Groundwater fluoride modeling using an artificial neural network: a review - Kumari, Neeta; Kumar, Gaurav; Hembrom, Saahil (Frontiers in Water, 2025)
Groundwater fluoride contamination poses serious health effects to humans, as excess amounts of fluoride can cause skeletal and dental fluorosis. The problem is critical in areas where the aquifers are surrounded by fluoride-bearing rocks. Apart from the geology, the meteorology of the place also plays an important role. The excess fluoride in water can also be associated with chemical ions found in water. Groundwater fluoride modeling using an artificial neural network (ANN) is a valuable approach. Inputs are selected through statistical analysis. The modeling process is carried out using the “nntool” in MATLAB software. This ANN model can be used to predict future fluoride levels based on primary data obtained from water sample analyses. The results of the correlation analysis help in deciding the inputs for the model. The network architecture can be determined through the trial-and-error method. The network should be trained, tested, and validated on separate datasets. The prediction accuracy of the network can be assessed using root mean square error (RMSE) analysis and the coefficient of determination (R2). Groundwater fluoride can also be modeled using logistic regression (LR), random forest (RF), Monte Carlo simulation (MCS), artificial neural network (ANN), support vector machine (SVM), gradient boosting (XGBoost), and Classification and Regression tree (CART) methods. However, ANN is best suited as it can address numerous inaccuracies within the data and extract information about the associations between input and output variables. The accurate prediction will help in decision-making and the proper management of groundwater fluoride contamination.