Evaluation and Prediction of Groundwater Quality for Irrigation Using an Integrated Water Quality Indices, Machine Learning Models and GIS Approaches: A Representative Case Study

Water, Journal Year: 2023, Volume and Issue: 15(4), P. 694 - 694

Published: Feb. 10, 2023

Agriculture has significantly aided in meeting the food needs of growing population. In addition, it boosted economic development irrigated regions. this study, an assessment groundwater (GW) quality for agricultural land was carried out El Kharga Oasis, Western Desert Egypt. Several irrigation water indices (IWQIs) and geographic information systems (GIS) were used modeling development. Two machine learning (ML) models (i.e., adaptive neuro-fuzzy inference system (ANFIS) support vector (SVM)) developed prediction eight IWQIs, including index (IWQI), sodium adsorption ratio (SAR), soluble percentage (SSP), potential salinity (PS), residual carbonate (RSC), Kelley (KI). The physicochemical parameters included T°, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, NO3−, they measured 140 GW wells. hydrochemical facies resources Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, Na-Cl, Ca-Mg-HCO3, Na-Ca-HCO3 types, which revealed silicate weathering, dissolution gypsum/calcite/dolomite/ halite, rock–water interactions, reverse ion exchange processes. IWQI, SAR, KI, PS showed that majority samples categorized purposes into no restriction (67.85%), excellent (100%), good (57.85%), to (65.71%), respectively. Moreover, selected as safe according SSP RSC. performance simulation evaluated based on several skills criteria, ANFIS model SVM capable simulating IWQIs with reasonable accuracy both training “determination coefficient (R2)” (R2 = 0.99 0.97) testing 0.97 0.76). presented models’ promising illustrates their use IWQI prediction. findings indicate ML methods geographically dispersed hydrogeochemical data, such SVM, be assessing irrigation. proposed methodological approach offers a useful tool identifying crucial components evolution mitigation measures related management arid semi-arid environments.

Language: Английский

---

Groundwater Quality and Health Risk Assessment Using Indexing Approaches, Multivariate Statistical Analysis, Artificial Neural Networks, and GIS Techniques in El Kharga Oasis, Egypt

Water, Journal Year: 2023, Volume and Issue: 15(6), P. 1216 - 1216

Published: March 20, 2023

The assessment and prediction of water quality are important aspects resource management. Therefore, the groundwater (GW) Nubian Sandstone Aquifer (NSSA) in El Kharga Oasis was evaluated using indexing approaches, such as drinking index (DWQI) health (HI), supported with multivariate analysis, artificial neural network (ANN) models, geographic information system (GIS) techniques. For this, physical chemical parameters were measured for 140 GW wells, which indicated Ca–Mg–SO4, mixed Ca–Mg–Cl–SO4, Na–Cl, Ca–Mg–HCO3, Na–Ca–HCO3 facies under influence silicate weathering, rock–water interactions, ion exchange processes. had high levels heavy metals, particularly iron (Fe) manganese (Mn), average concentrations above limits recommended by World Health Organization (WHO) water. DWQI categorized most samples not suitable (poor to very poor class), while some fell good class. results HI a potential risk due ingestion water, being higher children only one location. However, both adults, there low dermal exposure all locations. contaminants could be from natural sources, minerals leaching rocks soil, or human activities. Based on ANN modeling, ANN-SC-13 accurate model, since it demonstrated strongest correlation between best characteristics DWQI. example, this model’s thirteen extremely predicting R2 value training, cross-validation (CV), test data 0.99. ANN-SC-2 model measuring adults. CV, 1.00 models. at detecting adults (R2 = 0.99, 0.99 sets, respectively). Finally, integration physicochemical parameters, indices (WQIs), models can help us understand its controlling factors, implement necessary measures that prevent outbreaks various water-borne diseases detrimental health.

Language: Английский

---

Assessment of groundwater suitability for sustainable irrigation: A comprehensive study using indexical, statistical, and machine learning approaches

Groundwater for Sustainable Development, Journal Year: 2023, Volume and Issue: 24, P. 101059 - 101059

Published: Dec. 13, 2023

Language: Английский

---

Water Quality Evaluation and Prediction Using Irrigation Indices, Artificial Neural Networks, and Partial Least Square Regression Models for the Nile River, Egypt

Water, Journal Year: 2023, Volume and Issue: 15(12), P. 2244 - 2244

Published: June 15, 2023

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

Language: Английский

---

New approach into human health risk assessment associated with heavy metals in surface water and groundwater using Monte Carlo Method

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 10, 2024

Abstract This study assessed the environmental and health risks associated with heavy metals in water resources of Egypt's northwestern desert. The current approaches included Spearman correlation matrix, principal component analysis, cluster analysis to identify pollution sources quality-controlling factors. Various indices (HPI, MI, HQ, HI, CR) were applied evaluate human risks. Additionally, Monte Carlo method was employed for probabilistic carcinogenic non-carcinogenic risk assessment via oral dermal exposure routes adults children. Notably, all exhibited high HPI MI values exceeding permissible limits (HPI > 100 6), respectively. Furthermore, HI indicated significant both children, while contact posed a 19.4% samples 77.6% children (HI 1). Most CR 1 × 10 –4 Cd, Cr, Pb, suggesting vulnerability effects age groups. simulations reinforced these findings, indicating impact on adults. Consequently, comprehensive treatment measures are urgently needed mitigate Siwa Oasis.

Language: Английский

---

Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques

Marine Pollution Bulletin, Journal Year: 2024, Volume and Issue: 205, P. 116645 - 116645

Published: June 25, 2024

Language: Английский

---

Application of stable isotopes, mixing models, and K-means cluster analysis to detect recharge and salinity origins in Siwa Oasis, Egypt

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 25, P. 101124 - 101124

Published: Feb. 21, 2024

Waterlogging, soil salinization, and groundwater depletion in non-rechargeable aquifers are among the causes endangering sustainability of Siwa Oasis. The water resources include tertiary carbonate aquifer (TCA), Nubian sandstone (NSSA), springs, drains, hypersaline lakes. Geophysical logs were used to identify various systems. Stable isotopes, major ions, heavy metals examined. K-means cluster analysis based on principal component (PCA), hydrochemistry, stable mixing models using NETPATH applied order understand complex hydrogeological conditions different results showed that all samples under-saturated (SI < 1) with respect anhydrite, halite, gypsum minerals, indicating water's ability dissolve more from these minerals increase salinity.In contrast, calcite, dolomite, Ca-montmorillonite, illite supersaturated most > 1), showing their precipitate minerals. There is no recharge for systems, have paleo meteoric origin, where maximum values δ18O δ2H −8.45 −65.63, respectively. All springs originated TCA, as indicated K-mean analysis. a rapid salinity TCA 1998 2022 due evaporation, rock weathering, old trapped sea water, seepage saline salt significant variations between fresh NSSA (dilution) Zeituna lakes through downward flow some locations, over-extraction irrigation purposes has decreased pressure TCA. model confirmed lakes' contribution increasing by 2%–4%. Mixing could be best management Oasis, well application subsurface drip irrigation.

Language: Английский

---

Machine learning and GIS based groundwater quality prediction for agricultural practices - A case study form Arjunanadi River basin of South India

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 229, P. 109932 - 109932

Published: Jan. 16, 2025

Language: Английский

---

Groundwater quality assessment for drinking and irrigation uses within the vicinities of Volta Lake and Akosombo Dam in Ghana: a multi-methodological approach

Environmental Earth Sciences, Journal Year: 2025, Volume and Issue: 84(7)

Published: March 26, 2025

Language: Английский

---

Assessment of hydrogeochemistry in groundwater using water quality index model and indices approaches

Heliyon, Journal Year: 2023, Volume and Issue: 9(9), P. e19668 - e19668

Published: Sept. 1, 2023

Groundwater resources around the world required periodic monitoring in order to ensure safe and sustainable utilization for humans by keeping good status of water quality. However, this could be a daunting task developing countries due insufficient data spatiotemporal resolution. Therefore, research work aimed assess groundwater quality terms drinking irrigation purposes at adjacent part Rooppur Nuclear Power Plant (RNPP) Bangladesh. For achieving aim study, nine samples were collected seasonally (dry wet season) seventeen hydro-geochemical indicators analyzed, including Temperature (Temp.), pH, electrical conductivity (EC), total dissolved solids (TDS), alkalinity (TA), hardness (TH), organic carbon (TOC), bicarbonate (HCO3-), chloride (Cl-), phosphate (PO43-), sulfate (SO42-), nitrite (NO2-), nitrate (NO3-), sodium (Na+), potassium (K+), calcium (Ca2+) magnesium (Mg2+). The present study utilized Canadian Council Ministers Environment index (CCME-WQI) model purposes. In addition, indices EC, TDS, TH, adsorption ratio (SAR), percent (Na%), permeability (PI), Kelley's (KR), hazard (MHR), soluble percentage (SSP), Residual carbonate (RSC) used assessing computed mean CCME-WQI score found higher during dry season (ranges 48 74) than 40 65). Moreover, ranked between "poor" "marginal" categories implying unsuitable human consumption. Like model, majority also demonstrated suitable crop cultivation season. findings indicate that it requires additional care improve programme protecting RNPP area. Insightful information from might useful as baseline national strategic planners protect any emergencies associated with RNPP.

Language: Английский

---