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

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Environmental Risk Assessment of Trace Metal Pollution: A Statistical Perspective

Environmental Geochemistry and Health, Journal Year: 2025, Volume and Issue: 47(4)

Published: Feb. 28, 2025

Abstract Trace metal pollution is primarily driven by industrial, agricultural, and mining activities presents complex environmental challenges with significant implications for ecological human health. Traditional methods of risk assessment (ERA) often fall short in addressing the intricate dynamics trace metals, necessitating adoption advanced statistical techniques. This review focuses on integrating contemporary methods, such as Bayesian modeling, machine learning, geostatistics, into ERA frameworks to improve precision, reliability, interpretability. Using these innovative approaches, either alone or preferably combination, provides a better understanding mechanisms transport, bioavailability, their impacts can be achieved while also predicting future contamination patterns. The use spatial temporal analysis, coupled uncertainty quantification, enhances hotspots associated risks. Integrating models ecotoxicology further strengthens ability evaluate health risks, providing broad framework managing pollution. As new contaminants emerge existing pollutants evolve behavior, need adaptable, data-driven methodologies becomes ever more pressing. advancement tools interdisciplinary collaboration will essential developing effective management strategies informing policy decisions. Ultimately, lies diverse data sources, analytical techniques, stakeholder engagement, ensuring resilient approach mitigating protecting public

Language: Английский

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Health risk assessment of groundwater use for drinking in West Nile Delta, Egypt

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 3, 2025

Abstract Human health is at risk from drinking water contamination, which causes a number of problems in many parts the world. The geochemistry groundwater, its quality, origins groundwater pollution, and associated risks have all been subject substantial research recent decades. In this study, west Rosetta Nile branch Delta Aquifer examined for potential. Numerous quality indices were applied, such as index (WQI), synthetic pollution (SPI) models, assessment (HRA) method. limits measured parameters are used to test validity on basis WHO recommendations. TDS southern regions within desirable allowable with percent 25.3% 29.33%, respectively. Nearly study area has value HCO 3 , Al Ba. Ca Mg values center south portion investigated area, whereas north unsuitable. Na, Cl SO 4 fall desired level but become unsuitable towards north. Mn NO inappropriate except northwestern part. Fe suitable range southwestern regions. Pb, Zn, Cu, Cd undetected collected samples. Regarding WQI classified into classes good, poor, very poor unfit According SPI model, 20%, 18.7%, 8% 34.6% samples suitable, slightly, moderately, highly polluted unfit, respectively Based HRA, Children most category endangered 14.7% overall obtained, followed by females males 12% 8%, This offers insights conservation management coastal aquifers’ supplies. These findings significant implications developing strategies executing preventative actions reduce resource vulnerability related hazards West Delta, Egypt.

Language: Английский

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Evaluation of surface water quality in Brahmani River Basin, Odisha (India), for drinking purposes using GIS-based WQIs, multivariate statistical techniques and semi-variogram models

Innovative Infrastructure Solutions, Journal Year: 2024, Volume and Issue: 9(12)

Published: Nov. 23, 2024

Language: Английский

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Monte Carlo simulation and PMF model for assessing human health risks associated with heavy metals in groundwater: a case study of the Nubian aquifer, Siwa depression, Egypt

Frontiers in Earth Science, Journal Year: 2024, Volume and Issue: 12

Published: Sept. 27, 2024

Introduction The groundwater in arid countries such as Egypt represent the main water resources desert regions due to long distance between these (oasis) and Nile River. Contamination of limited with toxic metals threaten health individuals regions. Methods current study integrates isotopic tracers, hydrogeochemistry, geophysical logs, positive matrix factorization (PMF model), Monte Carlo (MCS) simulation for pollution source apportionment risks associated heavy Nubian Sandstone aquifer (NSSA). Results Discussion resource used drinking purposes (NSSA) is pale meteoric (non-rechargeable aquifer). Silicate weathering, old trapped sea water, reverse ion exchange evaporation, dissolution are dominant mechanisms controlling chemistry. PMF model showed that major ions NSSA originated from four significant sources (anthropogenic activities, minerals, iron-bearing mixing seawater, hydrothermal water). total risk (HI) oral values highlighted non-carcinogenic dangers adults children through exposure. At same time, dermal contact posed a no high children. Most samples had carcinogenic (CR) higher than allowed limits (1.0E-4) like Cadmium, chromium, lead, suggesting effects across all age groups. approach-based concern evaluation assessed 5th % CR (child) 0.00012, 0.0036, 0.0088 Cd, Cr, Pb, respectively, indicating more potential Urgent comprehensive treatment measures imperative mitigate identified area.

Language: Английский

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Assessment of the Groundwater Quality of the Jessore Municipality by Using Weighted Arithmetic Water Quality Index

Next research., Journal Year: 2025, Volume and Issue: 2(1), P. 100187 - 100187

Published: Feb. 3, 2025

Language: Английский

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The application of Simulated Annealing Algorithm, Firefly Algorithm, Invasive Weed Optimization, and Shuffled Frog Leaping Algorithm for prediction of Water Quality Index

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

Groundwater is a vital resource for drinking water, agriculture, and industry worldwide. Effective groundwater quality management crucial safeguarding public health ensuring ecological sustainability. Hydrogeochemical data modeling widely utilized to predict using various approaches. The method proposed in this study leverages an intelligent model combined with chemical compositions. Sampling was conducted from 175 agricultural wells the Arak Plain. By utilizing hydrogeochemical performing correlation sensitivity analyses, key compositions were identified: Ca²⁺, Cl⁻, EC, HCO₃⁻, K⁺, Mg²⁺, Na⁺, pH, SO₄²⁻, TDS, NO₃⁻.The predicted Water Quality Index (WQI) values composition artificial neural network (ANN) model. of served as model’s input, while WQI treated output. To enhance ANN's accuracy, several optimization algorithms used, including: Simulated Annealing Algorithm (SAA), Firefly (FA), Invasive Weed Optimization (IWO), Shuffled Frog Leaping (SFLA).The comparison results indicated that ANN-SAA outperformed other models. R² MSE predicting training data: = 0.8275, 0.0303 test 0.7357, 0.0371.These demonstrate provides reliable accurate index values, offering valuable tool assessment management.

Language: Английский

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Leveraging Novel Machine Learning Models in Predicting Groundwater Irrigation Suitability in Southeastern Nigeria: A Hydrogeochemical Approach

Scientific African, Journal Year: 2025, Volume and Issue: unknown, P. e02646 - e02646

Published: March 1, 2025

Language: Английский

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Integrating Unsupervised Machine Learning, Statistical Analysis, and Monte Carlo Simulation to Assess Toxic Metal Contamination and Salinization in Non-Rechargeable Aquifers

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104989 - 104989

Published: April 1, 2025

Language: Английский

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Aquatic System Assessment of Potentially Toxic Elements in El Manzala Lake, Egypt: A Statistical and Machine Learning Approach

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105027 - 105027

Published: April 1, 2025

Language: Английский

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