Insights into the forecasting capability of monthly precipitation via ensemble EEMD-DSE-KELM method

Precipitation plays an important role in determining the climate of a region. Accurate precipitation forecasting is an important factor in flood estimation, drought monitoring, irrigation planning, and river basin management. In this study, an integrated forecasting approach based on Ensemble Empirical Mode Decomposition-Differential Symbolic Entropy (EEMD-DSE) and Kernel Extreme Learning Machine (KELM) was applied to enhance the accuracy of monthly precipitation forecasting and reduce the complexity of hybrid forecasting process. In this regard, at first, the original time series was decomposed to several Inherent Mode Functions (IMFs) via EEMD method. Then, DSE was applied to analyze the complexity and reconstruct the subseries. The reconstructed subseries were used as inputs for KELM method. The obtained results proved high capability and efficiency of the applied integrated method in modeling the monthly precipitation. The results showed that time series decomposition based on EEMD-DSE improved models accuracy up to ۵۰% and ۱۵% compared to the single KELM and EEMD-KELM methods. The best evaluation criteria for test series using integrated approach was DC=۰.۸۷۸, R=۰.۹۱۰, and RMSE=۰.۱۲۵. Therefore, it could be deduced that the proposed forecasting model in this study is an effective forecasting model

Keywords:

Differential Symbolic Entropy , Integrated models , Kernel Extreme Learning Machine , Mode components , Precipitation.