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

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 temperature under both normal and extreme drought conditions. Optimization by the PSO algorithm and the inclusion of Q and DOY could help capture river thermal dynamics more accurately. The findings of this study could provide scientific references for river water temperature forecasting and river ecosystem protection.