A Hybrid Model of Empirical Wavelet Transform and Extreme Learning Machine for Dissolved Oxygen Forecasting

• Published in: 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) pp. 651 - 656
• Main Authors: Cao, Weijian, Gu, Yuwan, Huan, Juan, Qin, Yilin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2018
• Subjects: dissolved oxygen; empirical wavelet transform; hybrid model; water quality prediction
• DOI: 10.1109/Cybermatics_2018.2018.00132

The accurate predicting trend of dissolved oxygen (DO) can reduce the risk of aquaculture, so a combined non-linear prediction model based on empirical wavelet transform (EWT) and extreme learning machine (ELM) optimized by particle swarm optimization (PSO) is proposed. First of all, DO series are decomposed into a term of relatively subsequence by EWT, Secondly, the decomposed components are reconstructed using the C-C method, and thirdly an ELM prediction model of every component is established. At last, the predicted values of DO datasets are calculated by using RBF to reconstruct the forecasting values of all components. This model is tested in the special aquaculture farm in Liyang City, Jiangsu Province. Results indicate that the proposed prediction model of EWT-ELM has good performance than WD-ELM, EMD-ELM, ELM and EWT-BP. The research shows that the combined forecasting model can effectively extract the sequence characteristics, and can provide a basis for decision-making management of water quality, which has certain application value.