Articles | Volume 30, issue 2
https://doi.org/10.5194/hess-30-371-2026
https://doi.org/10.5194/hess-30-371-2026
Research article
 | 
26 Jan 2026
Research article |  | 26 Jan 2026

Probabilistic hierarchical interpolation and interpretable neural network configurations for flood prediction

Mostafa Saberian, Vidya Samadi, and Ioana Popescu

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Cited articles

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Short summary
Recent progress in NN (neural network) accelerated improvements in the performance of catchment modeling. Yet flood modeling remains a very difficult task. Focusing on two headwater streams, we developed N-HiTS (Neural Hierarchical Interpolation for Time Series Forecasting) and N-BEATS (Network-Based Expansion Analysis for Interpretable Time Series Forecasting) models and benchmarked them with LSTM (long short-term memory) to predict flooding. N-HiTS and N-BEATS outperformed LSTM for flood predictions. We demonstrated how the proposed models can be augmented with an uncertainty approach to predict flooding that is interpretable without considerable loss in accuracy.
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