Evaluating the impact of improved filter-wrapper input variable selection on long-term runoff forecasting using local and global climate information

•Improved golden jackal optimization (IGJO) considers complementary of local and global information.•LSTM-IGJO have the greatest enhancement in runoff prediction, achieving a NSE of 0.92.•IGJO retained critical inputs for variables selection, especially local weather variables.•IGJO extracted more r...

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Vydané v:	Journal of hydrology (Amsterdam) Ročník 644; s. 132034
Hlavní autori:	Yang, Binlin, Chen, Lu, Yi, Bin, Li, Siming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.11.2024
Predmet:	algorithms Canis aureus case studies climate Global climate indices Improved binary metaheuristic algorithm Local weather variables Long-term runoff forecasting rivers runoff weather Wrapper input variable selection Global climate indices Wrapper input variable selection Local weather variables Improved binary metaheuristic algorithm Long-term runoff forecasting
ISSN:	0022-1694
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