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

被引：20

作者：

Zounemat-Kermani, Mohammad ^{[1
]}

Mahdavi-Meymand, Amin ^{[2
]}

Hinkelmann, Reinhard ^{[3
]}

机构：

[1] Shahid Bahonar Univ Kerman, Dept Water Engn, Kerman, Iran

[2] Polish Acad Sci, Inst Hydroengn, Gdansk, Poland

[3] Tech Univ Berlin, Inst Civil Engn, Chair Water Resources Management & Modeling Hydro, Berlin, Germany

来源：

EARTH SCIENCE INFORMATICS | 2021年 / 14卷 / 02期

关键词：

Machine learning; Neurocomputing; Surface hydrology; Evolutionary algorithms; Artificial intelligence; ARTIFICIAL-INTELLIGENCE; PREDICTION; STREAMFLOW; ALGORITHMS; PERCEPTRON; CLIMATE;

D O I：

10.1007/s12145-021-00599-1

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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-2 > 0.92 and RMSE < 18.6 m(3)/s. Despite the fact that the integrative neural network models slightly acted better in forecasting flow rate (mean values of R-2 > 0.94 and RMSE < 15.3 m(3)/s), they were not as computationally effective as the other applied models.

引用

页码：893 / 911

页数：19

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