Due to sudden declines in groundwater levels in Neyshabur Plain, one of the most
important parts of water supply management programs at the catchment scale is to accurately
predict the groundwater level fluctuations. In this paper, the rainfall data from 22 rain gauges
and evapotranspiration stations during the period of 1974–2015 were used to find the
cumulative effects of rainfall and evapotranspiration on fluctuations in groundwater levels.
First, using the Hargreaves-Samani method, the modified evapotranspiration was calculated on
the plain. Using the Kriging method, the average amount of precipitation and evapotranspira-
tion of the reference plant was also calculated. Then, employing the fuzzy logic, the fuzzy
standardized evapotranspiration and precipitation index (SEPI) was produced. The correlation
results between SEPI indicator and fluctuations in groundwater levels showed that the long-
term time scales had greater correlations. Thus, the correlations for the time scales of 30, 36,
42, 48, 54 and 60 months were respectively obtained as 0.56, 0.68, 0.71, 0.69, 0.59 and 046.
These six parameters were used for principal components analysis (PCA) and the selection
criteria (SC) index was used to select the properties affecting every component. The ranking
results of testing local linear regression with PCA (LLR-PCA) and dynamic local linear
regression with PCA (DLLR-PCA) models, Broyden, Fletcher, Goldfarb, Shanno algorithm
with PCA (BFGS-PCA) neural network and Conjugate Gradient-PCA indicated that the
DLLR model with three main components had the best performance so that the values of
R2, RMSE, MBE and MAE were obtained as 0.84, 0.215, 0.028 and 0.162, respectively. The
results generally showed that due to severe linearity between SEPI indicator and its time
scales, the use of PCA is essential for simulating fluctuations of the groundwater levels.