Groundwater Quality Research Articles

ASSESSING GROUNDWATER QUALITY IN NORTH JAKARTA: A STATISTICAL APPROACH

Aims: This study investigates the groundwater quality in North Jakarta. Methodology and results: The groundwater data from thirty-one sampling sites were analyzed for physical and chemical parameters such as total dissolved solids (TDS), pH, turbidity, nitrate (NO3), sulfate (SO4), chloride (Cl), manganese (Mn), fluoride (F), and iron (Fe). Principal Component Analysis (PCA) and Spearman’s correlation matrix were utilized to evaluate the contamination sources. Conclusion, significance, and impact study: The findings reveal significant variability in chloride and total dissolved solids (TDS) concentrations, while fluoride levels remained stable. Strong correlations were found between TDS and chloride, as well as, turbidity and iron, suggesting contamination from seawater intrusion and industrial activities. PCA identified four key components explaining 77% of the total variance. The first component, dominated by TDS, turbidity, iron, and chlorine, indicates ongoing seawater infiltration in the coastal aquifer. The second component, associated with turbidity, nitrate, and fluoride, indicates contamination from human activities. This study shows the value of multivariate statistical techniques like PCA, in assessing groundwater quality. The correlation matrix further confirms the influence of seawater on groundwater salinity and the presence of heavy metals, possibly due to soil disturbance. Moreover, there is an influence of geological and geographical factors, particularly the excessive groundwater extraction leading to seawater intrusion and groundwater quality deterioration.

Critical role of vegetation and human activity indicators in the prediction of shallow groundwater quality distribution in Jianghan Plain with LightGBM algorithm and SHAP analysis.

Critical role of vegetation and human activity indicators in the prediction of shallow groundwater quality distribution in Jianghan Plain with LightGBM algorithm and SHAP analysis.

Research on the traceability and treatment of nitrate pollution in groundwater: a comprehensive review.

The preservation of groundwater quality is essential for maintaining the integrity of the water ecological cycle. The preservation of groundwater quality is crucial for sustaining the integrity of the water ecological cycle. Nitrate (NO3-) has emerged as a pervasive contaminant in groundwater, attracting significant research attention due to its extensive distribution and the potential environmental consequences it poses. The primary sources of NO3- pollution include soil organic nitrogen, atmospheric nitrogen deposition, domestic sewage, industrial wastewater, landfill leachate, as well as organic and inorganic nitrogen fertilizers and manure. A comprehensive understanding of these sources is imperative for devising effective strategies to mitigate NO3- contamination. Technologies for tracing NO3--polluted groundwater include hydrochemical analysis, nitrogen and oxygen isotope techniques, microbial tracers, and numerical simulations. Quantitative isotope analysis frequently necessitates the application of mathematical models such as IsoSource, IsoError, IsoConc, MixSIR, SIAR, and MixSIAR to deduce the origins of pollution. This study provides a summary of the application scenarios, as well as the strengths and limitations of these models. In terms of remediation, pump and treat and permeable reactive barrier are predominant technologies currently employed. These approaches are designed to remove or reduce NO3- concentrations in groundwater, thereby restoring its quality. The study offers a systematic examination of NO3- pollution, encompassing its origins, detection methodologies, andremediation approaches, highlighting the role of numerical simulations and integrating multidisciplinary knowledge. Additionally, this review delves into technological advancements and future trends concerning the detection and treatment of NO3- pollution in groundwater. It proposes methods to control the spread of pollution and acts as a guide for identifying and preventing pollution sources.

GeaGrow: a mobile tool for soil nutrient prediction and fertilizer optimization using artificial neural networks

IntroductionMost farmers in Nigeria lack knowledge of their farmland’s nutrient content, often relying on intuition for crop cultivation. Even when aware, they struggle to interpret soil information, leading to improper fertilizer application, which can degrade soil and ground water quality. Traditional soil nutrient analysis requires field sample collection and laboratory analysis; a tedious and time-consuming process. Digital Soil Mapping (DSM) leverages Machine Learning (ML) to create detailed soil maps, helping mitigate nutrient depletion. Despite its growing use, existing DSM-based ML methods face challenges in prediction accuracy and data representation.AimThis study presents GeaGrow, an innovative mobile app that enhances agricultural productivity by predicting soil properties and providing tailored fertilizer recommendations for yam, maize, cassava, upland rice, and lowland rice in southwest Nigeria using Artificial Neural Networks (ANN).Materials and methodsThe presented method involved the collection of soil samples from six states in southwest Nigeria which were analysed in the laboratory to compile the primary dataset mapped to the coordinates. A secondary dataset was compiled using iSDAsoil’s API for data augmentation and validation. The two sets of data were pre-processed and normalized using Python, and an ANN was employed to predict soil properties such as NPK, Organic Carbon, Soil Textural Composition and pH levels through regressive analysis while building a composite model for Soil Texture Classification based on the predicted soil composition. The model’s performance yielded a Mean Absolute Error (MAE) of 1.9750 for NPK and Organic Carbon prediction, 3.5461 for Soil Textural Composition prediction, and 0.1029 for pH prediction. For the classification of the soil texture, the results showed a high accuracy value of 99.9585%.ResultsThe results highlight the effectiveness of combining soil texture with water retention, NPK, and Organic Carbon to predict pH and optimize fertilizer application. The GeaGrow app provides farmers with accessible, location-based soil insights and personalized crop recommendations, marking a significant advancement in agricultural technology. The GeaGrow app also provides smallholder farmers with scalable, ease of adoption and use of the developed mobile application.ConclusionThis research demonstrates the potential of ML to transform soil nutrient management and improve crop yields, contributing to sustainable farming practices in Nigeria.

Analysis of fulfillment of domestic clean water needs of Muara Baru village, Air Kumbang subdistrict, Banyuasin regency using the rainwater harvesting (RWH) method

Rainwater Harvesting (RWH) is a method of collecting rainwater for alternative sources of clean water. The purpose of this study is to carry out RWH planning, including determining strategies and conducting feasibility analysis based on the quality and quantity of rainwater in Muara Baru Village, which is an area with critical water catchment conditions and is not yet served by a clean water pipeline network. The primary data collected included three types of houses, the number of different residents, and sampling the quality of rainwater and groundwater at the site. The selected water catchment area is the roof surface of the house. In terms of quantity, the potential supply of rainwater meets the needs for non-drinking water use. In terms of quality, rainwater samples have not met the quality standards for clean water based on Permenkes 32/2017. The rainwater collected will go through a disinfection and filtration process so that it meets the quality standards of clean water, and then it will be stored in the ground tank and roof tank to be distributed to each house. Thus, the planned RWH is able to provide an alternative source of clean water for the community in the Muara Baru Village area.

Hydrogeochemical characterization and potential geogenic source of fluoride contamination in Gariaband district of southern Chhattisgarh, India.

Fluoride contamination of groundwater exerts serious health concerns in developing countries like India, where surface water resources in general are not considered for domestic consumption. The present investigation attempts to evaluate the groundwater quality in terms of fluoride contamination in Deobhog and adjoining areas in Gariaband district, Chhattisgarh. A total of 68 groundwater samples were collected both in the pre- and post-monsoon period and analyzed for different physicochemical parameters like Temperature, pH, EC, TDS, Na+, K+, Ca2+, Mg2+, HCO3-, NO3-, Cl-, SO42-, and F-. The results reveal that the cation abundance is in the order of Ca2+ > Na+ > Mg2+ > K+, and that for anions, it is HCO3- > Cl- > SO42- > NO3- > F-. Hydrochemical characterization shows that the water is dominantly calcium bicarbonate type in general, irrespective of their fluoride concentration. The source of the dissolved constituents in water is identified to be due to rock-water interaction. Hydrochemical processes like ion exchange, along with carbonate and silicate weathering, are responsible for major cations and anions concentrations in fluoride-rich water. Changes in alkalinity of water accelerate the dissolution of fluoride from fluoride-bearing mineral phases, thus increasing the concentration in water. The highest concentration of fluoride is observed at Nangaldehi village (4.9mgL-1 post-and 3.83mgL-1 pre-monsoon), in the north-eastern part where dental fluorosis is noted in residents of the affected area. Lithounits like mafic granulite, Dongargarh granite, charnockite, etc. have a higher concentration of fluoride due to the presence of minerals such as apatite, biotite, hornblende, etc., confirmed through detailed petrographic studies. The fluoride from these sources also dispersed into the residual soil developed over the weathered rocks. The water in the area is observed to be undersaturated with fluoride, which signifies the area's proneness to more fluoride contamination in the near future. The contaminated aquifers are structurally controlled, and the shallow, unconfined aquifers are less contaminated with fluoride than those that are present at an elevation around 200m above mean sea level. This study provides first-hand information about the fluoride contamination in the area and established its geogenic origin. It also emphasizes to explore the possibility of alternate surface water sources for the affected areas.

Assessment of groundwater quality in Piryaloi, Pakistan: integrating GPI, SPI, and GIS for comprehensive analysis

ABSTRACT At present, the groundwater quality in various parts of the world is under serious threat. As a result, human health is highly affected. Thus, the present study analyzed and mapped the groundwater quality of Piryaloi, Pakistan using two widely applied indices, i.e., groundwater pollution index (GPI), synthetic pollution index (SPI), and GIS. Water samples were analyzed for various physicochemical parameters such as pH, turbidity, total dissolved solids (TDS), electrical conductivity (EC), chloride (Cl−), calcium (Ca2+), magnesium (Mg2+), total hardness (TH), carbonates (CO32−), bicarbonates (HCO31−), fluoride (F), and heavy metals such as sodium (Na), potassium (K), iron (Fe), manganese (Mn), nickel (Ni), zinc (Zn), molybdenum (Mo), boron (B), arsenic (As), cobalt (Co), copper (Cu), cadmium (Cd), palladium (Pd), and chromium (Cr). According to the GPI, 46.67, 40 and 13.33% of samples were excellent, good, and poor, respectively. Whereas, according to the SPI, 33.3, 40, 20, and 6.7% of samples were suitable, slightly polluted, moderately polluted, and highly polluted respectively. Despite the different inputs to the indices, the proportionate ranking showed a moderate correlation (R2 = 0.62) between the results of both indices. Interpolated maps also depicted that in some areas, groundwater is contaminated, and thus it should be treated well before drinking.

Assessment of the Impact of Auto-mechanic Workshop on Groundwater Quality in Central Business District, Oxbow Lake, Swali, Bayelsa State, Nigeria

Assessment of the Impact of Auto-mechanic Workshop on Groundwater Quality in Central Business District, Oxbow Lake, Swali, Bayelsa State, Nigeria

Hydrogeochemical processes regulating groundwater quality and its suitability for drinking purposes in the recent alluvial plain, Blue Nile Region, Sudan.

Groundwater is becoming a critical freshwater source in the Blue Nile Region, Sudan. However, overexploitation and contamination increasingly threaten its sustainability. This study evaluates the hydrogeochemical processes influencing groundwater and its suitability for drinking purposes in the recent alluvial plain. A total of 342 groundwater samples were collected from domestic wells and monitoring boreholes in 2022 for hydrochemical and multivariate statistical analysis. The results revealed that the groundwater is predominantly of HCO3-Ca·Mg and HCO3-Ca·Na facies, reflecting the influences of natural processes like mineral dissolution and ion exchange. The key factors affecting groundwater quality include rock-water interactions and anthropogenic influences. Most groundwater samples complied with the World Health Organization (WHO, WHO. (2022). Fourth edition incorporating the first and second addenda Guidelines for drinking-water quality) and Sudanese drinking water standards. However, F- concentrations exceeding permissible levels were detected in northeastern and southwestern boreholes, posing potential health risks. Elevated total dissolved solids (TDS) were linked to mineral dissolution and agricultural runoff. The Water Quality Index (WQI) categorized 39.42% of samples as excellent, 50.72% as good, and 9.57% as poor to very poor, with 0.29% unsuitable for drinking. Areas with low human activities exhibited better water quality, while compromised samples correlated with nitrate contamination, salinity, and poor waste management practices. This study provides a robust foundation for developing sustainable groundwater management strategies to mitigate contamination risks and secure safe drinking water access.

Seasonal variations in groundwater chemistry and quality and associated health risks from domestic wells and crucial constraints in the Pearl River Delta.

Groundwater quality is strongly compromised by polluted surface water recharge in rapidly developing urban regions. However, gaps still remain in the understanding of the critical contaminants controlling water quality and the health risks associated with groundwater consumption, particularly considering seasonal and climate changes in rainfall. This work focused on changes in groundwater quality and critical contaminants in domestic wells in the fast-developing Pearl River Delta (PRD) from the wet season to the dry season. The stable isotope δD and δ18O values indicated that groundwater was largely impacted by precipitation and has experienced strong evaporation. The groundwater generally exhibited oxidizing and slightly alkaline properties and was predominantly of the Ca-HCO3 type. Owing to the dominant water type of Ca-HCO3 and the high concentrations of Ca, concerns related to hard water arose, particularly during the wet season, which promotes the need for water softening before groundwater use. Although the heavy metal pollution index (HPI) and water quality index (WQI) indicated excellent or good water quality, 34% and 47% of the groundwater samples presented elevated concentrations of arsenic and nitrate, respectively, compared with the WHO recommended levels, and the contamination level was elevated during the dry season. To our knowledge, this study is the first to report the fluoride concentrations in the PRD groundwater, with median values below 0.5 mg L-1, underscoring the need for dietary fluoride supplementation. Health risk assessment confirmed the presence of both noncarcinogenic risks from arsenic and nitrate and cancer risk from arsenic in local populations resulting from groundwater consumption in the PRD region. This research emphasizes the importance of critical contaminants that constrain groundwater quality from different seasons with large variations in rainfall. Our work highlights the urgent need for the construction of adequate sanitation systems and for the control of agricultural nonpoint source pollution in rapidly urbanizing areas to safeguard both surface water and groundwater resources.

Spatial pattern of groundwater chemistry in a typical piedmont plain of Northern China driven by natural and anthropogenic forces.

Groundwater is crucial for human society's development in piedmont plains, yet its hydrogeochemistry often exhibits complex spatial distributions due to the interplay of nature and human factors. Ninety-two phreatic groundwater samples were collected from a typical piedmont plain in northern China and analyzed using self-organizing map combined with hydrogeochemical simulation, diagrams, and the entropy-weighted water quality index. Groundwater samples were categorized into four clusters, demonstrating a gradual hydrogeochemical facies evolution from HCO3-Ca to Cl-Mg·Ca and Cl-Na, along with an increase in NO3- content in the order of clusters IV, II, III, and I. Natural processes, including silicates weathering and reverse cation-exchange, establish the natural fundamental framework of groundwater chemistry, which is furtherly sculptured by agricultural substances input. Groundwater quality was predominantly excellent or good, with entropy-weighted water quality index (EWQI) values below 100 at over 92% of the sampling sites. Groundwater quality is relatively poorer in the upstream areas near the mountains and along the Hutuo River, where the stratum permeability is high, but improves in the downstream areas where permeability is lower. Agricultural land use and spatial variation in aquifer permeability are responsible for the observed spatial variations in groundwater chemistry. Agricultural contaminants warrant attention for the protection of groundwater quality in piedmont plains that with long-term agricultural activities, especially in the upstream areas near the mountains. This research improves the understanding of the spatial distribution and variation of groundwater chemistry in piedmont plains, and provides scientific guidance for related groundwater development and management.

TECHNOLOGICAL ADVANCES IN DRILLING EFFICIENCY AND SUSTAINABILITY IN THE PERMIAN BASIN

Recent advancements in drilling technologies have significantly improved operational efficiency and sustainability in the Permian Basin, one of the world’s most important oil and gas production hubs. Innovations such as the integration of digital systems, optimization of bottom hole assemblies (BHAs), and Managed Pressure Drilling (MPD) have contributed to faster well completions, reduced operational costs, and minimized environmental impact. A record-breaking well was completed using real-time data analysis and high-performance drilling equipment, demonstrating how automation and continuous monitoring optimize drilling parameters, enhance rate of penetration (ROP), and reduce non-productive time (NPT). The integration of predictive analytics and machine learning has further enabled dynamic adjustments, improving overall efficiency. The application of MPD in deep wells, particularly in the Wolfcamp D formation, has shown promising results, increasing ROP by 23.8% while lowering drilling fluid costs. Additionally, new approaches in directional drilling have reduced the need for multiple BHAs, streamlining operations and cutting expenses. Beyond drilling performance, water management remains a critical challenge in unconventional oil production. Studies have highlighted the need for sustainable water use, including the reuse of produced water to reduce freshwater consumption and environmental risks. Monitoring groundwater quality is essential to mitigate contamination risks from hydraulic fracturing. As demand for efficiency and sustainability grows, these innovations mark a significant step in unconventional well drilling. With continuous technological advancements, the Permian Basin is set to remain a leader in the oil and gas industry, establishing new benchmarks for future exploration and production.

Geostatistical analysis and multivariate assessment of groundwater quality

Appraising groundwater quality is critical for identifying health-related risks and establishing foundations for safe water supply management. Traditional statistical analyses of groundwater quality often overlook spatial information and autocorrelation, which are essential for comprehensive assessment. This study examines 76 water samples collected from Tehsil Jaranwala, analyzing 12 water quality parameters (WQPs) through multivariate statistical techniques and a non-parametric geostatistical kriging approach. We estimate the variogram parameters using three techniques: Ordinary Least Squares (OLS), Maximum Likelihood Estimation (MLE), and Restricted Maximum Likelihood (REML), selecting the method with the lowest mean square error for further analysis. These parameters are incorporated into kriging techniques to predict WQPs at unmeasured locations. Contour plots generated via the R software package geoR provide a visual representation of ungauged locations, highlighting the most critical areas. This case study revealed that eight significant WQPs, including Electrical Conductivity (EC), Sulfate, Total Dissolved Solids (TDS), Sodium, Chloride, Bicarbonate, Alkalinity, and Fluoride, were present in concentrations higher than the World Health Organization (WHO) permissible limits in certain regions of Jaranwala. Specifically, elevated contamination levels were observed in areas between the north longitude of 73.15°–73.20° and east latitude of 31.80°–32°. These findings underscore the potential health risks associated with groundwater consumption in this area and provide a foundation for informed policy decisions to mitigate these risks. These visualizations are intended to assist government agencies in making informed decisions and implementing necessary actions and policies.

Development of Transient Hydrodynamic and Hydrodispesive Models in Semi-Arid Environments

The hydrogeological study of the Rharb coastal basin, located in the semi-arid northwest region of Morocco, focuses on its two aquifers: the Plio-Quaternary aquifer characterized by high-quality water with salt concentrations ranging from 0.4 to 2 g/L, and the Upper Quaternary aquifer, with lower water quality (2 to 6 g/L). The deep aquifer is overexploited for agricultural purposes. This overexploitation has led to declining piezometric levels and the worsening of the oceanic intrusion phenomenon. The study aims to develop a numerical model for a period of 15 years, from 1992/93 to 2006/07 for monitoring groundwater quantity and quality, considering recharge, exploitation, and basin characteristics. A hydrodynamic model based on storage coefficient calibration identifies overexploitation for irrigation, increasing from 93 Mm3 in 1993 to 170 Mm3 in 2007, as the primary driver of declining water levels. A hydrodispersive model highlights higher salt concentrations in the shallow aquifer (up to 6 g/L), high nitrate concentrations due to human activity, and pinpoints areas of seawater intrusion approximately 500 m from the shoreline. Although the deeper aquifer remains relatively preserved, negative hydraulic balances from −15.4 Mm3 in 1993 to −36.6 Mm3 in 2007 indicate an impending critical period.

Assessment and Validation of Shallow Groundwater Vulnerability to Contamination Based on Fuzzy Logic and DRASTIC Method for Sustainable Groundwater Management in Southeast Hungary

A hierarchical fuzzy inference system (FIS) integrated with the DRASTIC model is applied in this study to enhance the assessment of shallow groundwater vulnerability in southeast Hungary, a region characterized by extensive agriculture and industrial growth. Traditional groundwater vulnerability models often struggle with parameter imprecision and uncertainty, affecting their reliability. To address these limitations, fuzzy logic was incorporated to refine the classification of vulnerability zones. The hierarchical FIS incorporates the seven DRASTIC parameters: depth to the water table, net recharge, aquifer media, soil media, topography, vadose zone impact, and hydraulic conductivity, assigning flexible ratings through fuzzy membership functions. The model classifies the fuzzy groundwater vulnerability index (FGWVI) into low, moderate, and high categories, revealing that 63.9% of the study area is highly susceptible to contamination, particularly in regions with shallow water tables and sandy soils. Validation was conducted using nitrate (NO3−) concentrations and electrical conductivity (EC) measurements from 46 agricultural wells to assess the correlation between predicted vulnerability zones and actual groundwater quality indicators. The correlation analysis revealed a moderately strong positive relationship between FGWVI and both NO3− (R2 = 0.4785) and EC (R2 = 0.528), supporting the model’s ability to identify high-risk contamination zones. This study highlights the effectiveness of the fuzzy-enhanced DRASTIC model in evaluating aquifer vulnerability and provides crucial insights to assist policymakers in identifying pollution sources and developing strategies to mitigate groundwater contamination, thereby alleviating the stress on this critical resource.

Groundwater Treatment Technologies for Ensuring Safe and Sustainable Water

Water is an essential resource that supports life, ecosystems, and human advancement. Among its various sources, groundwater holds a vital component of the hydrological cycle. Groundwater is an important resource that supports agricultural, industrial and domestic activities. However, its overexploitation and contamination have leads to declining water levels and quality of water leads to serious environmental challenges. Effective groundwater treatment ensures its sustainability and secure use. Treatment methods can be compartmentalizing into physical, chemical, and biological processes. Physical treatments including sedimentation, aeration, and filtration are used to remove suspended solids and dissolved gases. Chemical treatments including chlorination, coagulation, and ion exchange, are use to eliminating pathogens, heavy metals, and chemical pollutants. Biological processes including bioremediation and constructed wetlands, utilize microbial activity to destroy the organic pollutants and enhance water quality. Advanced treatment methods including reverse osmosis, advanced oxidation processes (AOPs), ultraviolet (UV) disinfection, ozonation, activated carbon filtration, membrane bioreactors (MBR), electrocoagulation, and photocatalytic water purification and Nano filtration gives higher efficiency in removing complex pollutants and recharging groundwater quality. The parameters like nitrates, fluorides, chlorides, total dissolved solids (TDS), and the presence of pathogens are vital for water quality evaluation. Increased nitrate levels indicate contamination from fertilizers and sewage. Small amount of Fluoride beneficial for dental health, but excessive, lead to fluorosis. The source of Chlorides from industrial activities causing taste and corrosion potential. Total Dissolved Solids affecting the water hardness. Pathogens leads to microbial contamination and affect the purity of water. It is essential to ensure the quality and safety of water for consumption and other uses.

Long-term Groundwater Quality Assessment and Management Strategies in Rapidly Urbanizing Regions: A Case Study of Tiruvallur

Long-term Groundwater Quality Assessment and Management Strategies in Rapidly Urbanizing Regions: A Case Study of Tiruvallur

Groundwater quality drivers in the drought-prone Thakurgaon District, Northwestern Bangladesh: An integrated fuzzy logic and statistical modeling approach.

Groundwater quality drivers in the drought-prone Thakurgaon District, Northwestern Bangladesh: An integrated fuzzy logic and statistical modeling approach.

Tracking the spatiotemporal evolution of groundwater chemistry in the Quaternary aquifer system of Debrecen area, Hungary: integration of classical and unsupervised learning methods

Monitoring changes in groundwater quality over time helps identify time-dependent factors influencing water safety and supports the development of effective management strategies. This study investigates the spatiotemporal evolution of groundwater chemistry in the Debrecen area, Hungary, from 2019 to 2024, using indexing, machine learning, and multivariate statistical techniques. These techniques include self-organizing maps (SOM), hierarchical cluster analysis (HCA), principal component analysis (PCA), and groundwater quality indexing (GWQI). The hydrochemical analysis revealed that Ca-Mg-HCO₃ is the dominant water type, with a temporal shift toward Na-HCO₃, reflecting increased salinity driven by ongoing rock-water interactions. SOM analysis showed a transition from heterogeneous to more uniform groundwater chemistry over time, suggesting greater stability in the aquifer system. Elevated salinity zones shifted spatially due to changes in groundwater recharge and flow patterns, while hardness intensified and expanded, indicating continued carbonate dissolution. HCA highlighted temporal shifts in groundwater composition, with six clusters identified in 2019 and five clusters in 2024, reflecting a gradual homogenization of water quality. PCA further confirmed this trend, linking it to underlying hydrochemical processes, such as water–rock interactions, with limited contributions from anthropogenic influences. The GWQI analysis indicated a general improvement in groundwater quality over time, with most regions meeting drinking water standards. However, specific areas exhibited signs of localized contamination, requiring targeted management. These findings underscore the importance of continuous groundwater quality monitoring to detect emerging trends and guide resource management. The study highlights the need for sustainable practices to safeguard water resources and ensure long-term water security in the Debrecen area.

Streamlining the monitoring and assessment of irrigation groundwater quality using machine learning techniques

Continuous evaluation of groundwater quality is vital for ensuring its long-term sustainability. However, traditional assessment methods for various purposes face challenges due to cost and time constraints. In this study, machine learning (ML) models, including Gaussian Process Regression (GPR), Decision Tree (DT), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were employed to predict five irrigation water quality (IWQ) indices using only physical parameters (electrical conductivity (EC) and pH) and site conditions (Elevation, depth to water table, and distance to river). A dataset of 246 groundwater samples from the Eocene aquifer in Minia, Egypt, was collected and analyzed to measure groundwater quality parameters. Five combinations of the input parameters were utilized to calculate IWQ indices: sodium adsorption ratio (SAR), sodium percentage (Na %), total hardness (TH), permeability index (PI), and Kell’s ratio (KR). ML models were developed to estimate IWQ parameters based solely on physical measurements and site conditions. The results revealed that GPR, DT, SVR, and ANN strongly predicted all IWQ parameters during training. The results demonstrated that GPR accurately predicted groundwater quality, followed by DT, SVR, and ANN. The best performance of the GPR model was achieved during the fourth combination, which includes EC and distance to the river. The evaluation of GPR through the fourth combination revealed the highest accuracy with a correlation coefficient of 0.97, 0.82, 0.96, 0.87, and 0.81 in predicting SAR, %Na, TH, PI, and KR. The study emphasizes the capacity of machine learning models to efficiently employ readily available and quantifiable field data to predict IWQ characteristics. Moreover, the research findings, contributing to the second goal of the Sustainable Development Goals (SDGs), “No Hunger,” and the sixth goal, “Clean water and sanitation,” have the potential to enhance agricultural productivity and water conservation.