Yiqi Liu
ABSTRACT
Biochemical oxygen demand (BOD) is one of the principal indicators to evaluate wastewater effluent qualities. Establishing an effective prediction model is one of important way to monitor sewage water quality properly. However, due to the nonlinearity, time-varying and large delay of sewage treatment process, the traditional back-propagation neural network (BPNN) and Elman neural network (ElmanNN) models are often prone to fall into local optimization, then resulting in poor prediction accuracy when dealing with high-dimension and complex data structure. Therefore, this paper proposes a VIP-PSO-Elman model. In the proposed model, the partial least square (PLS) method is able to extract hidden information by variable importance projection (VIP) and then used for variable selection. Finally, the PSO algorithm is implemented to optimize Elman network connection weights and thresholds to achieve the global optimal solution. The proposed model was validated by a data set from University of California database (UCI). The results showed that the model has good performance in root mean square error (RMSE) and correlation coefficient (R).
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Index Terms
- Prediction of Biochemical Oxygen Demand Based on VIP-PSO-Elman Model in Wastewater Treatment
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