Data pre-processing effect on ANN-based prediction intervals construction of the evaporation process at different climate regions in Iran

Abstract

The point predictions of stochastic processes, such as evaporation, by data-driven methods such as Artificial Neural Network (ANN), are associated with uncertainties. Furthermore, the performance of data-driven models, as well as their uncertainty, are dependent on the quality and quantity of the used data. The main aim of this research was the Uncertainty Quantifying (UQ) of the ANN-based evaporation predictions by Prediction Intervals (PIs) analysis using the data from three stations in Iran (i.e. Tabriz, Urmia, and Ardabil). In this way, data pre-processing methods i.e. Wavelet-based De-noising (WD), training with Jitted Data (JD) and also their combination i.e. Hybrid Wavelet De-noising and Jitted Data (HWDJD) were applied to examine their effects on the estimated values of PIs. The Lower-Upper-Bound Estimation (LUBE) method as the direct NN-based PI construction was utilized for estimating the PI values. Since the efficiency of any ANN model and consequently, the robustness of the uncertainty analysis is sensitive to the correct selection of input variables, the first order Partial Derivation (PaD) sensitivity analysis method was also used to select dominant inputs among all potential input variables. The results indicated that the LUBE technique could provide acceptable results in estimating the uncertainty bounds, whereas the uncertainty quantity would be affected by the used data pre-processing methods. The reduction of uncertainty effect via data pre-processing methods was significant in modeling Urmia and Ardabil stations. Results showed that the reduction of PI bandwidths via HWDJD, WD, and JD methods were up to 30%, 21%, and 9%, respectively. It means that to reduce the ANN-based modeling uncertainty due to the uncertainty involved in the input data set, at first, the contaminant noise of the available data should be eliminated from the time series, then artificially generated time series (JD) that mimic the smoothed time series pattern can be generated and used in the training process.