| ATTRIBUTE | VALUE |
|---|
| type | A |
| database id | 9213 |
| title | A comprehensive survey on conventional and modern neural networks: application to river flow forecasting |
| authors | Zounemat-Kermani M., Mahdavi-Meymand A., Hinkelmann R/ |
| pages | 893 — 911 |
| DOI | 10.1007/s12145-021-00599-1 |
| keywords | Machine learning, Neurocomputing, Surface hydrology, Evolutionary algorithms, Artificial intelligence |
| affiliations | - Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
- Institute of Hydro-Engineering, Polish Academy of Sciences, Gdańsk, Poland
- Chair of Water Resources Management and Modeling of Hydrosystems, Institute of Civil Engineering, Technische Universität Berlin, Berlin, Germany
|
| abstracts | 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. |
| attributes | [reviewed] [scientific] |
| language | en |