article · Heliyon
Groundwater quality assessment across 48 wells in the El-Menoufia region combined chemical indexing, health risk evaluations, geographic information systems, and recurrent neural networks. Although overall drinking water quality index scores classified most samples as good for consumption, specific areas revealed significant contamination. In particular, concentrations of lead, manganese, and iron exceeded standard thresholds, with the central zone showing the highest levels of metal pollution. Health risk analyses showed non-carcinogenic dangers for children through both oral intake and dermal contact, while adults faced risks solely through ingestion. Recurrent neural network models successfully forecasted key contamination and quality metrics using selected input parameters, demonstrating high predictive accuracy across testing datasets. This approach demonstrates how combining standard geochemical evaluations with predictive machine learning models can improve monitoring of vulnerable drinking water resources.
Groundwater contamination from heavy metals poses severe health threats, particularly to developing children. Demonstrating that machine learning models can accurately forecast water quality indices from a limited set of parameters allows environmental managers to identify high-risk locations more quickly. This integrated assessment method helps direct remediation efforts and targeted testing to regions where hazardous elements such as lead and manganese threaten community health.
The research demonstrates an applied predictive framework that could assist water resource authorities, municipal planners, and environmental consultancies in automated water monitoring. By using neural networks to predict complex water quality indices from fewer chemical inputs, the tool could lower operational testing costs. The technology currently sits at an applied and tested research stage, having been validated on regional field data, but requires software product development before reaching commercial deployment.
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<h2>Abstract</h2> Assessing and predicting quality of groundwater is crucial in managing groundwater availability effectively. In the current study, groundwater quality was thoroughly appraised using various indexing methods, including the drinking water quality index (DWQI), pollution index of heavy metals (HPI), pollution index (PI), metal index (MI), degree of contamination (C<sub>d</sub>), and risk indicators, like hazard quotient (HQ) and total hazard indicator (HI). The assessments were augmented through multivariate analytical techniques, models based on recurrent neural networks (RNNs), and integration of geographic information system (GIS) technology. The analysis measured physicochemical parameters across 48 groundwater wells from El-Menoufia region, revealing distinct water types influenced by ion exchange, rock-water interactions, and silicate weathering. Notably, the groundwater showed elevated levels of certain metals, particularly manganese (Mn) and lead (Pb), exceeding the drinking water limits. The DWQI deemed the bulk of the tested samples suitable for consumption, assigning them to the "good" category, whereas a small number were considered inferior quality. The HPI, MI, and C<sub>d</sub> indices indicated significant pollution in the central study region. The PI revealed that Pb, Mn, and Fe were significant contributors to water pollution, falling between classes IV (strongly affected) and V (seriously affected). HQ and HI analyses identified the central area of the study as particularly prone to metal contamination, signifying a high risk to children via oral and dermal routes and to adults through oral exposure alone (non-carcinogenic risk). The adults had no health risks due to dermal contact. Finally, the RNN simulation model effectively predicted the health and water quality indices in training and testing series. For instance, the RNN model excelled in predicting the DWQI, with three key parameters being crucial. The model demonstrated an excellent fit on the training set, achieving an R<sup>2</sup> of 1.00 with a very low root mean of squared error (RMSE) of 0.01. However, on the testing set, the model's performance slightly decreased, showing an R<sup>2</sup> of 0.96 and an RMSE of 2.73. Regarding HPI, the RNN model performed exceptionally well as the primary predictor, with R<sup>2</sup> values of 1.00 (RMSE = 0.01) and 0.93 (RMSE = 27.35) for the training and testing sets, respectively. This study provides a unique perspective for improving the integration of various techniques to gain a more comprehensive understanding of groundwater quality and its associated health risks, with a strong focus on feature selection strategies to enhance model accuracy and interpretability.
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DOI: 10.1016/j.heliyon.2024.e36606
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