| 摘要: |
| 针对一些水库水质监测数据序列不仅具有平稳性、周期性，而且具有显著的多尺度性的特点，在单一自回归模型的基础上，利用多尺度小波分析的原理与方法对水质数据序列作预处理，进行分解与重构，并对重构的不同尺度下的数据子序列分别建立高、低频自回归预测模型，最后叠加各尺度下的预测结果。将该方法应用于梁辉水库4种水质指标的预测研究，结果表明与单一自回归模型相比，预测精度有明显提高。 |
| 关键词: 水库 水质预测 平稳时间序列 小波分解 自回归 |
| DOI： |
| 分类号:TV213.4；X832 |
| 基金项目: |
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| A wavelet autoregressive model and its application to water quality forecast |
|
CHEN Ji-jiang
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|
Water Conservancy Bureau of Yuyao City
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| Abstract: |
| In general, water quality time series not only has stationary and periodicity characteristics, but also has obvious multi-scale features. To improve the precision of the traditional autoregressive models, which were once widely used for forecasting water quality, the autoregressive model combined with the multi-scale wavelet analysis theory is proposed as a new forecasting model called WAR(Wavelet Autoregressive Model ). Finally, this new method and the traditional autoregressive model are applied to predict four water quality indicators in the Lianghui reservoir. The analysis results show that the WAR model has significantly improved the prediction accuracy in comparison with the traditional autoregressive model. At the same time it is also found that the model is feasible and practical, and can only provide references for similar studies. In view of this, when we predict water quality, in order to improve the prediction accuracy, it is important to choose the model according to actual situations, and this point is crucial. |
| Key words: reservoir water quality forecast stationary time series wavelet decomposition autoregressive model |
