| Cite this article: | 穆玉珠.基于非线性多尺度模型的黄河三角洲降水量预测[J].灌溉排水学报,0,():-. |
| muyuzhu.基于非线性多尺度模型的黄河三角洲降水量预测[J].灌溉排水学报,0,():-. |
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| DOI： |
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| In yellow River delta Precipitation Forecast Bsed on Nonlinear Multi-scale Model |
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muyuzhu
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Xinxiang Bureau of Hydrology and Water Resources Survey
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| Abstract: |
| 【Background】Under the influence of climate change and human activities, precipitation in various regions has also changed. 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 are very concerned about the accurate prediction of precipitation. 【Objective】In order to improve the prediction accuracy of precipitation, 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 is performed on IMF, and on this basis, two multi-scale forecast models of precipitation in the Yellow River Delta are established. 【Result】The results of the study show that there are periods of September, 13, 23,76 and 135 months in precipitation in the Yellow River Delta, and 9-month fluctuations are the main ones; the 65-year monthly average precipitation data is predicted, Results show, The relative error of the model 1 is between 0.9% and 9.8%, and the relative error of the model 2 prediction is 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. |
| Key words: precipitation; time series; multiscale; EMD; predict |
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