MINI REVIEW article
Front. Water
Sec. Water and Human Health
Volume 7 - 2025 | doi: 10.3389/frwa.2025.1580202
This article is part of the Research TopicIndian Scenario on Exposure, Characterization and Health Risk Appraisal of Toxic Contaminants in GroundwaterView all articles
Groundwater Fluoride modelling using Artificial Neural Network: A review
Provisionally accepted
- 1Birla Institute of Technology, Mesra, Ranchi, India
- 2Birla Institute of Technology, Mesra, Ranchi, Ranchi, India
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Groundwater fluoride contamination is posing serious health effects in humans as their excess amount cause skeletal and dental fluorosis. The problem is critical in the areas having fluoride bearing rocks around the aquifers. Apart from the geology, meteorology of the place, also plays an important role. The excess fluoride in water can also be associated with chemical ions found in water. The groundwater fluoride modelling using artificial neural network (ANN) is a valuable approach. Inputs are selected through statistical analysis. The modelling is done by “nntool” of MATLAB software. The ANN modelling can be used for future fluoride level prediction based on the primary data collected through water sample analysis. The results of the correlation analysis help in deciding the input of the model. The network architecture can be decided by trial-and-error method. The network should be trained, tested and validated with seperate dataset. The prediction accuracy of the network can be assessed by the calculation of root mean square error analysis (RMSE) and coefficient of determination (R2). The Groundwater fluoride can also be modelled by using logistic regression (LR) and Random Forest (RF), Monte Carlo Simulation (MCS), Artificial neural networks (ANN), Support Vector Machine (SVM), Gradient Boosting (Xgboost), and classification and regression tree (CART) methods etc but ANN is the best suited as it can address numerous inaccuracies within the data. It can extract the information about association between input and output variables. The accurate prediction will help in decision making and proper management of groundwater fluoride contamination.
Keywords: Artificial neural network (ANN), Fluoride, Geogenic, Groundwater, MATLAB, root mean square error analysis (RMSE)
Received: 20 Feb 2025; Accepted: 14 Apr 2025.
Copyright: © 2025 Kumari, Kumar and Hembrom. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Neeta Kumari, Birla Institute of Technology, Mesra, Ranchi, India
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