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| 基于集合经验模态分解和支持向量机的溶解氧预测 |
| DO Prediction Based on Ensemble Empirical Mode Decomposition and Support Vector Machine |
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| DOI： |
| 中文关键词: 集合经验模态分解 支持向量机 溶解氧预测 相关分析 |
| 英文关键词: Ensemble empirical mode decomposition Support vector machine Dissolved oxygen prediction Correlation analysis |
| 基金项目:教育部人文社科研究基金资助项目（17YJC630003）；重庆市社会科学规划基金资助项目 (2016BS081)；重庆市教委科学技术研究基金资助项目（KJ1706175） |
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| 中文摘要: |
| 应用集合经验模态分解（EEMD）和支持向量机（SVM）相结合的方法，建立一种天然水体溶解氧浓度预测模型。首先，利用EEMD方法将溶解氧时序分解成不同频段的分量，以降低序列的非平稳性；然后，根据各序列分量的自身特征建立合适的SVM预测模型，此过程通过相关分析确定各分量输入量；最后，将各子分量预测值合成得到最终的预测结果。使用该模型对嘉陵江北温泉段的溶解氧浓度进行预测，结果表明，与传统单一的SVM和BP神经网络模型相比，该模型能有效提高预测精密度，具有良好的应用前景。 |
| 英文摘要: |
| A prediction model of dissolved oxygen (DO) concentration in natural water was established by a combination of ensemble empirical mode decomposition (EEMD) and support vector machine (SVM). Firstly, DO series were decomposed into several components with different frequency bands by EEMD to reduce the series instability. Secondly, an appropriate prediction model was built for each component of the sequence according to its own characteristics, and the input variables of each component were determined by correlation analysis method. Finally, the predicted value of each component was composed to get the final result. Taking the water quality monitoring sites in the north hot spring reach of Jialing River as an example for DO concentration prediction, results showed that the model had better generalization ability and a good application prospect compared with the traditional single SVM and back propagation neural network. |
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