Prediction of River Water Quality Based on Two-Layer Decomposition Method And Extreme Learning Machine
Aiming at the problem that chemical oxygen demand and permanganate index in urban river water qual-ity can not be effectively predicted,a river water quality prediction method based on a two-layer decomposition meth-od and extreme learning machine is proposed.Improved complete ensemble EMD(ICEEMDAN)was introduced to decompose the original water quality time series.The sub-components were divided into high-frequency components and low-frequency components.Variational mode decomposition(VMD)was added to decompose the high-frequency components twice,and extreme learning machine(ELM)and least squares support vector machine(LSSVM)prediction models for all components were established.The final prediction result was obtained by adding the predicted values of each component linearly.Taking the water quality data set of Suzhou watercourses as a sample,this method obtains the mean absolute error(MAE),the root mean square error(RMSE)and the mean absolute per-centage error(MAPE)of chemical oxygen demand and permanganate index are 0.076,0.158,2.674%and 0.182,0.193,0.673%respectively.The robustness of this method in identifying and analyzing key water quality parameters is better than other comparison models,which overcomes the problem of mode aliasing in the reconstruction process of ensemble empirical mode decomposition(EEMD).Especially in the case of high-frequency components,it can be predicted more effectively.
River water quality predictionImproved complete ensemble EMDVariational mode decompositionExtreme Learning machineFrequency division processing
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