Articles | Volume 30, issue 2
https://doi.org/10.5194/hess-30-371-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-371-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Probabilistic hierarchical interpolation and interpretable neural network configurations for flood prediction
Mostafa Saberian
Glenn Department of Civil Engineering, Clemson University, Clemson, SC, USA
Department of Agricultural Sciences, Clemson University, Clemson, SC, USA
Artificial Intelligence Research Institute for Science and Engineering (AIRISE), Clemson University, Clemson, SC, USA
Ioana Popescu
Department of Hydroinformatics and Socio-Technical Innovation, IHE Delft Institute for Water Education, Delft, the Netherlands
Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
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Rishav Karanjit, Vidya Samadi, Amanda Hughes, Pamela Murray-Tuite, and Keri Stephens
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-25, https://doi.org/10.5194/nhess-2024-25, 2024
Revised manuscript not accepted
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This research paper focused on creating a new paradigm for flood evacuation decisions – so-called human-AI Convergence (HAC) system. A Natural Language Processing (NLP) method was used to mine and filter human data from X posts that were deemed relevant to flooding. The human data along with a river hydraulic model and AI algorithms were integrated into an evacuation re-routing algorithm to forecast flood depth and define evacuation decisions.
Faisal Sardar, Muhammad Haris Ali, Ioana Popescu, Andreja Jonoski, Schalk Jan van Andel, and Claudia Bertini
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-276, https://doi.org/10.5194/hess-2023-276, 2023
Manuscript not accepted for further review
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This article analyzes surface and groundwater interactions in a small transboundary lowland catchment. The study also investigates the influence of rainfall representation in model on surface subsurface hydrological simulations. Emphasizing the significance of these interactions, the research highlighted the role of subsurface baseflow in contributing to river discharge. Despite minimal impact on streamflow, spatial variability in rainfall can cause localized fluctuations in groundwater levels.
Betina I. Guido, Ioana Popescu, Vidya Samadi, and Biswa Bhattacharya
Nat. Hazards Earth Syst. Sci., 23, 2663–2681, https://doi.org/10.5194/nhess-23-2663-2023, https://doi.org/10.5194/nhess-23-2663-2023, 2023
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We used an integrated model to evaluate the impacts of nature-based solutions (NBSs) on flood mitigation across the Little Pee Dee and Lumber River watershed, the Carolinas, US. This area is strongly affected by climatic disasters, which are expected to increase due to climate change and urbanization, so exploring an NBS approach is crucial for adapting to future alterations. Our research found that NBSs can have visible effects on the reduction in hurricane-driven flooding.
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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.
Recent progress in NN (neural network) accelerated improvements in the performance of catchment...