Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1029/2025GL118317 |
Licence  |
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| Title (Primary) | The value of forecasters-in-the-loop in real-time flood forecasting in the age of machine learning |
| Author | Tran, V.N.; Xu, D.; Le, P.; Kim, J.; Nguyen, V.T.
; Nguyen, G.T.; Restrepo, P.J.; Ivanov, V.Y. |
| Source Titel | Geophysical Research Letters |
| Year | 2026 |
| Department | HDG |
| Volume | 53 |
| Issue | 8 |
| Page From | e2025GL118317 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Data and Software links | https://doi.org/10.5281/zenodo.15066778 |
| Supplements | Supplement 1 |
| Keywords | real-time flood forecasting; forecasters-in-the-loop; machine learning |
| Abstract | Machine learning (ML) applications in hydrological
forecasting are increasingly prevalent and show great potential.
However, many previous studies have only evaluated performance through
reanalysis or retrospective simulations compared to simplified
baselines. This study provides the first assessment of ML performance
against actual operational forecasting systems operated by the
California Nevada River Forecast Center (CNRFC), which combines the
Community Hydrologic Prediction System (CHPS) with
forecasters-in-the-loop. Results demonstrate that
forecasters-in-the-loop systems consistently outperform ML models in
both general forecasts and flood alerting across lead times up to 96 hr,
even when ML models use observed forcings, while CNRFC operational
process relies on biased weather forecasts. Our analysis reveals that
forecaster expertise maintains forecast reliability despite inaccurate
precipitation inputs, with human-guided systems showing superior
performance degradation characteristics at extended lead times. These
findings highlight the irreplaceable value of human expertise in
operational forecasting and caution against overstating current ML
capabilities in real-world applications. |
| Tran, V.N., Xu, D., Le, P., Kim, J., Nguyen, V.T., Nguyen, G.T., Restrepo, P.J., Ivanov, V.Y. (2026): The value of forecasters-in-the-loop in real-time flood forecasting in the age of machine learning Geophys. Res. Lett. 53 (8), e2025GL118317 10.1029/2025GL118317 |
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