Journal of Hydroinformatics· 2026Q2
Advancing long-horizon hydrological forecasting: a Mamba-based approach with explainable AI for generalized streamflow prediction
- 2citations
- Q2SCImago
- 2026year
Short summary
A Mamba-based model achieved streamflow forecasting accuracy comparable to, and in some aspects exceeding, Transformers across 125 Iowa watersheds for 120-h predictions, with explainable AI revealing distinct feature utilization patterns.
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Key points
- Mamba architecture achieved comparable or superior 120-h streamflow forecasting accuracy to Transformers across 125 Iowa watersheds.
- Mamba and Transformer models significantly outperformed Persistence, LSTM, GRU, and Seq2Seq models.
- Explainable AI (SHapley Additive exPlanations) revealed distinct feature utilization patterns, enhancing model transparency.
- The study benchmarks Mamba against multiple established hydrological forecasting models using Nash–Sutcliffe efficiency, Kling–Gupta efficiency, Pearson's r, and normalized root-mean-square error.
AI-generated from the title and abstract; the full text is not read.
Abstract
ABSTRACT Accurate long-horizon streamflow forecasting is crucial for water resource management, but existing models often face efficiency and interpretability challenges. This study comprehensively evaluates the Mamba architecture, which utilizes state-space models (SSMs) for efficient sequence processing, for 120-h hourly generalized streamflow prediction across 125 diverse Iowa watersheds using 72-h historical inputs. Performance was benchmarked against Persistence, long short-term memory, gated recurrent unit, Seq2Seq, and Transformer models employing Nash–Sutcliffe efficiency, Kling–Gupta efficiency, Pearson's r, and normalized root-mean-square error. Results demonstrate that Mamba architecture achieves predictive accuracy comparable to, and in several aspects marginally exceeding, the robust Transformer baseline, with both models significantly outperforming other established methods. Critically, explainable AI usingSHapley Additive exPlanations values provided insights into tested models' decision-making, revealing distinct feature utilization patterns and enhancing model transparency. This research highlights Mamba's potential as an efficient, accurate, and interpretable alternative for advancing operational long-range hydrological forecasting.
The authors' abstract, as published at the source. Journal of Hydroinformatics, 2026 · DOI ↗
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Environmental EngineeringEnvironmental Science