Forecasting Groundwater table depth fluctuations using conjunction models of Wavelet – Neural - Fuzzy Network (WNF) (case study: Aleshtar Plain)

The aim of this study was to estimate the groundwater level of Aleshtar plain at times t + 1, t + 3 and t + 6 using the parameters of temperature, precipitation and groundwater level at times t, t-1 and t-2 using models: neural network (ANN) , a neural fuzzy inference system (ANFIS), a neural-wavelet (WNN), and an integrated neural-fuzzy-wavelet (WNF) network. Two indices R2 and RMSE were used to evaluate the models. The results of predicting different models showed that ANFIS, WNN and WNF have higher accuracy in predicting groundwater level than ANN model. Also, the comparison of the results obtained from wavelet-based models and other models showed that these models (WNN and WMF) have higher accuracy than other models due to pre-processing and data analysis. The use of WNF model compared to ANN has increased the R2 index from 0.94 to 0.98 (in one-month forecast), 0.84 to 0.93 (in three-month forecast) 0.76 to 0.85 ( In the six-month forecast). Also, WNF compared to ANN model, has decreased RMSE index from 0.56 to 0.32 (in one-month forecast), 0.96 to 0.66 (in three-month forecast) and 1.18 to 0.97 (in the six-month forecast). The results of groundwater depth prediction with four models showed that these models have more accurate in predicting shorter time steps. Also, using of models in predictions with a time delay of more than three months, not only does not have much effect on the accuracy of the model, but in models with wavelet basis reduces the accuracy.