Analysis Of Groundwater Quality Research Articles

Groundwater Quality Prediction and Analysis Using Machine Learning Models and Geospatial Technology

The most prominent source of drinking water is groundwater, followed by lakes and reservoirs. Hydrological parameters like temperature, dissolved oxygen, pH, conductivity, ORP, and turbidity often change due to waste dumping into natural drinking water sources, particularly in densely populated areas. As a result, the water quality must be tested before public consumption to ensure healthy living in society. This research collected water samples from 129 wells in the Kanchipuram district in Tamil Nadu, India. An efficient integrated machine-learning-based prediction model has been proposed and modeled to determine the groundwater quality index (GQI). Several machine learning models were used to predict the water’s quality, including the naïve Bayes model, the KNN classifier, and the XGBoost classifier. Water quality predictions in 2024 were made using a combination of classification algorithms and models based on long short-term memory (LSTM) neural networks. The projected water quality characteristics were analyzed using geographical information system (GIS) technology to better understand and visualize the results. The XGBoost classifier model outperforms prior findings in the literature, with an accuracy of roughly 94.6%. The classification and prediction model was validated using collected and tested current data samples from a selected well. The findings were accurate within the 5% error range, promoting sustainability.

Multivariate statistical and hydrogeochemical analysis of seasonal groundwater quality variations in coastal villages of Trivandrum district, south India

This study attempts a detailed assessment of the quality of groundwater in the coastal region of Trivandrum District, Kerala where groundwater is the main source of drinking water. Forty groundwater samples were collected during the pre-monsoon and post-monsoon periods. The collected samples were analyzed for physical properties such as electrical conductivity (EC), pH, total dissolved solids (TDS), and total hardness, along with chemical properties, including major cations (Ca2⁺, Mg2⁺, Na⁺, K⁺) and anions (Cl⁻, SO₄2⁻, HCO₃⁻, NO₃⁻). The analysis of groundwater quality reveals significant spatial and seasonal variations caused by both natural and manmade influences. Water Quality Index (WQI), hydrogeochemical plots, and Principal Component Analysis (PCA) were used to analyses the data. The results show that Vakkom, Kazhakottam, Veli-Attipara, and Pozhiyoor show significant deterioration, and areas such as Varkala, Ayroor, and Edava generally maintain good water quality. The Water Quality Index (WQI) assessment indicates that approximately 22.5% of the studied area falls under excellent quality, while 17.5% is classified as poor. The WHO standard and BSI standards were used to derive the WQI. Principal Component Analysis (PCA) identified electrical conductivity, total dissolved solids, and total hardness as the primary factors affecting groundwater quality, explaining 65.17% and 61.03% of the total variance in the pre-monsoon and post-monsoon periods, respectively. Hydrochemical plots collaborate these results, emphasize the influence of rock-water interactions as the main geochemical process, further compounded by pollution from agricultural runoff and urban development. These findings highlight the need for sustainable groundwater management strategies in coastal communities. Effective measures, including pollution mitigation, sustainable agricultural practice, proper waste management, and preservation of freshwater ecosystems, are essential for ensuring the sustainability of groundwater resources.

Enhancing sustainability through GIS mapping for groundwater quality analysis

Abstract The current study attempts to develop sustainability by utilizing Geographic Information System (GIS) technology by examining groundwater quality in Rajahmundry by dividing the region into 1 km × 1 km grids, resulting in 45 different areas. The grids are thoroughly dispersed, and groundwater samples have been collected from a variety of sources, including open wells, hand pumps, and bore wells, in each grid in order to assess water quality. The evaluation includes a physico-chemical parameter namely, pH, total alkalinity, TDS, total hardness, calcium hardness, magnesium hardness, chloride, nitrates, and sulphates. The purpose of this study is to create a map of the spatial distribution of these groundwater qualities using GIS in order to obtain a general understanding of the current state of the entire valorisation of groundwater resources. The current approach is novel in that it not only allows detecting the issues affecting water quality but also enables developing specific approaches for the sustainable management and protection of groundwater in Rajahmundry. Overall, through GIS mapping and conclusive water quality assessment, the study provides a significant input into the field of environmental science in terms of enhancing water sustainability practices.

Analysis of Groundwater Quality in Semi-arid Region of Eastern - South Rajasthan on the Basis of Water Quality Index (WQI)

Abstract: The present study was conducted to evaluate the groundwater quality for drinking purposes in selected locations of the Eastern- South Rajasthan of India. Total eight sights groundwater samples were collected during 2022 and the samples were analyzed for physicochemical parameters i.e., pH, total hardness, total dissolved solids, Nitrate, Sulphate, fluoride, Chloride, and alkalinity were calculated. The results of the study are illustrated with the help of descriptive statistics, diagrams, and the water quality index (WQI). The focus of this research was to assess the current state of groundwater quality and the spatial distribution of characteristics in order to determine the quality of drinkable water. The WQI was used to categorize the samples into categories based on its acceptability for consumption. It is observed that most of the samples fall in the water unsuitable for drinking purposes category and a few samples are in the very poor water category.

Analysis of groundwater and surface water quality using modelled water index and multivariate statistics in Kampala and Mbarara Districts, Uganda

The demand for domestic, public and industrial use of water increases with population growth. However, activities from humans and natural environment have resulted into decline in water quality. The aim of this study was to evaluate the quality of groundwater and surface water collected from Kampala and Mbarara districts of Uganda using water quality index and multivariate statistics. A total of 10 physicochemical parameters (pH, water temperature, chloride, sulphate, nitrate, electrical conductivity, total dissolved solids, total suspended solids, total solids, and phosphate) were measured monthly to evaluate the quality of water in the study districts. Results showed that sulphate, electrical conductivity and total dissolved solids were observed with significantly high values in surface water than the groundwater for both seasons. Among all the parameters measured, electrical conductivity was observed with the highest value (2129.67 ± 1.41 µS/cm) while nitrate concentration (0.57 ± 0.02 mg/L) was observed with the lowest during the wet season in surface water. In addition, the mean values of nitrate, electrical conductivity, total dissolved solids and sulphate detected in the groundwater and surface water were within the recommended limits, while phosphate and chloride were observed with mean concentrations above the regulatory limits in 50% of the samples. The correlation analysis revealed strong and positive association between electrical conductivity and total dissolved solids in groundwater and surface water for both seasons. Also, the chemical make-up of groundwater and surface water within the study districts revealed that the determination of water quality rests on two major indices (conductivity and anthropogenic-pollutants) based on the factor analysis. The two indices accounted for 0.619% in groundwater and over 0.70% in surface water, indicating more pollution in surface water. Moreover, the water quality index (WQI) values obtained ranged from 14.81 to 115.73 in dry season and 7.77 to 108.24 during the wet season. According to WQI classification, 42% of the samples collected fell within the “excellent” and “good” categories while others fell within “poor” and “unfit” categories (58%) during the dry season. Based on these findings, appropriate treatment methods, proper sanitation and waste management should be implemented in locations with critical water conditions. Also, frequent monitoring of groundwater and surface water quality by environmental protection agency is highly recommended.

Analysis of Groundwater Quality in Rural Homes in Southeastern Mexico: Comparison of Treatment Methods

Analysis of Groundwater Quality in Rural Homes in Southeastern Mexico: Comparison of Treatment Methods

Groundwater quality analysis in Hail region of northwest Saudi Arabia based on physicochemical investigation

The acquired groundwater data from seventeen wells were hydrochemically analyzed for Sulfate, Chloride, Ammonia, Nitrate, Nitrite, pH, EC (Electrical Conductivity), TDS (Total Dissolved Solids), total hardness, and total alkalinity concentrations and then compared to the national and international standards. The results range as follows: pH (6.65–8.26), EC (382–1214 μS·cm−1), Total hardness (72–310 ppm), TDS (258–775 ppm), Total alkalinity (54–138 ppm), Cl- (19–202 ppm), Ammonia (NH3) (0.0 to 1.5 mg/liter), Nitrate (NO3) (4.7 to 65.6 ppm), Nitrite (NO2) (0.007 to 0.05 ppm), and sulfate (SO4) (29–278 ppm). About 70.58 % of the study area’s groundwater samples have TDS levels below 500 mg/l, which are considered safe for drinking. The EC of the groundwater found that 100 % of the samples fall within the allowed level. Based on these results, the groundwater quality is suitable for drinking and irrigation purposes in Hail region. Hence, this research shows that using GIS and chemical analysis of groundwater could be useful tools for the decision-making process of local and international authorities and water resources management.

GIS based geostatistical modelling and trends analysis of groundwater quality for suitable uses in Dhaka division

Preserving the quality of groundwater has become Bangladesh’s primary challenge in recent years. This study explores temporal trend variations in groundwater quality on a broader scale across 18 stations within the Dhaka division over 35 years. The data set encompasses an analysis of 15 distinct water quality parameters. Modified Mann-Kendal, Sens Slope and Mann-Kendal tests were performed to determine the trend’s variation and slope. In addition, the spatial–temporal changes in the quality of groundwater are studied through Geographic Information System (GIS) mapping and Piper diagram was applied to identify the unique hydrochemical properties. This is the first study conducted on this area using various trends analysis and no in-depth study is available highlighting the trends analysis of groundwater quality on a larger magnitude. In contrast, the correlation matrix reveals a high association between Mg2+ and SO42−, Na+ and Cl− that affects salinity and overall hardness at the majority of sites. The Piper diagram also demonstrates that the groundwater in Madaripur Sadar has major salinity issues. The analysis reveals a distinctive dominance of bicarbonate (HCO3−) ions across all sampling stations, with (HCO3−) equivalent fractions consistently ranging from 0.70 to 0.99 which can cause a significant impact on groundwater uses. This extensive analysis of long-term groundwater quality trends in the Dhaka Division enables researchers to comprehend the overall transition of groundwater quality for hardness related complications in future. Moreover, it can be a baseline study considering the valuable implications and future steps for sustainable water resource management in this region.

Phreatic Groundwater Quality Analysis Based on Physical and Chemical Parameters in Kuta Raja Sub-District

Phreatic groundwater, discovered easily around communities in the Kuta Raja Sub-district, is sourced from the subsurface and stored in saturated areas, where it flows naturally through seepage or jets. Therefore, this study aimed to analyze the quality of phreatic groundwater using physical and chemical parameters. Color, taste, smell, and temperature were the physical parameters analyzed, while chemical parameters tested included electrical conductivity value, acidity (pH), and hardness level. To conduct the experiment, a total of 50 samples were obtained using a random sampling method. Physical parameters were evaluated using the observational method. Test for groundwater hardness level was conducted at the Aceh Health Office's Health & Medical Equipment Testing Laboratory. The pH was measured using a pH meter, while electrical conductivity value and phreatic groundwater temperature were tested using a water quality checker. The results showed that the physical quality of groundwater was in good condition. Approximately 64% of the samples had characteristics of yellow, tasteless, and odorless. The temperature value was in the medium category (30°C-35°C). Additionally, the electrical conductivity value of 84% was in the medium class (1,200–2,500) brackish groundwater category. It was important to acknowledge that a groundwater pH value of 6.5–8.5 based on field measurements was in the normal class. The hardness level (CaCO3) of phreatic groundwater was evaluated using the Titrimetric method based on SNI Number 06-6989.12-2004. The test was conducted on 6 groundwater samples in terms of water flow and variations in electrical conductivity values. Based on observations, 4 samples were categorized as "hard" samples, while 2 were classified as “very hard”.

Physicochemical Analysis of Groundwater Quality of Some Selected Villages of Tosham Region

To evaluate the groundwater quality of the Tosham area of the Bhiwani district in the Indian state of Haryana, a comprehensive study has been conducted. Eleven physicochemical parameters, including pH, electrical conductivity, total alkalinity, total hardness, total dissolved solids, calcium, magnesium, fluoride, chloride, nitrate, and sulphate, were analysed on 600 groundwater samples that were collected over the course of different seasons from ten selected villages. Various physicochemical parameters, including pH, total alkalinity, total hardness, total dissolved solids, calcium, magnesium, fluoride, chloride, nitrate, and sulphate, are found to be higher than the desirable limits suggested by the ICMR, according to analytical data of some groundwater samples from the study areas. Groundwater quality parameters show that some sampling stations groundwater samples are highly unfit for human consumption due to high levels of total alkalinity, total hardness, total dissolved solids, calcium, magnesium, fluoride, and chloride. As a result, appropriate recommendations for improving the groundwater quality have been made

Physicochemical Analysis of Groundwater Quality of Tosham Town

A systematic study has been carried out to assess the groundwater quality of Tosham town of Bhiwani district of Haryana state, India. 400 groundwater samples from ten selected stations were collected in the different seasons and assessed for 11 physicochemical parameters i.e. pH, electrical conductivity, total alkalinity, total hardness, total dissolved solids, calcium, magnesium, fluoride, chloride, nitrate and sulphate respectively. The analytical data of various physicochemical parameters of some groundwater samples of the study areas indicates that parameters such as pH, total alkalinity, total hardness, total dissolved solids, calcium, magnesium, fluoride, chloride, nitrate and sulphate are found to be in excess than the desirable limits recommended by ICMR. The groundwater quality parameters values indicates that groundwater samples of some sampling stations are quite unfit for drinking purpose because of high value of total hardness, total dissolved solids, calcium, magnesium, fluoride and chloride so that suitable suggestions were made to improve the groundwater quality.

Groundwater quality assessment using machine learning models: a comprehensive study on the industrial corridor of a semi-arid region.

Water plays a significant role in sustaining the lives of humans and other living organisms. Groundwater quality analysis has become inevitable, because of increased contamination of water resources and global warming. This study used machine learning (ML) models to predict the water quality index (WQI) and water quality classification (WQC). Forty groundwater samples were collected near the Ranipet industrial corridor, and the hydrogeochemistry and heavy metal contamination were analyzed. WQC prediction employed random forest (RF), gradient boosting (GB), decision tree (DT), and K-nearest neighbor (KNN) models, and WQI prediction used extreme gradient boosting (XGBoost), support vector regressor (SVR), RF, and multi-layer perceptron (MLP) models. The grid search method is used to evaluate the ML model by F1 score, accuracy, recall, precision, and Matthews correlation coefficient (MCC) for WQC and the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), and median absolute percentage error (MAPE) for WQI. The WQI results indicate that the groundwater quality of the study area is very poor and unsuitable for drinking or irrigation purposes. The performance metrics of the RF model excelled in predicting both WQC (accuracy = 97%) and WQI (R2 = 91.0%), outperforming other models and emphasizing ML's superiority in groundwater quality assessment. The findings suggest that ML models perform well and yield better accuracy than conventional techniques used in groundwater quality assessment studies.

Characterization of heavy metal contamination in groundwater of typical mining area in Hunan Province

Heavy metal pollution in mining areas is a major cause of groundwater contamination, characterized by high toxicity, difficult degradability, and easy accumulation, and the source of pollution is not easily identified. Relying on the results of groundwater quality analysis tests in a typical mining area, this paper uses the SPSS 18.0 statistical analysis model to analyze the statistical characteristics of different indicator factors in the antimony mining area. The conclusions play a crucial role in implementing health and safety measures for the mining area and its surrounding residents. The statistical study results show that Mn, Se, As, and Sb are closely related to human mining activities and are polluted to varying degrees; the principal component analysis model indicates that the upstream monitoring points 26#, 22#, and 25# in the mining area groundwater are less polluted. The five monitoring points with a comprehensive principal component F > 1 are all located within the range of the metal mine cluster, indicating that the groundwater in the mining area is particularly sensitive to the impact of anthropogenic mineral extraction. This research summarizes the hydrogeological and geochemical statistical characteristics of the groundwater in the mining area, providing a reference for groundwater pollution risk diagnosis, ecological restoration, and heavy metal pollution prevention and control in this and similar mining areas.

Hydrogeochemical and GIS Analysis of Groundwater Quality for Drinking and Irrigation Purposes in Kuzhithuraiyar Sub-Basin, Kanniyakumari District, Tamil Nadu, India

A hydrogeochemical analysis was conducted to find the suitability of the groundwater for drinking and irrigation purposes in the Kuzhithuraiyar sub-basin, Kanniyakumari district, Tamil Nadu, India. 48 groundwater samples were collected from the different locations of the study area during both the pre- and postmonsoon periods. This study assesses the groundwater quality for drinking and irrigation purposes based on the analytical results, water quality index, Wilcox plot, and irrigation parameters such as electrical conductance, Kelly’s ratio, sodium absorption ratio values, magnesium hazards, bicarbonate, and the residual sodium carbonate index. The pH values ranged from 8.5 to 6.5, suggesting generally acceptable conditions. TDS concentrations range from 24 to 1277 mg/L, whereas EC values range from 37.50 to 1996 μS/cm. It is observed from the collected samples that the pre-monsoon water samples TDS’ values were exceeded the recommended TDS limits than postmonsoon samples. Water quality indices indicate that 50% of premonsoon samples and 48% of postmonsoon samples are suitable for drinking. In total, 10% of samples are admissible to a doubtful category before and after the monsoon, 6% are good to permissible during the monsoon, and 84% are good to permissible. Kelly’s ratio shows that 56% and 48% of samples collected during the post- and premonsoon are suitable for irrigation, respectively, whereas the remaining samples are not. Due to magnesium hazards, 40% and 44% of pre- and postmonsoon samples are not suitable for irrigation. Overall, the postmonsoon samples exceeded the permissible TDS limit (1000 mg/L) by 10% and the premonsoon samples by 6. 13% of samples obtained after the monsoon and 19% collected before the monsoon have a potential salinity greater than three, indicating that these samples are unacceptable. The sustaining water quality and mitigating possible hazards in the Kuzhithuraiyar sub-basin require continuous monitoring and focused measures.

Space-temporal analysis of groundwater quality in three areas of the state of Yucatán, México, and its relationship with existing anthropogenic activity.

Groundwater in the Yucatan State is the only source of water. The karst aquifer in Yucatan is vulnerable to pollution. Anthropic activities in Yucatan, such as pig farming, are usually related to high wastewater discharges and water pollution. Administrative and logistical issues in developing on-site sampling to evaluate water quality are common in Mexico. The RENAMECA database provides official data related to groundwater quality. However, no analysis based on this database has been reported. A groundwater quality evaluation based on five reference pig farms and the effect of spatial and temporal anthropic activities in the study area was developed. Eighteen wells based on their location concerning the selected pig farms were studied. On-site sampling and laboratory analysis of the supply water and wastewater in the study case farm were done. Fecal coliforms (FC) values (maximum 2850 MPN [100mL] -1) in most cases for supply water wells exceeded the allowed limit by NOM-127-SAA1-2021. The year of monitoring was significant (P < 0.05) on FC concentrations. Population density and the proximity of wells to population centers affect negatively the presence of total dissolved solids (TDS) and total nitrogen (TN). TDS (maximum value 2620mg L -1) and phosphorus presence could be related to agricultural activities, human settlements, and local aquifer conditions. A local wastewater treatment issue is evident. Groundwater is not quality for consumption without treatment. Regarding the issues in on-site water monitoring, database analysis provides an approximation of the real situation of groundwater quality.

Successive bootstrapping deep learning approach and airborne EM-borehole data fusion to understand salt water in the Mississippi River Valley Alluvial Aquifer

Increasing demands from agriculture and urbanization have decreased groundwater level and increased salinity worldwide. Better aquifer characterization and soil salinity mapping are important for proactive groundwater management. Airborne electromagnetic (AEM) is a powerful tool for aquifer characterization and salinity delineation. However, AEM needs to be interpreted with caution before being used for groundwater quality analysis. This study introduces a framework that utilizes the AEM data for both lithologic modeling and salinity delineation. A resistivity-to-lithology (R2L) model is developed to interpret AEM resistivity to lithology based a depth-dependent multi-resistivity thresholds. Then, a cokriging method is used to integrate AEM data from two different EM systems to predict resistivity at the aquifer. Finally, a resistivity-to-chloride concentration (R2C) model utilizes the resistivity model to estimate chloride concentrations at sand facies. A deep learning artificial neural network (DL-ANN) model is introduced with a successive bootstrapping approach to estimate total dissolved solids first and then use it together with resistivity data to estimate chloride concentration. The methodology was applied to delineating salinity plumes in the Mississippi River Valley alluvial aquifer (MRVA). This study found that the salinity distribution in MRVA is highly correlated with the Jurassic salt basin, salt domes, faulting, seismicity, and river water quality. The result indicates salinity upconing due to excessive pumping.

Hydrogeochemical analysis of groundwater quality during the pre- monsoon season of Manipur, India

ABSTRACT The water quality index (WQI) and irrigational index for groundwater were studied in the northeastern section of the Manipur Valley in northeast India. Water samples were collected in 2022 during the pre-monsoon season. To compute the water quality index for drinking water, the basic chemical parameters of total hardness, pH, electrical conductivity, total dissolved solids, Calcium, Sodium, Iron, Magnesium, Manganese, Chlorine, and Hydrogen Carbonate were used. The assessment of bacteriological quality is also done, which is crucial for overall water quality evaluation, alongside physical and chemical analyze. For determining irrigation suitability, irrigational indices such as sodium absorption ratio, sodium percentage, and magnesium hazard were calculated. WQI, %Na+, Sodium Adsorption Ratio (SAR), magnesium hazard, permeability Index and total hardness indicate that most water samples are harmless for irrigation and drinking purpose. They have affirmative relationships indicating that these characteristics are interdependent. Approximately 25% of the Piedmont zone groundwater is found to be unfit for agricultural and drinking usage. The encrustation of gypsum, halite, and evaporation into the Disang shares accelerate the dissolution of ions in Piedmont water, resulting in quality degradation. According to Gibbs plots, Durov Scatter Plot, and Hill Piper Trilinear Diagram of water dominated the rock-weathering process, while hydrochemical facies progressed from the beginning to the intermediate stage of Chadha’s graphical representation illustrates the progression of hydrochemical processes in surface water. Therefore, proper integrated water resource management and development are required for effective water resource utilization, particularly around the Piedmont zone. In recent years, the quality of groundwater has caused a significant deal of alarm, and an increasing number of studies relating to it have been published. The geological, structural, and geomorphological features in the intermontane Imphal Valley in Manipur, India, were identified using SRTM DEM data by QGIS Software. This area has simple geology and structural elements, making it an appropriate site to evaluate the use of remote sensing and GIS tools in geological studies. To prevent a water crisis, groundwater resources must be managed sustainably. The current study also concentrated on a bibliometric examination of groundwater management and access to gauge the state of the field. A bibliometric examination of these papers may offer insight into current research and trends. We are the first to join Groundwater Quality Assessment with bibliometric analysis. This prediction, if taken into consideration strategically during the planning of preventive measures for groundwater quality can help to analyze future evaluation to a great extent.

Analysing the impact on groundwater quality using dynamic programming and vision transformer

The demand for sustainable water sources has intensified the focus on ground water harvesting, necessitating rigorous quality analysis for diverse applications. The existing methods for ground water quality analysis are briefly discussed, revealing a research gap in optimizing the sampling and testing process. To address this gap, we employ Dynamic Programming, which is utilized to streamline the sampling and testing process by selecting optimal locations and times. This study proposes a new methodology that combines Dynamic Programming (DP) and the Vision Transformer (ViT) model to evaluate the impact on ground water quality efficiently. This optimization not only enhances data comprehensiveness but also minimizes resource utilization, a crucial aspect in resource-constrained environments. The incorporation of the Vision Transformer model into the methodology brings a novel dimension to ground water quality analysis. ViT, a cutting-edge deep learning model for image analysis, is adapted to process visual data from ground water samples. This includes identifying contaminants, suspended solids, and microbial content, providing a more holistic understanding of ground water quality than traditional methods. Results from our integrated approach demonstrate its efficiency and accuracy in assessing ground water quality. It achieves a 10–15% boost in sampling optimization efficiency, 5–10% improvement in visual data accuracy, and 5–10% increase in precision. DP-ViT reduces resource utilization by 15–20% and consistently offers 5–10% higher data coverage.

Groundwater quality and potential analysis using geospatial techniques: The case of Ashanti Region in Ghana

The ecosystem and economy's reliance on clean water is influenced by various factors such as geology, topography, soil types, activities, and the presence of plants and animals. The Ghana Water Company is encountering difficulties in delivering water to consumers in the Ashanti Region due to the shortage of surface water resources, leading to water rationing in the area. Furthermore, poor waste disposal practices, illegal mining, use of fertilizers, and industrial activities have resulted in surface and groundwater source damage. Therefore, there is a need to implement a reliable, simple, and timely method to assess groundwater quality. This study aims to employ GIS and RS techniques to evaluate groundwater quality and potential in the Ashanti Region, Ghana. The Water Quality Index (WQI) was estimated using pH, Total Dissolve Solid (TDS), Chloride, Total Hardness (TH), Nitrate, Temperature, Turbidity, Iron, and Electrical Conductivity (EC). The study then used the WQI distribution to conduct a groundwater potential analysis to identify suitable areas for borehole placement. Digital thematic layers and maps were developed to expose the spatial distribution of water quality parameters, enabling the identification of groundwater pollution control and remedial measures. The study estimated the region's groundwater potential using an integrated GIS and Analytical Hierarchical Process (AHP) technique, grouping under excellent, good, fair, and poor potential. The WQI in the Ashanti Region ranged from 5.208 to 134.232, with 32.252% of the study area having an excellent WQI and 60.168% of the study area having a good WQI. Poor water quality covered 7.550% of the study area. The results showed that the GIS-based AHP approach accurately mapped the spatial distribution of WQI and Groundwater Potential Zones (GWPZ). This information is helpful to planners in water resource management in groundwater exploration and future planning. Policymakers and stakeholders must ensure that groundwater sources are protected from pollution.

Machine learning, Water Quality Index, and GIS-based analysis of groundwater quality

Abstract Water is essential for life, as it supports bodily functions, nourishes crops, and maintains ecosystems. Drinking water is crucial for maintaining good health and can also contribute to economic development by reducing healthcare costs and improving productivity. In this study, we employed five different machine learning algorithms – logistic regression (LR), decision tree classifier (DTC), extreme gradient boosting (XGB), random forest (RF), and K-nearest neighbors (KNN) – to analyze the dataset, and their prediction performance were evaluated using four metrics: accuracy, precision, recall, and F1 score. Physiochemical parameters of 30 groundwater samples were analyzed to determine the Water Quality Index (WQI) of Pano Aqil city, Pakistan. The samples were categorized into the following four classes based on their WQI values: excellent water, good water, poor water, and unfit for drinking. The WQI scores showed that only 43.33% of the samples were deemed acceptable for drinking, indicating that the majority (56.67%) were unsuitable. The findings suggest that the DTC and XGB algorithms outperform all other algorithms, achieving overall accuracies of 100% each. In contrast, RF, KNN, and LR exhibit overall accuracies of 88, 75, and 50%, respectively. Researchers seeking to enhance water quality using machine learning can benefit from the models described in this study for water quality prediction.