A comprehensive survey on conventional and modern neural networks: application to river flow forecasting.

This study appraises different types of conventional (e.g., GRNN, RBNN, & MLPNN) and modern neural networks (e.g., integrative, inclusive, hybrid, & recurrent) in forecasting daily flow in the Thames River located in the United Kingdom. The models are mathematically, statistically, and diagnostically compared based on the forecasted results for ten different time-series assortments. The results indicate that all the neural network models acceptably forecasted the daily flow rate, with mean values of R² > 0.92 and RMSE < 18.6 m³/s. Despite the fact that the integrative neural network models slightly acted better in forecasting flow rate (mean values of R² > 0.94 and RMSE < 15.3 m³/s), they were not as computationally effective as the other applied models. [ABSTRACT FROM AUTHOR]