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<p>Accurate long-term reservoir storage forecasting is essential for sustainable water management in semi-arid regions. This study presents a horizon-adaptive benchmarking framework integrating physics-informed deep learning and classical time-series models for 12-month-ahead prediction. Five models—PIT-T, LSTM, SARIMAX, MLP, and an Ensemble—were trained on 12-month lags and evaluated across 12 horizons. PIT-T achieved the lowest 1-month RMSE (≈ 2.0 hm³) and best short-term memory preservation, MLP excelled at 2–6 months, LSTM dominated 7–10 months, and the Ensemble was most stable at 11–12 months, maintaining seasonal persistence. Robustness tests under reduced complexity and noise confirmed that PIT-T and Ensemble models retained accuracy (RMSE ≈ 5.5–5.8 hm³, NSE ≈ 0.22–0.29). These results show that no single model is optimal across all horizons, underscoring the need for horizon-specific model selection as a reliable, physically consistent approach for adaptive reservoir management in data-scarce basins.</p>

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A Horizon-Adaptive Benchmarking Framework for Long-Term Reservoir Storage Forecasting Using Physics-Informed Transformers and Machine Learning

  • 期刊论文

  • 发表日期：

    2025年12月29日

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  • Ekinhan Eriskin,
  • Özlem Terzi,
  • Emine Dilek Taylan

摘要

Accurate long-term reservoir storage forecasting is essential for sustainable water management in semi-arid regions. This study presents a horizon-adaptive benchmarking framework integrating physics-informed deep learning and classical time-series models for 12-month-ahead prediction. Five models—PIT-T, LSTM, SARIMAX, MLP, and an Ensemble—were trained on 12-month lags and evaluated across 12 horizons. PIT-T achieved the lowest 1-month RMSE (≈ 2.0 hm³) and best short-term memory preservation, MLP excelled at 2–6 months, LSTM dominated 7–10 months, and the Ensemble was most stable at 11–12 months, maintaining seasonal persistence. Robustness tests under reduced complexity and noise confirmed that PIT-T and Ensemble models retained accuracy (RMSE ≈ 5.5–5.8 hm³, NSE ≈ 0.22–0.29). These results show that no single model is optimal across all horizons, underscoring the need for horizon-specific model selection as a reliable, physically consistent approach for adaptive reservoir management in data-scarce basins.