Modified irrigation water quality index for efficient water quality management of micro-irrigation systems

The quality of groundwater resources in artisanal mining districts in Ghana is under threat due to pollution; rendering the resource unsafe for drinking and irrigation purposes. This makes the assessment of the quality of groundwater resources a relevant aspect of groundwater studies as it informs decision making and monitoring. This study adopts 3 Machine Learning (ML) models, Support Vector Regression (SVR), Gradient Boost Regression (GBR), and Artificial Neural Network (ANN), to evaluate a variety of irrigation water quality metrics such as Sodium Percentage (Na%), Soluble Sodium Percentage (SSP), Sodium Adsorption Ratio (SAR), Residual Sodium Carbonate (RSC), Permeability Index (PI), Pollution Index of Groundwater (PIG), Kelly’s Ratio (KR), and Magnesium Hazard (MH). 105 samples were collected from a mining area in Northern Ghana and analysed through traditional methods. The Irrigation Water Quality Indices (IWQIs) demonstrate that all water samples are suitable for use as irrigable water with the exception of MH, Na%, PI, and PIG which revealed that 69.52%, 8.57%, 29.52%, and 3.81% are inappropriate for irrigation. SVR, GBR and ANN were used to establish important factors that may influence IWQIs in the area. The measured data was used as independent variables, and the derived IWQIs, the dependent variables. The results revealed that ANN, GBR, and SVR are all viable options for the prediction of IWQIs, but GBR exhibited variable performance in some indices making it lack consistency and thus falls a bit short compared to ANN and SVR. SVR models overall performed best with SVR-RSC having the highest accuracy.

ABSTRACTIn this study, surface water quality was assessed on the basis of irrigation water quality indices and the irrigation water quality index (IWQI) via GIS. The study was carried out on the basis of analyses of samples collected in August (dry) and November (wet) 2023 from 12 designated points along the Yıldız River in Sivas. The sodium adsorption ratio (SAR), Kelly index (KI), sodium percentage (Na%), permeability index (PI), residual sodium carbonate (RSC), magnesium hazard (MH) indices and IWQI were calculated to determine the classification of irrigation water quality. Additionally, analyses of Ca2+, Cl−, Fe2+, K+, HCO3−, Mg2+, Mn, Na+, pH and SO42− were conducted on the samples. The spatial distributions of the calculated parameters were mapped via GIS, and irrigation water quality assessment was performed according to the US Salinity Diagram and irrigation water quality standards. The IWQI values ranged from 401 to 61 during the rainy season and from 42 to 67 during the dry season. In the rainy season, two surface water samples were classified as ‘poor (MR: moderate restriction, IWQI: 55–70)’ and nine as ‘very poor (HR: high restriction, IWQI: 40–55)’. In the dry season, three surface water samples were classified as ‘poor (MR: moderate restriction)’ and nine as ‘very poor (HR: high restriction)’. According to the US Salinity Diagram, the majority of surface water samples in both the rainy and dry seasons fell into categories C3S1 (high‐salinity hazard–low‐sodium hazard) and C2S1 (medium‐salinity hazard–low‐sodium hazard), respectively. The results highlight the effectiveness of these methodologies in evaluating surface water quality, assisting in the development of informed management strategies for sustainable water resource use in agricultural environments. The IWQI has proven to be a good tool for assessing the quality of irrigation water in the study area and managing water quality and can help decision makers manage water resources more effectively for sustainable agriculture.

Introduction: An appropriate water resources management and planning is necessary due to the scarcity of water resources and rapidly growing world population. In this regard, selecting appropriate methods for irrigation is one of the most important issues. Drip irrigation is a recent advanced irrigation method in which fertilizers can be efficiently applied along with irrigation water. Drip fertigation, however, can potentially cause clogging of emitters. Various factors such as clogging increase manufactures’ coefficient of variation and water temperature and pressure changes could alter emitter discharge and water distribution uniformity. The aim of this study is to evaluate the effect of fertigation on clogging of emitters and the performance of drip irrigation systems. Materials and Methods: This study was performed as a laboratory experiment at the University of Zabol. The experiment was done in the form of factorial in a completely randomized design with three replications in the hydraulics laboratory, the University of Zabol. The first factor was fertilizer type including: F0 (control), F1 (ammonium nitrate) and F2 (urea) and the second factor was the emitter types including one-nozzle on line (A), six-nozzles in line (B) and eight-nozzles on line (C). The tap water was used for irrigation. The system included 9 laterals, 3 m each with 18 emitters on each lateral. Fertilizer solution with known concentrations of 0.08 grams per liter was entered into the system from a plastic tank. Fertilizer tank was covered to avoid water evaporation even in a small amount. The experiment lasted for 60 days with 12 operating hours per day. The emitter discharge was measured every three days at the end of day. In order to evaluate the degree of emitter clogging, the percentages of discharge reduction (Qt), Christiansen’s coefficient of uniformity (CU), distribution uniformity (DU) and discharge coefficient of variations (Vm) were calculated as follows: (1) (2) (3) (4) where qa, qm and qt are the average, primary and secondary emitter discharges (L/hrs), respectively, qi is the individual emitter discharge (L/hrs), Sm is the standard deviation of discharge (L/hrs) and n is the number of measurements. Results and Discussion: The results indicated that both fertilizer and emitter type have significant effect on reduction of emitter discharge and distribution uniformity as well as on increase of emitter coefficient of variation. The Duncan test for comparing means showed that the A type emitters had the highest clogging while the B type emitters had the lowest clogging. The percentages of discharge reduction for emitters A, B and C were about 18, 24 and 22, respectively, for treatment F0 (control); 24, 39 and 30 for treatment F1; and 34, 44 and 32 for treatment F2. The results indicated that the emitter clogging increases with altering fertilizer from F0 to F2. F2 (urea fertilizer) had the worse effect on emitter clogging than F1 (ammonium nitrate fertilizer) which could be due to more nitrate produced by urea fertilizer. Also, the results showed that the emitter clogging and discharge coefficient of variation are increased by increasing the elapsed time. Urea and ammonium nitrate fertilizers are hydrolyzed in water and partly converted to nitrate, which is consumed by algae and other microorganisms causing slime accumulation. Bacterial slimes can be a direct cause of clogging for emitters. Conclusion: According to the results, both fertilizer and emitter types may significantly change the hydraulic properties of emitters. The smallest clogging belonged to emitter of type A when fertilizer F0 was applied as it results in discharge reduction of 18.44%. The largest clogging belonged to emitter of type B when fertilizer F2 was applied (discharge reduction was about 44%). In general, it could be said that fertigation may influence emitter discharge depending on fertilizer treatments (e.g. fertilizer type and concentration), water properties and emitter type. The clogging problems must be attended more specifically as it may reduce farmers’ willingness for drip irrigation implementation and makes them do surface irrigation which may result in more water losses. This study showed that the quality of water used in drip fertigation increases the clogging made by fertilizer application. So, the quality of irrigation water should be investigated every few days. The use of nitrogen fertilizer may cause biological clogging of emitters, so when such fertilizer are used, the type of emitter should be considered.

Irrigation has made a significant contribution to supporting the population’s expanding food demands, as well as promoting economic growth in irrigated regions. The current investigation was carried out in order to estimate the quality of the groundwater for agricultural viability in the Algerian Desert using various water quality indices and geographic information systems (GIS). In addition, support vector machine regression (SVMR) was applied to forecast eight irrigation water quality indices (IWQIs), such as the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), sodium percentage (Na%), soluble sodium percentage (SSP), potential salinity (PS), Kelly index (KI), permeability index (PI), potential salinity (PS), permeability index (PI), and residual sodium carbonate (RSC). Several physicochemical variables, such as temperature (T°), hydrogen ion concentration (pH), total dissolved solids (TDS), electrical conductivity (EC), K+, Na2+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, and NO3−, were measured from 45 deep groundwater wells. The hydrochemical facies of the groundwater resources were Ca–Mg–Cl/SO4 and Na–Cl−, which revealed evaporation, reverse ion exchange, and rock–water interaction processes. The IWQI, Na%, SAR, SSP, KI, PS, PI, and RSC showed mean values of 50.78, 43.07, 4.85, 41.78, 0.74, 29.60, 45.65, and −20.44, respectively. For instance, the IWQI for the obtained results indicated that the groundwater samples were categorized into high restriction to moderate restriction for irrigation purposes, which can only be used for plants that are highly salt tolerant. The SVMR model produced robust estimates for eight IWQIs in calibration (Cal.), with R2 values varying between 0.90 and 0.97. Furthermore, in validation (Val.), R2 values between 0.88 and 0.95 were achieved using the SVMR model, which produced reliable estimates for eight IWQIs. These findings support the feasibility of using IWQIs and SVMR models for the evaluation and management of the groundwater of complex terminal aquifers for irrigation. Finally, the combination of IWQIs, SVMR, and GIS was effective and an applicable technique for interpreting and forecasting the irrigation water quality used in both arid and semi-arid regions.

Irrigation water quality issues arising from salt water intrusion or industrial pollution are experienced in many parts of Kerala state. This study was undertaken at Eloor, near Cochin, which is under the threat of industrial pollution, with the twin objectives of assessing the shallow groundwater quality of an area, and to evaluate the performance of Irrigation Water Quality Index (IWQI) as compared to hydro-chemical parameters. Water samples collected from 10 open wells during three different seasons for two years were analysed, and physico-chemical characteristics of water viz., Electrical Conductivity (EC), Sodium (Na), Potassium (K), Calcium (Ca), Magnesium (Mg), Chloride (Cl), Carbonate (CO3), and Bicarbonate (HCO3) were determined. Sodium Adsorption Ratio (SAR), Kelly Index (KI), Sodium Percentage (Na%), and Residual Sodium Carbonate (RSC) were calculated. Further, the combined measure IWQI was also computed. The study revealed that groundwater in many parts of Eloor was of poor quality with serious quality concerns during pre-monsoon season, where the IWQI of less than 70 was seen in about 85% of the geographical area. There were wide differences between the water quality indications given by single hydro-chemical methods and the combined hydro-chemical method. The spatial interpolation of EC values suggested that groundwater over the entire region is suitable for irrigation, while as per SAR classifications only 10% of the study area has water unfit for irrigation. The IWQI indicated that 70% of the area has poor quality of irrigation water. The study showed that IWQI combines the inferences from all individual hydro-chemical parameters, and IWQI should preferably be used to provide better and more comprehensive assessment of the irrigation water quality and for informed decision on suitability of water for irrigation planning and management.

Evaluation of drinking and irrigation water quality, and potential risks indices in the Dera Ismail Khan district, Pakistan

This study aims to present a comparative assessment of hydrochemical characterization and groundwater quality in karst aquifers with the support of GIS which is essential to correlate the source of water with climate and geology, and to evaluate suitability of water for various uses. The study area is the Altinova region in Turkey where intensive agricultural activities prevail and travertine covers 85% of the study area. A total of 25 groundwater wells were monitored by seasonal field measurements and extensive water quality analyses for a period of one year to establish correlation between groundwater quality, its source, regional climate, and geology. A comprehensive analysis was conducted to assess the groundwater's suitability for irrigation based on electrical conductivity, sodium adsorption ratio, sodium percentage, magnesium hazard, permeability index, residual sodium bicarbonate, Kelly’s ratio, and an irrigation water quality (IWQ) index. This multi-parameter evaluation was further integrated with geospatial analysis using ArcGIS, providing a detailed spatial understanding of hydrochemical variations across the area. Major cations and anions dominance were identified as Ca2+ > Na+ > Mg2+ > K+ and HCO3− > Cl− > SO42−, respectively. Spatial mapping identified high concentrations of Ca2+, TDS, TH, and SO42− in non-karstic areas, occasionally exceeding WHO guidelines. Nitrate concentrations displayed varied spatial distribution. The SAR values generally matched C2-S1 and C3-S1 classes, suggesting medium to high salinity risks and low sodium presence. Based on the IWQ index and observed correlations with total dissolved solids, the groundwater in Altinova’s karstic aquifer is considered suitable for irrigation, with salinization largely due to ionic interactions and geology. The presented comparative assessment provides a holistic approach for understanding hydrochemical characteristics of karst aquifers, and analyzing the impacts of natural factors and anthropogenic pollution sources on groundwater quality.

Irrigation alleviates poverty in arid and semi-arid regions, such as Ethiopia, necessitating a water suitability assessment. This study evaluates irrigation water quality in the Irob catchment, Northern Ethiopia. It differs by using combined parameters of the irrigation water quality index (IWQI) for comprehensive assessment beyond standard comparisons. Eighteen water samples were collected and analyzed using ultraviolet spectrometry, titration, and atomic absorption spectrometry. The evaluation considered electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), sodium adsorption ratio, sodium percentage, residual sodium carbonate, permeability index, magnesium ratio, Kelley's index, potential salinity, and the IWQI. The findings revealed that 22.2, 38.9, 88.8, 66.7, 83.3, 66.7, 100, 72.2, and 55.6% of samples exceeded recommended standards for EC, TDS, TH, permeability index, manganese, cobalt, copper, cadmium, and nickel, respectively. Most water quality parameters meet standards, but improved irrigation management is crucial to reduce risks. The IWQI indicates that 22.2% of water samples have minor restrictions, while 77.8% have no restrictions. This method provides key insights for evaluating irrigation water quality in similar hydrogeological and environmental conditions, particularly in semi-arid and arid regions. The findings enhance the understanding of sustainable water quality, supporting local authorities in developing resilient irrigation strategies for regional agricultural sustainability.

Groundwater quality and associated health risk in the arid environment, Rabigh basin, Western Saudi Arabia, was assessed using an integrated approach namely groundwater suitability zone (GWSZ) maps, drinking water quality index (DWQI), irrigation water quality (IWQ) parameters, irrigation water quality index (IWQI), chronic daily index (CDI), and hazard quotient (HQ). Groundwater samples were collected (n = 50) and analysed. Groundwater is alkaline (80%), fresh to brackish, and hard to very hard, and 78% of samples exceeded the international drinking water safe limit. The DWQI indicates that groundwater samples are excellent (24%), good (24%), poor (20%), very poor (10%), and unsuitable (22%) classes for drinking use. Total HQ (HQoral F- + HQoral NO3-) indicated that 68%, 80%, and 72% of samples express non-carcinogenic health threat to adult, children, and infant, respectively, in the study region. IWQ parameters, namely, EC, sodium adsorption ratio (SAR), Kelly's ratio (KR), sodium percentage (Na%), permeability index (PI), and magnesium hazard (MH), suggest that 72%, 66%, 64%, 98%, and 92% of samples have SAR < 6, KR < 1, MH < 50, PI > 25%, and Na% < 60%, respectively, which are suitable for irrigation. USSL classification implies that groundwater is suitable only for salt-tolerant crops and high permeability soil. IWQI values suggest that groundwater in 12%, 82%, and 6% of wells are low, medium, and highly suitable, respectively, for irrigation. Furthermore, only 42% of samples are recommended for livestock uses due to high F- (> 2). GWSZ maps, DWQI, and IWQI imply that groundwater in the upstream region is suitable whereas groundwater in the downstream is not recommended for any uses. Hence, this study recommended proper groundwater augmentation methods to reduce the salinity and improve the water quality in the shallow aquifer in the arid environment. The GWSZ, DWQI, and IWQI maps will aid to identify the suitable zones for groundwater development and sustainable management.

Water quality is identically important as quantity in terms of meeting basic human needs. Therefore, evaluating the surface-water quality and the associated hydrochemical characteristics is essential for managing water resources in arid and semi-arid environments. Therefore, the present research was conducted to evaluate and predict water quality for agricultural purposes across the Nile River, Egypt. For that, several irrigation water quality indices (IWQIs) were used, along with an artificial neural network (ANN), partial least square regression (PLSR) models, and geographic information system (GIS) tools. The physicochemical parameters, such as T °C, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, and NO3−, were measured at 51 surface-water locations. As a result, the ions contents were the following: Ca2+ &gt; Na+ &gt; Mg2+ &gt; K+ and HCO3− &gt; Cl− &gt; SO42− &gt; NO3− &gt; CO32−, reflecting Ca-HCO3 and mixed Ca-Mg-Cl-SO4 water types. The irrigation water quality index (IWQI), sodium adsorption ratio (SAR), sodium percentage (Na%), soluble sodium percentage (SSP), permeability index (PI), and magnesium hazard (MH) had mean values of 92.30, 1.01, 35.85, 31.75, 72.30, and 43.95, respectively. For instance, the IWQI readings revealed that approximately 98% of the samples were inside the no restriction category, while approximately 2% of the samples fell within the low restriction area for irrigation. The ANN-IWQI-6 model’s six indices, with R2 values of 0.999 for calibration (Cal.) and 0.945 for validation (Val.) datasets, are crucial for predicting IWQI. The rest of the models behaved admirably in terms of predicting SAR, Na%, SSP, PI, and MR with R2 values for the Cal. and validation Val. of 0.999. The findings revealed that ANN and PLSR models are effective methods for predicting irrigation water quality to assist decision plans. To summarize, integrating physicochemical features, WQIs, ANN, PLSR, models, and GIS tools to evaluate surface-water suitability for irrigation offers a complete image of water quality for sustainable development.

The assessment of groundwater for irrigation was a crucial step towards sustainable water resource management. Traditionally, irrigation water suitability was carried out by assessing several irrigation parameters individually. In this research, an attempt was made to develop a new irrigation water quality (IWQI) index by integrating various irrigation water suitability parameters i.e., sodium adsorption ratio (SAR), Magnesium Adsorption Ratio (MAR), residual sodium carbonate (RSC), Exchangeable sodium ratio or Kelly ratio (KR), Soluble Sodium Percentage (SSP), total hardness (TH), Electrical Conductivity (EC), Permeability index (PI) and fluoride (F−). A stretch of the Dwarka River basin was selected as a type area for this research. Water sample collection, analysis followed by classification of water parameters into five different classes, and assigning weights and ranks were the principal methodologies adopted for the building up of the Irrigation water quality index (IWQI). The order of abundance of anions in the study area was HCO3− > Cl− > CO3−2 > SO42− > F− and cation are Ca+2 > Na+ > Mg+2 > K+. A total of 607 water samples were collected and the computed IWQI with respect to the present study reveals that 95.38% (579 samples) of the total water sample were suitable for irrigation whereas the remaining 4.61% (28 samples) of the total sample show unsuitability for irrigation. Nawapara, Junidpur, Chakpara, Bhelian were among the most polluted village. Outcomes of the present study can be a first-hand tool to the policymakers, planners, and government officials for sustainable water resource management in the study area.

Ascertaining water quality for irrigational use by employing conventional methods is often time taking and expensive due to the determination of multiple parameters needed, especially in developing countries. Therefore, constructing precise and adequate models may be beneficial in resolving this problem in agricultural water management to determine the suitable water quality classes for optimal crop yield production. To achieve this objective, five machine learning (ML) models, namely linear regression (LR), random subspace (RSS), additive regression (AR), reduced error pruning tree (REPTree), and support vector machine (SVM), have been developed and tested for predicting of six irrigation water quality (IWQ) indices such as sodium adsorption ratio (SAR), percent sodium (%Na), permeability index (PI), Kelly ratio (KR), soluble sodium percentage (SSP), and magnesium hazards (MH) in groundwater of the Nand Samand catchment of Rajasthan. The accuracy of these models was determined serially using the mean squared error (MSE), correlation coefficients (r), mean absolute error (MAE), and root mean square error (RMSE). The SVM model showed the best-fit model for all irrigation indices during testing, that is, RMSE: 0.0662, 4.0568, 3.0168, 0.1113, 3.7046, and 5.1066; r: 0.9364, 0.9618, 0.9588, 0.9819, 0.9547, and 0.8903; MSE: 0.004381, 16.45781, 9.101218, 0.012383, 13.72447, and 26.078; MAE: 0.042, 3.1999, 2.3584, 0.0726, 2.9603, and 4.0582 for KR, MH, SSP, SAR, %Na, and PI, respectively. The KR and SAR values were predicted accurately by the SVM model in comparison to the observed values. As a result, machine learning algorithms can improve irrigation water quality characteristics, which is critical for farmers and crop management in various irrigation procedures. Additionally, the findings of this research suggest that ML models are effective tools for reliably predicting groundwater quality using general water quality parameters that may be acquired directly on periodical basis. Assessment of water quality indices may also help in deriving optimal strategies to utilise inferior quality water conjunctively with fresh water resources in the water-limited areas.

Currently, a well-developed combination of irrigation water quality index (IWQIs) and entropy water quality index (EWQIs) for surface water appraisal in a polluted subtropical urban river is very scarce in the literature. To close this gap, we developed IWQIs by establishing statistics-based weights of variables recommended by FAO 29 standard value using the National Sanitation Foundation Water Quality Index (NSFWQI) compared with the proposed EWQIs based on information entropy in the Dhaleshwari River, Bangladesh. Fifty surface water samples were collected from five sampling locations during the dry and wet seasons and analyzed for sixteen variables. Principal component analysis (PCA), factor analysis (FA), Moran's spatial autocorrelation, and random forest (RF) model were employed in the datasets. Weights were allocated for primary variables to compute IWQI-1, 2 and EWQI-1, 2, respectively. The resultant IWQIs showed a similar trend with EWQIs and revealed poor to good quality water, with IWQI-1 for the dry season and IWQI-2 for the wet season is further suggested. The entropy theory recognized that Mg2+, Cr, TDS, and Cl- for the dry season and Cd, Cr, Cl-, and SO42- for the wet season are the major contaminants that affect irrigation water quality. The primary input variables were lessened to ultimately shortlisted ten variables, which revealed good performance in demonstrating water quality status since weights have come effectively from PCA than FA. The results of the RF model depict NO3-, Mg2+, and Cr as the most predominant variables influencing surface water quality. A significant dispersed pattern was detected for IWQImin-3 in the wet season (Moran's I>0). Overall, both IWQIs and EWQIs will generate water quality control cost-effective, completely objective to establish a scientific basis of sustainable water management in the study basin.

Integration of multivariate statistical analysis, geochemical modeling, and irrigation water quality assessment in the aquifers of the South-Atlas Tinghir-Errachidia-Boudenib basin (Pre-African Trough, Morocco)

Fuzzy logic, geostatistics, and multiple linear models to evaluate irrigation metrics and their influencing factors in a drought-prone agricultural region