| Research on the annual rainfall prediction for irrigation area based on the EMD and IDAR |
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| DOI：10.7606/j.issn.1000-7601.2014.03.033 |
| Key Words: empirical mode decomposition(EMD) information diffusion approximate reasoning(IDAR) annual precipitation prediction model irrigation area |
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| Abstract: |
| According to rainfall such a nonlinear, non-stationary sequence, this paper carried out a research combining empirical mode decomposition(EMD) and information diffusion approximate reasoning method (IDAR) method to solve the problem on annual precipitation forecast under the condition of insufficient data material sequence. First, the annual precipitation time series with typical nonlinear and non-stationary feature was made data processing by the EMD method. Some sub-data series including different scale features were decomposed out from the original material. Then we applied the information diffusion approximate reasoning technology to describe the nonlinear relation within precipitation component, and established a series of prediction rules between adjacent years based on the current trend for forecasting. In this paper a long irrigation precipitation data was as the samples for calculation, and its computing result was compared with other prediction method. The
contrast results showed that: The comprehensive forecast method based on IDAR and EMD was highly effective when precipitation forecasting. The sum of absolute values of the result error was only 1.34, which was better than other statistical results by artificial neural network, linear regression method and original information diffusion approximate reasoning. At the same time, this method has b
een used in the rainfall forecast in Wenyu River irrigation district, has obtained satisfactory results. In the research process, we also have found: The information diffusion approximate reasoning technology can convert a sample points to a fuzzy set, which can partly offset the information shortage due to sample data insufficient to a certain extent. Furthermore, the fuzzy treatment in IDAR also can convert the problem from contradiction mode into compatibility mode. The EMD method can effectively decompose precipitation sequence with nonlinear and non-stationary characteristics, and retain various distribution rule in space (or time) scales within their original sequence. The combination of IDAR and EMD methods is very valuable for forecast problems of the incomplete and non-stationary sequence. Through the comparison with other forecast methods, it find that the combination model has high forecasting precision and application value, due to it well smoothing the sample data and exploring information knowledge. |
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