Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers

The rising incidence of droughts in specific global regions in recent years, primarily attributed to global warming, has markedly increased the demand for reliable and accurate streamflow estimation. Streamflow estimation is essential for the effective management and utilization of water resources,...

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Vydané v:	Atmosphere Ročník 16; číslo 1; s. 106
Hlavní autori:	Achite, Mohammed, Katipoğlu, Okan Mert, Kartal, Veysi, Sarıgöl, Metin, Jehanzaib, Muhammad, Gül, Enes
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.01.2025
Predmet:	Accuracy Algorithms Artificial intelligence Artificial neural networks Back propagation Climate change Computational intelligence Correlation coefficient Correlation coefficients Deep learning Drought Environmental aspects Error analysis Floods Forecast accuracy Global warming GMDH Group method of data handling Humidity Hydroelectric power Hydrology Life on Earth Long short-term memory Machine learning Monthly forecasts Mountains Neural networks Performance evaluation Precipitation prediction Quality of life Recurrent neural networks Regression analysis Rivers Soft computing Statistical analysis Stream discharge Stream flow Streamflow Streamflow estimation Streamflow forecasting Vegetation Water resources Wavelet transforms Turkey Algeria Iran China
ISSN:	2073-4433, 2073-4433
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