Assessing the Impact of Rainfall Nowcasts on an Encoder-Decoder LSTM Model for Short-Term Flash Flood Prediction

Rim Mhedhbi () and Marina G. Erechtchoukova ()
Additional contact information
Rim Mhedhbi: York University
Marina G. Erechtchoukova: York University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 4, No 7, 1623-1638

Abstract: Abstract Flash floods pose significant threats as immediate and highly destructive natural hazards. Extending the forecast horizon of flash flood prediction models has been a key objective to enable timely warning or other mitigating measures. The integration of precipitation predictions into data-driven flash flood models remains unexplored. In this study, we propose an Encoder-Decoder LSTM-based model architecture for short-term flash flood prediction, which incorporates short-term rainfall forecasts and evaluates the influence of the associated uncertainty on these predictions. We conducted three sets of experiments to predict flash flood occurrences within a watershed with a 30-minute response time. The first set employed a baseline LSTM model without rainfall forecast integration. The second one utilized a proposed encoder-decoder LSTM model that incorporated accurate rainfall forecasts. Lastly, the third set of experiments introduced errors into the rainfall forecasts to evaluate the impact of forecast uncertainty on flood prediction. Computational experiments demonstrate that incorporating accurate rainfall nowcasts significantly enhances flash flood predictability, with F1-score improvements ranging from 10 to 60%, depending on the hydrological year. Furthermore, even when errors in rainfall magnitude and timing were introduced, overall the proposed framework outperformed models that did not use rainfall forecasts, delivering reliable predictions for up to two hours.

Keywords: LSTM Model; Encoder-Decoder Architecture; Flash Flood Prediction; Rainfall Nowcasting; Forecast Uncertainty (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-024-04037-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:39:y:2025:i:4:d:10.1007_s11269-024-04037-x

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-024-04037-x

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().