BOKU - Universität für Bodenkultur Wien - Forschungsinformationssystem

Logo BOKU-Forschungsportal

Gewählte Publikation:

Kratzert, F; Klotz, D; Herrnegger, M; Sampson, AK; Hochreiter, S; Nearing, GS.
(2019): Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning
WATER RESOUR RES. 2019; 55(12): 11344-11354. FullText FullText_BOKU

Abstract:
Long short-term memory (LSTM) networks offer unprecedented accuracy for prediction in ungauged basins. We trained and tested several LSTMs on 531 basins from the CAMELS data set using k-fold validation, so that predictions were made in basins that supplied no training data. The training and test data set included similar to 30 years of daily rainfall-runoff data from catchments in the United States ranging in size from 4 to 2,000 km(2) with aridity index from 0.22 to 5.20, and including 12 of the 13 IGPB vegetated land cover classifications. This effectively "ungauged" model was benchmarked over a 15-year validation period against the Sacramento Soil Moisture Accounting (SAC-SMA) model and also against the NOAA National Water Model reanalysis. SAC-SMA was calibrated separately for each basin using 15 years of daily data. The out-of-sample LSTM had higher median Nash-Sutcliffe Efficiencies across the 531 basins (0.69) than either the calibrated SAC-SMA (0.64) or the National Water Model (0.58). This indicates that there is (typically) sufficient information in available catchment attributes data about similarities and differences between catchment-level rainfall-runoff behaviors to provide out-of-sample simulations that are generally more accurate than current models under ideal (i.e., calibrated) conditions. We found evidence that adding physical constraints to the LSTM models might improve simulations, which we suggest motivates future research related to physics-guided machine learning.
Autor/innen der BOKU Wien:
Herrnegger Mathew
Klotz Daniel
Kratzert Frederik Fiete
BOKU Gendermonitor:


Find related publications in this database (Keywords)
prediction in ungauged basins
machine learning
CAMELS
LSTM


Altmetric:
© BOKU Wien Impressum