain. Further, the Ground Water Quality Index (GWQI) and its effective factors were estimated by using digital maps and the secondary data. NeuroSolutions software was used for simulating the groundwater quality. To do this, a model was trained and optimized in the SOM and then the optimized model was tested. In the next step, the performance of SOM in groundwater quality simulation was confirmed (test stage, Rsqr=0.8, and MSE=0.008). Then, the digital maps of the SOM inputs were converted to raster format in GIS. In the last step, a raster layer was generated by combining the model input layers which comprised the model inputs values. The tested SOM was used to simulate GWQI in the sites without the secondary data of the groundwater quality. Finally, the groundwater quality map was generated by coupling the results of SOM estimations and GIS capabilities. The results revealed that the coupling of SOM and GIS has high performance in the simulation of the groundwater quality. According to the results, a limited area of the studied plain has groundwater resources with low quality (GWQI>0.04). Therefore, that will be a threat to the life of humans, animals, and vegetative species. Therefore, it is necessary to plan for managing the groundwater quality in the Mazandaran plain.

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.

The process of water quality testing is money/time-consuming, quite important and difficult stage for routine measurements. Therefore, use of models has become commonplace in simulating water quality. In this study, the coactive neuro-fuzzy inference system (CANFIS) was used to simulate groundwater quality. Further, geographic information system (GIS) was used as the pre-processor and post-processor tool to demonstrate spatial variation of groundwater quality. All important factors were quantified and groundwater quality index (GWQI) was developed. The proposed model was trained and validated by taking a case study of Mazandaran Plain located in northern part of Iran. The factors affecting groundwater quality were the input variables for the simulation, whereas GWQI index was the output. The developed model was validated to simulate groundwater quality. Network validation was performed via comparison between the estimated and actual GWQI values. In GIS, the study area was separated to raster format in the pixel dimensions of 1 km and also by incorporation of input data layers of the Fuzzy Network-CANFIS model; the geo-referenced layers of the effective factors in groundwater quality were earned. Therefore, numeric values of each pixel with geographical coordinates were entered to the Fuzzy Network-CANFIS model and thus simulation of groundwater quality was accessed in the study area. Finally, the simulated GWQI indices using the Fuzzy Network-CANFIS model were entered into GIS, and hence groundwater quality map (raster layer) based on the results of the network simulation was earned. The study’s results confirm the high efficiency of incorporation of neuro-fuzzy techniques and GIS. It is also worth noting that the general quality of the groundwater in the most studied plain is fairly low.

In the present study, multilayer perceptron (MLP) neural network and support vector regression (SVR) models were developed to assess the suitability of groundwater for drinking purposes in the northern Khartoum area, Sudan. The groundwater quality was evaluated by predicting the groundwater quality index (GWQI). GWQI is a statistical model that uses sub-indices and accumulation functions to reduce the dimensionality of groundwater quality data. In the first stage, GWQI was calculated using 11 physiochemical parameters collected from 20 groundwater wells. These parameters include pH, EC, TDS, TH, Cl−, SO4−2, NO3−, Ca+2, Mg+2, Na+, and HCO3−. The primary investigation confirmed that all parameters except for EC and NO3− are beyond the standard limits of the World Health Organization (WHO). The measured GWQI ranged from 21 to 396. As a result, groundwater samples were classified into three classes. The majority of the samples, roughly 75%, projected into the excellent water category; 20% were considered good water and 5% were classified as unsuitable. GWQI models are powerful tools in groundwater quality assessment; however, the computation is lengthy, time-consuming, and often associated with calculation errors. To overcome these limitations, this study applied artificial intelligence (AI) techniques to develop a reliable model for the prediction of GWQI by employing MLP neural network and SVR models. In this stage, the input data were the detected physiochemical parameters, and the output was the computed GWQI. The dataset was divided into two groups with a ratio of 80% to 20% for models training and validation. The predicted (AI) and actual (calculated GWQI) models were compared using four statistical criteria, namely, mean square error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Based on the obtained values of the performance measures, the results revealed the robustness and efficiency of MLP and SVR models in modeling GWQI. Consequently, groundwater quality in the north Khartoum area is evaluated as suitable for human consumption except for BH 18, where highly mineralized water is observed. The developed approach is advantageous in groundwater quality evaluation and is recommended to be incorporated in groundwater quality modeling.

The Muyira sector is one of ten sectors of the Nyanza district in the Amayaga region of Rwanda. Amayaga region is part of the country's drought-prone areas, with high groundwater dependence. Most inhabitants in the area depend on groundwater for drinking and domestic purposes. Therefore, a hydrogeochemical characterization and assessment of groundwater quality in the study area was carried out using a combined application of hydrochemical models, multivariate statistical techniques, and GIS-based ordinary kriging interpolation on seven (7) borehole water samples. This study aimed to determine the concentrations and spatial distribution of various ions, groundwater quality issues, and the geochemical processes contributing to groundwater chemistry. The abundance of major cations in the groundwater is in the order Na+ > Ca2+ > K+ > Mg2+, whereas that of the major anions varies in the order HCO3− > SO42− > Cl−. Ca-Mg-Na-HCO3 water type is common in the area, possibly due to the dissolution of magnesite and silicate minerals in the basement rocks. Also, results indicate weak acids (i.e., HCO3−) dominance over strong acids (i.e., SO42− and Cl−). Ion exchange reactions and magnesite and silicate minerals weathering primarily control the area's groundwater chemistry. The results of the Pollution Index for Groundwater (0.29-0.55) and Groundwater Quality Index (29.14-53.68) indicate groundwater in the area is suitable for drinking. The sodium percentage (36.88–78.20%, mean of 57.83%), magnesium ratios (13.90–94.66, mean of 35.70), and sodium adsorption ratio (4.63–16.92, mean of 11.78) suggests that groundwater in the study area is suitable for irrigation purposes.

In recent years, groundwater pollution has become increasingly a serious environmental problem throughout the world due to increasing dependency on it for various purposes. The Damodar Fan Delta is one of the agriculture-dominated areas in West Bengal especially for rice cultivation and it has a serious constraint regarding groundwater quantity and quality. The present study aims to evaluate the groundwater quality parameters and spatial variation of groundwater quality index (GWQI) for 2019 using the fuzzy analytic hierarchy process (FAHP) method. The 12 water quality parameters such as pH, TDS, iron (Fe−) and fluoride (F−), major anions (SO42−, Cl−, NO3−, and HCO3−), and cations (Na+, Ca2+, Mg2+, and K+) for the 29 sample wells of the study area were used for constructing the GWQI. This study used the FAHP method to define the weights of the different parameters for the GWQI. The results reveal that the bicarbonate content of 51% of sample wells exceeds the acceptable limit of drinking water, which is maximum in the study area. Furthermore, higher concentrations of TDS, pH, fluoride, chloride, calcium, magnesium, and sodium are found in few locations while nitrate and sulfate contents of all sample wells fall under the acceptable limits. The result shows that 13.79% of the samples are excellent, 68.97% of the samples are very good, 13.79% of the samples are poor, and 3.45% of the samples are very poor for drinking purposes. Moreover, it is observed that very poor quality water samples are located in the eastern part and the poor water wells are located in the northwestern and eastern part while excellent water quality wells are located in the western and central part of the study area. The understanding of the groundwater quality can help the policymakers for the proper management of water resources in the study area.

The study of groundwater quality for drinking and industrial usage is critically important for the conservation and sustainable management of groundwater resources. The main purpose of this study was to evaluate the drinking and industrial groundwater quality status of five blocks of Gurdaspur district, Punjab, India, falling along international boundary of India–Pakistan thus serving as transboundary aquifers, by investigating spatial distribution of drinking and industrial groundwater quality indices using GIS approach. To study the drinking water quality, Groundwater quality index (GWQI) method was applied. The spatial distribution map of GWQI showed that the groundwater quality was poor in the Dera Baba Nanak block as compared to the other blocks. Further, corrosion and scaling indices namely Langelier saturation index (LSI), Aggressive index (AI), Ryznar stability index (RSI), Puckorius index (PI) and Larson-Skold index (LS) were calculated with the help of water quality parameters of all the samples. LSI, AI and RSI indicated the scaling tendency of sampled groundwater while, the other two indices, PI and LS suggested the corrosive behaviour of majority of the sampled groundwater. The findings of GWQI and industrial indices indicated that the groundwater quality was by and large not suitable for drinking and industrial usage in some areas. Therefore, regular and appropriate monitoring of groundwater quality is suggested in the study area and particularly in the regions where groundwater quality issues are reported so as to sustainably manage the groundwater resources.

Assessing groundwater quality for drinking water supply using hybrid fuzzy-GIS-based water quality index

The so-called groundwater quality simulation is to study the spatial and temporal variation law of the concentration of a certain soluble substance (element or other component) when it is dispersed and transported by simulation method, to predict the instantaneous dynamics and expansion range of polluted groundwater, and to provide scientific basis for formulating reasonable and effective prevention measures. In recent years, the environmental hydrogeology survey results of some provinces, cities and regions in China show that the groundwater in most cities in China has been polluted to varying degrees. This situation directly affects industrial and agricultural production and people's health. However, in the past, environmental hydrogeology work was mostly limited to surface investigation, lacking quantitative and predictive evaluation research. Monitoring groundwater quality dynamics and forecasting the development trend of groundwater pollution is one of the main tasks of groundwater pollution investigation and research. Relevant information shows that some countries in the world have already carried out this research and established a predictive groundwater quality model. Among them, the United States has established 22 material transport models and 5 heat transport models; France has established 5 models of material transport and 4 models of heat transport. The following countries have only established material transport models: 7 in Israel; 2 in the UK; 2 in Canada; 1 in West Germany and 1 in Japan. Therefore, we believe that the study of groundwater quality simulation experiment, which is still blank in China's scientific field, has become an important subject that needs to be carried out urgently to ensure the four modernizations and people's health. The hydrogeological conditions and dynamic characteristics of groundwater resources have been comprehensively analyzed and studied. It points out that the continuous exploitation of groundwater resources in 2018 has led to the deterioration of groundwater quality, land degradation, underground, cracks and depletion of groundwater resources, and puts forward some preventive measures.

Groundwater quality is typically measured through water sampling and lab analysis. The field-based measurements are costly and time-consuming when applied over a large domain. In this study, we developed a machine learning-based framework to map groundwater quality in an unconfined aquifer in the north of Iran. Groundwater samples were provided from 248 monitoring wells across the region. The groundwater quality index (GWQI) in each well was measured and classified into four classes: very poor, poor, good, and excellent, according to their cut-off values. Factors affecting groundwater quality, including distance to industrial centers, distance to residential areas, population density, aquifer transmissivity, precipitation, evaporation, geology, and elevation, were identified and prepared in the GIS environment. Six machine learning classifiers, including extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), artificial neural networks (ANN), k-nearest neighbor (KNN), and Gaussian classifier model (GCM), were used to establish relationships between GWQI and its controlling factors. The algorithms were evaluated using the receiver operating characteristic curve (ROC) and statistical efficiencies (overall accuracy, precision, recall, and F-1 score). Accuracy assessment showed that ML algorithms provided high accuracy in predicting groundwater quality. However, RF was selected as the optimum model given its higher accuracy (overall accuracy, precision, and recall = 0.92; ROC = 0.95). The trained RF model was used to map GWQI classes across the entire region. Results showed that the poor GWQI class is dominant in the study area (covering 66% of the study area), followed by good (19% of the area), very poor (14% of the area), and excellent (< 1% of the area) classes. An area of very poor GWQI was observed in the north. Feature analysis indicated that the distance to industrial locations is the main factor affecting groundwater quality in the region. The study provides a cost-effective methodology in groundwater quality modeling that can be duplicated in other regions with similar hydrological and geological settings.

Pleistocene aquifer is exploited for many purposes, including irrigation, domestic, production, and livestock use in Phu My town, Ba Ria – Vung Tau province. Groundwater Quality Index (GWQI) method combined with Geographic Information System (GIS) foundation is applied to determine the spatial variation as well as the suitability of groundwater in the study area. Water quality parameters in this study include pH, TDS, total hardness, Cl-, F-, NH4+, NO3-, SO42-, Pb2+, and Fe2+ were selected for analyzing from 17 monitoring wells in dry and wet seasons in 2017. The results indicate that water quality parameters such as Cl-, F-, NH4+-N, Pb2+ và Fe2+ exceed the maximum allowable levels by National Technical Regulation on Groundwater Quality. The groundwater quality, according to GWQI analysis results, shows that indicate 88% and 94% of the monitoring wells are from “good” to “excellent” type in the dry and wet seasons, respectively. The number of wells that have water quality from “poor” to “water unsuitable for drinking purpose” varies between the dry and wet seasons. Corresponding with the GWQI map, it shows that the area with good quality groundwater accounts for 98% of the total study area (331.44 km2) in the dry season and 94.5% of the study area (319.58 km2) in the wet season.

Simulation of ground water quality for noyyal river basin of Coimbatore city, Tamilnadu using MODFLOW

A fuzzy-logic based decision-making approach for identification of groundwater quality based on groundwater quality indices

Investigating the influence of solar distillation on improving the groundwater quality index in the southern region of Riyadh, Saudi Arabia

The study assessed the impact of land-use types on the groundwater quality of the mid River Njoro catchment, Kenya. Groundwater samples were collected from eight boreholes between the period of October 2017 to February 2018 and analyzed for pH, temperature, electrical conductivity, dissolved oxygen, nitrate, ammonium, and total phosphorus. These parameters were used to calculate the Groundwater Quality Index (GQI) value of the study area. The concentration maps (“primary maps I”) were constructed using Kriging interpolation of ArcGIS software from the seven groundwater quality parameters. The “primary maps I” were standardized with the KEBS and WHO standards to the “primary maps II” for ease of integration into a GIS environment. The “primary maps II” were then rated and weighted using a polynomial function to generate “rank maps” before calculating the GQI using spatial analyst tools of ArcGIS software. The land use map was prepared from a high-resolution Google earth satellite imagery of 2015. The mean GQI values for the different land use polygons were calculated and compared using GIS techniques. The GQI ranged from 68.38 to 70.92, indicating a high groundwater quality of mid River Njoro catchment. The major land-use types identified include settlement area, forest cover, agricultural land and mixed area. The agricultural land dominated the study area, followed by settlement area, forest cover and finally mixed area. The mean GQI value in each land use type varied minimally and this could be because of the diffuse nature of the land use types of the study area. Settlement area had low GQI, followed by agricultural land, mixed area and the forest cover had the highest mean GQI value, which corresponds to good quality of groundwater. Even though the variation is insignificant in this particular study, it somehow indicates the adverse effects of different land use on the quality of groundwater.