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Heavy metals attract a great deal of attention nowadays due to their potential accumulation in living creatures and transference in the food chain. Sediments of water reservoirs are considered to be a source of accumulation of these metals that develop in response to human activities and soil erosion. This study collected 180 samples of the surface sediments of water reservoir 1 at Chahnimeh in Sistan. Efficiency of the ANFIS model was evaluated to estimate the five bonds following the measurement of parameters in the laboratory.The following results were obtained for the parameters: organic carbon (OC) %, 0.31; cation exchange capacity (CEC), 37.07 Cmol kg; total Pb, 25.19 mg/kg; clay %, 45.87; and silt %, 39.02. These parameters were used as input for the training model. In the output layer, lead bonds were chosen as modeling targets in the following way: Pb f1 (4.61); Pb f2 (0.54); Pb f3 (16.28); Pb f4 (3.42); and Pb f5 (0.38) mg/kg. The best input compound in this model was chosen using the gamma test. From a total of 180, 88 data were considered for the model training section. Eventually, the neural-fuzzy model (subtractive clustering), developed for the prediction of lead bonds in the studied region, was able to account for over 99% of lead bonds in the sediments; considering statistical criteria of root mean squares error or RMSE (0.0337-0.0813) and determination coefficient or R (0.92-0.99), this model showed good performance with regard to prediction.

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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 MLP 1 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 MLP 1 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.

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