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Table 1 displays the statistical parameters of calibration and validation data. The utmost value of calibration period was bigger than that of the validation range as the minimum value was significantly less than that of validation. Hence, extrapolation problems might not exist in this data set. The skewness in both calibration and validation data sets aren't drastically different. At the forecasting station, the autocorrelation and partial autocorrelation functions of water level with corresponding confidence limits were estimated up to 20 lags as proven in Fig. 4. The ACF for most successive lags was quite saturated in the drinking water level series as a sign of high persistence. The PACF shows a substantial correlation up to lag 4. Thereafter, correlations fell within the confidence limitations. In this full case, five delay water amounts sometimes (t-1) to (t-5) had been regarded as inputs. The cross correlations between drinking water amounts and rainfalls during flood time of year were also motivated to estimate the amount to which two variables are correlated. It had been found that the drinking water level was much less correlated using its at-site rainfall although the positive relation was proven up to 8 earlier rainfalls. The CCFs of five antecedent rainfalls are 0.16, 0.17, 0.16, 0.15, and 0.13, respectively, and regarded as predictor variables. The amount of input was directly dependant on the amount of lagged values to be utilized for forecasting of another value. The overall function of input-result relations for both SMLR and ANN versions are the following: Case вЂ“ 1 Ht to Ht+4 = f [ Ht-1,Ht-2, Ht-3, Ht-4, Ht-5] Case вЂ“ 2 Ht to Ht+4 = f [Ht-1,Ht-2, Ht-3, Ht-4, Ht-5, Rt-1, Rt-2, Rt-3, Rt-4, Rt-5] Where t = time (day), H = drinking water level and R = rainfall The output water amounts (H) at time.