Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting - Rahimzad, Maryam; Moghaddam Nia, Alireza; Zolfonoon, Hosam; Soltani, Jaber; Danandeh Mehr, Ali; Kwon, Hyun-Han (Water resources management, 2021)
Keywords: Algorithms; Atmospheric Sciences; Civil Engineering; Deep learning; Earth and Environmental Science; Earth Sciences; Environment; Errors; Flood forecasting; Flood management; Forecasting; Geotechnical Engineering & Applied Earth Sciences; High flow; Hydrogeology; Hydrology; Hydrology/Water Resources; Kentucky; Kentucky River; Learning algorithms; Long short-term memory; Low flow; Machine learning; Mathematical models; Maximum flow; Mitigation; Multilayer perceptrons; neural networks; regression analysis; Resource allocation; River basins; Statistical analysis; Statistical methods; Stream discharge; Stream flow; Streamflow forecasting; Support vector machines; time series analysis; water; Water resources; watersheds; Kentucky River; Kentucky; Data-driven modeling; Deep learning; Machine learning algorithms; Streamflow forecasting; LSTM