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A comprehensive survey on conventional and modern neural networks: application to river flow forecasting.
- Source :
- Earth Science Informatics; Jun2021, Vol. 14 Issue 2, p893-911, 19p
- Publication Year :
- 2021
-
Abstract
- 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 R<superscript>2</superscript> > 0.92 and RMSE < 18.6 m<superscript>3</superscript>/s. Despite the fact that the integrative neural network models slightly acted better in forecasting flow rate (mean values of R<superscript>2</superscript> > 0.94 and RMSE < 15.3 m<superscript>3</superscript>/s), they were not as computationally effective as the other applied models. [ABSTRACT FROM AUTHOR]
- Subjects :
- STREAMFLOW
ARTIFICIAL neural networks
FORECASTING
Subjects
Details
- Language :
- English
- ISSN :
- 18650473
- Volume :
- 14
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Earth Science Informatics
- Publication Type :
- Academic Journal
- Accession number :
- 150234325
- Full Text :
- https://doi.org/10.1007/s12145-021-00599-1