Citation:
Gauch, M. , Tang, R. , Mai, J. , Tolson, B. , Gharari, S. , & Lin, J. . (2019). Machine Learning for Streamflow Prediction: Current Status and Future Prospects. AGU Fall Meeting Abstracts, 2019, H33L-2127.
Abstract:
Accurate streamflow prediction is an open challenge in hydrology. We show that approaches based on machine learning can provide more accurate predictions than physically-based models and discuss potential for improvement in hybrid approaches.
