Precipitation Forecast in Yellow River Delta Based on Nonlinear Multi-scale Mode

Abstract

【Background】 Precipitation is closely related to human production, life and ecology. The change of precipitation is related to the sustainable utilization of regional water resources, the protection of ecological environment and the development of economy and society, The research on the variation characteristics and evolution trend of precipitation has become a hot topic in the field of climate and water resources. Scholars and researchers has paid much attention on the accurate prediction of precipitation. 【Objective】 The purpose of this paper is to improve the prediction accuracy of precipitation, and reflect the actual characteristics of precipitation. 【Method】 Based on the advantages of empirical mode decomposition in the analysis and processing of nonlinear time series and other fields, Empirical Mode Decomposition (EMD) was carried out for the monthly average precipitation data of the Yellow River Delta Meteorological Station from 1954 to 2018, and a series of eigenmode functions were obtained. Hilbert transform was performed on IMF, and on this basis, two multi-scale forecast models of precipitation in the Yellow River Delta were established. 【Result】 The results showed that there were periods of 9, 13, 23, 76 and 135 months in precipitation in the Yellow River Delta, and 9-month fluctuations were the main ones; The 65-year monthly average precipitation data was predicted. The relative error of the model 1 was between 0.9% and 9.8%, and the relative error of the model 2 was between 1.6% and 11.8%. When modeling, the average prediction error of model 1 without considering the initial phase was 2.70%, and the overall prediction accuracy was better than that of model 2 considering the initial phase. 【Conclusion】 The fitting accuracy and significance of the two models meet the requirements.

Keywords

• precipitation
• time series
• multiscale
• emd
• predict