Amirkabir University of Technology Amirkabir Journal of Civil Engineering 2588-297X 52 10 2020 12 21 Monthly precipitation prediction improving using the integrated model based on kernel-wavelet and complementary ensemble empirical mode decomposition Monthly precipitation prediction improving using the integrated model based on kernel-wavelet and complementary ensemble empirical mode decomposition 2649 2660 3603 10.22060/ceej.2019.16043.6109 FA Kiyoumars Roushangar Civil Engineering Department, Tabriz University, Tabriz, Iran. Roghayeh Ghasempour Water resource engineering and management, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran Journal Article 2019 03 23 Estimates of monthly rainfall are important for various purposes such as flood estimation, drought, irrigation planning, and river basin management. In the present study, the monthly rainfall of Tabriz station was investigated using the intelligent Gaussian Process Regression (GPR) method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Wavelet Transform (WT). Different models were defined based on teleconnection patterns and climatic elements, and the impact of different input parameters was assessed. The obtained results proved high capability and efficiency of the applied method in predicting the monthly precipitation. The results showed that time series decomposition based on wavelet transformation led to more accurate outcomes compared to the complementary ensemble empirical mode decomposition. The best evaluation of test series using wavelet transform decomposition was obtained for the state of modeling based on teleconnection patterns and climatic elements with the values of DC=0.889, R=0.961 and RMSE=0.036. Also, based on the sensitivity analysis, Pt-3 was found to be the most effective parameter in modeling. Estimates of monthly rainfall are important for various purposes such as flood estimation, drought, irrigation planning, and river basin management. In the present study, the monthly rainfall of Tabriz station was investigated using the intelligent Gaussian Process Regression (GPR) method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Wavelet Transform (WT). Different models were defined based on teleconnection patterns and climatic elements, and the impact of different input parameters was assessed. The obtained results proved high capability and efficiency of the applied method in predicting the monthly precipitation. The results showed that time series decomposition based on wavelet transformation led to more accurate outcomes compared to the complementary ensemble empirical mode decomposition. The best evaluation of test series using wavelet transform decomposition was obtained for the state of modeling based on teleconnection patterns and climatic elements with the values of DC=0.889, R=0.961 and RMSE=0.036. Also, based on the sensitivity analysis, Pt-3 was found to be the most effective parameter in modeling. https://ceej.aut.ac.ir/article_3603_6a8018b3a00b69c008601b8becae392b.pdf