Background: One issue of concern in water supply is the quality of water. Measuring the qualitative
parameters of water is time-consuming and costly. Predicting these parameters using various models
leads to a reduction in related expenses and the presentation of overall and comprehensive statistics for
water resource management.
Methods: The present study used an artificial neural network (ANN) to simulate fluoride concentrations
in groundwater resources in Khaf and surrounding villages based on the physical and chemical
properties of the water. ANN modeling was applied with regard to diverse inputs.
Results: The MLP1 model with eight inputs of parameters such as root mean square error (RMSE) and
correlation coefficient of actual and predicted outputs exhibited the best results. The lowest fluoride
concentration (0.15 mg L-1) was found in Sad village, and the highest concentration (3.59 mg L-1) was
found in Mahabad village. Based on World Health Organization (WHO) standards, 56.6% of the villages
are in the desirable range, 33.3% of them had fluoride concentrations below standard levels, and 10%
had higher than standard concentrations of fluoride.
Conclusion: The simulation results from the testing stage for MLP1 as well as the high conformity
between experimental and predicted data indicated that this model with its high confidence coefficient
can be used to predict fluoride concentrations in groundwater resources.
Keywords: Water quality, Artificial neural network model, Fluoride, Groundwate
