Research on the Filtration Process of Water Plants Based on RNN
The current drinking water standards are constantly improving,and traditional water quality prediction methods have obvious shortcomings.A study is conducted on the use of Recurrent Neural Networks(RNNs)for processing time series data.The analysis mainly focuses on annual data,summer data and winter data,and the results show that the optimal model MSE and MAE models for annual data are 0.004 7 and 0.054 1,respectively.The optimal model MSE and MAE for winter data are 0.005 1 and 0.054 4,respectively.The simulation effect of summer data is relatively poor,with the lowest MSE and MAE values of 0.285 9 and 0.470 4.It shows that when RNN is used to predict a large amount of water quality data,its simulation is effective and the fitting accuracy is high.However,when simulating models with small amounts of data and complex data situations,the fitting effect is not very good.
filtration water quality predictionRecurrent Neural Networkstime seriesoptimal model
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