Abstract
Accurate rainfall forecasting is one of the most important and challenging hydrological modeling tasks with significant benefits for many sectors of the economy. This study presents novel insight into how to improve the accuracy of a new generation of stochastic monthly rainfall forecast models by examining four different preprocessing techniques: (1) time series modeling without preprocessing which is the common method in stochastic modeling as the base case, (2) preprocess using differencing, spectral analysis seasonal and non-seasonal standardization techniques, (3) two-step preprocessing including stationarization and normalization of data using 8 different transformations, and (4) two-step preprocessing, unlike scenario 3, so that the main time series was normalized and transformed to be stationary. Using the autocorrelation function and partial autocorrelation function diagrams, the parameters of the stochastic model are determined. The results indicate that the proposed data preprocessing normalization and transformation techniques can lead to major improvements in the prediction accuracy of the new monthly rainfall forecast model.
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Authors would like the acknowledge their gratitude and appreciation for the Department of Irrigation and Drainage (DID), Malaysia, for providing the rainfall dataset of the studied case study and their admirable cooperation
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Ebtehaj, I., Bonakdari, H., Zeynoddin, M. et al. Evaluation of preprocessing techniques for improving the accuracy of stochastic rainfall forecast models. Int. J. Environ. Sci. Technol. 17, 505–524 (2020). https://doi.org/10.1007/s13762-019-02361-z
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DOI: https://doi.org/10.1007/s13762-019-02361-z