Monthly precipitation prediction improving using the integrated model based on kernel-wavelet and complementary ensemble empirical mode decomposition

Document Type : Research Article

Authors

1 Civil Engineering Department, Tabriz University, Tabriz, Iran.

2 Water resource engineering and management, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

Abstract

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.

Keywords

Main Subjects

References

[1] M.j. Nazemsadat, A.A. Gamgar Haghighi, M. Sharifzadeh, M. Ahmadvand, Adoption of long-term rainfall forecasts ( studied by wheat farmers in Fars Province), Journal of Iranian Agricultural Science and Education, 22 (2006)1-15. [in Persian]
[2] P. Tofani, E. Mosaedi, A. Fakheri Fard, Precipitation forecast using wavelet theory, Water and Soil Journal (Agriculture Sciences and Technology), 25(5) (2011)1217-1226. [in Persian]
[3] H. Sharifan, B. Ghahreman, Estimation of Rain Forecast Using ARIMA Technique in Golestan Province, Journal of Agricultural Sciences and Natural Resources, 14 (2008) 13-14. [in Persian]
[4] M. Gholabi, A. Akhund, Ali, F. Radmanesh, M. Kashifipour , Comparison of Predicting the Jenkins Box Models in Seasonal Modeling (Case Study: Selected Stations in Khuzestan Province), Quarterly Journal of Geographic Research, 29(3) (2012) 61-72. [in Persian]
[5] A. S. Soltani, A. Saberi, M. Gheisouri, Determination of the best time series model for forecasting annual rainfall of selected stations of Western Azerbaijan province, Researches in Geographical Sciences, 17(44) (2017) 87105.
[6] ASCE, Task Committee on Application of Artificial Neural Networks in Hydrology, Artificial Neural Networks in hydrology. I: Preliminary concepts, Hydrological Engineering, ASCE. 5(2) (2000) 115-123.
[7] C. Siviapragasam, S. Liong, Rainfall and runoff forcasting with SSA-SVM approach, Hydroinformation, 3(2001) 141-152.
[8] K. Roushangar, R. Ghasempour, The study of the performance of classical and artificial intelligence methods in the estimation of roughness coefficients in pontoons, Irrigation and Drainage Journal of Iran, 12(4) (2019) 811-822. [in Persian]
[9] O. Kisi, M. Cimen, Precipitation forecasting by using wavelet-support vector machine conjunction model, Engineering Application Artificial Intelligence, 25 (2012) 783–792.
[10] F.S. Marzano, E. Fionda, P. Ciotti, Neural-network approach to ground- based passive microwave estimation of precipitation intensity and extinction,  Hydrology, 328 (2006) 121–131.
[11] Z. Razzaghzadeh, V. Nourani, N. Behfar, The conjunction of feature extraction method with AI-based ensemble statistical downscaling models, Amirkabir Journal of Civil Engineering, DOI: 10.22060/ceej.2018.14986.5806, (2018). [in Persian]
[12] S. Kumar, D. Tripathy, S. Nayak, S. Mohaparta, Prediction of rainfall in India using artificial neural network models, International Journal of intelligent system and applications, 12 (2013) 1-22.
[13] D. Nayak, A. Mahapatra, P. Mishra, A survey on rainfall prediction using artificial neural network, International journal of computer applications, 72(16) (2013) 32-40.
[14] K.M. Lau, H.Y. Weng, Climate signal detection using wavelet transform, How to make time series sing, Bull Am Meteorol Soc, 76 (1995) 2391-2402.
[15] K. Adamowski, A. Prokoph, J. Adamowski, Development of a new method of wavelet aided trend detection and estimation, Hydrology Process, 23(18) (2009) 2686–2696.
[16] C.M. Chou, Complexity analysis of rainfall and runoff time series based on sample entropy in different temporal scales, Stochastic Environmental Research and Risk Assessment, 6 (2011) 1401–1408.
[17] Y. Amirat, M. Benbouzidb, T. Wang, K. Bacha, G. Feld, EEMD-based notch filter for induction machine bearing faults detection, Applied Acoustics, 133 (2018)  202–209.
[18] Z. Wu, N.F. Huang, A study of the characteristics of white noise using the empirical mode decomposition method, Proc RS Lond 460A: 1597–1611, (2004).
[19] C.E. Rasmussen, C.K.I. Williams, Gaussian Processes for Machine Learning. The MIT Press, Cambridge, MA, (2006).
[20] W.C. Dawson, R. Wilby, An artificial neural network approach to rainfall-runoff modelling. Hydrological Sciences Journal, 43(1) (1998) 47-66.
[21] NOAA Earth System Research laboratory, https://www.esrl.noaa.gov/psd/data/climateindices/list/, (2009).
 
Amirkabir Journal of Civil Engineering
Volume 52, Issue 10
January 2021
Pages 2649-2660

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