Improving short-term streamflow forecasting by flow mode clustering

Runoff prediction with high accuracy is vital to protect people’s properties and lives. In this study, a Short-Term Streamflow Forecasting Framework combining Long Short-Term Memory (LSTM) with Self-Organizing Map (SOM) is proposed, which clusters hydro-meteorological factors through SOM network to...

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Veröffentlicht in:	Stochastic environmental research and risk assessment 2023-05, Vol.37 (5), p.1799-1819
Hauptverfasser:	Liu, Shuqi, Zhou, Xinzhi, Li, Bo, He, Xin, Zhang, Yuexin, Fu, Yi
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Aquatic Pollution Chemistry and Earth Sciences Clustering Computational Intelligence Computer Science Earth and Environmental Science Earth Sciences Environment Forecasting Hydrometeorology Lead time Long short-term memory Math. Appl. in Environmental Science Mathematical models Modelling Original Paper Physical characteristics Physical properties Physics Predictions Probability Theory and Stochastic Processes River basins Runoff Self organizing maps Statistics for Engineering Stream discharge Stream flow Streamflow forecasting Waste Water Technology Water Management Water Pollution Control
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