Water temperature forecasting based on modified artificial neural network methods: Two cases of the Yangtze River

The Science of the Total Environment
Rujian QiuYuwei Tao

Abstract

Water temperature is a controlling indicator of river habitat since many physical, chemical and biological processes in rivers are temperature dependent. Highly precise and reliable predictions of water temperature are important for river ecological management. In this study, a hybrid model named BP_PSO3, based on the BPNN (back propagation neural network) optimized by the PSO (particle swarm optimization) algorithm, is proposed for water temperature prediction using air temperature (Ta), discharge (Q) and day of year (DOY) as input variables. The performance of the BP_PSO3 model was compared with that of the BP_PSO1 (with Ta as the input) and BP_PSO2 (with Ta and Q as the inputs) models to evaluate the importance of the inputs. In addition, a comparison among the BPNN, RBFNN (radial basis function neural network), WNN (wavelet neural network), GRNN (general regression neural network), ELMNN (Elman neural network), and BP_PSO-based models was carried out based on the MAE, RMSE, NSE and R2. The eight artificial intelligence models were examined to predict the water temperature at the Cuntan and Datong stations in the Yangtze River. The results indicated that the hybrid BPNN-PSO3 model had a stronger ability to forecast water tem...Continue Reading

References

Jan 1, 1992·IEEE Transactions on Neural Networks·Q Zhang, A Benveniste
Jan 1, 1991·IEEE Transactions on Neural Networks·D F Specht
Jun 13, 2018·PeerJ·Senlin ZhuMarijana Hadzima-Nyarko
Nov 9, 2018·Environmental Science and Pollution Research International·Senlin ZhuShiqiang Wu

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Citations

Mar 31, 2021·Environmental Science and Pollution Research International·Yue JiaTao Yang
Aug 1, 2021·BMC Medical Informatics and Decision Making·Guixiang LiKai Wu

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