IoT-integrated deep learning for forecasting and decision support in reservoir water management under drought conditions
This study presents an IoT-enabled forecasting and decision-support framework for proactive reservoir management under drought conditions. Using more than two decades of high-resolution hydrometeorological data, we develop and compare Long Short-Term Memory (LSTM) and extended LSTM (xLSTM) models. T...
| Autores: | , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) |
| Repositorio: | r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) |
| OAI Identifier: | oai:cttc.fundanetsuite.com:p8790 |
| Acesso em linha: | https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8790 |
| Access Level: | acceso abierto |
| Palavra-chave: | Internet of Things (IoT) Deep learning Long short-term memory (LSTM) Extended LSTM (xLSTM) Water reservoir forecasting Hydrological modeling Drought preparedness Climate change adaptation |
| Resumo: | This study presents an IoT-enabled forecasting and decision-support framework for proactive reservoir management under drought conditions. Using more than two decades of high-resolution hydrometeorological data, we develop and compare Long Short-Term Memory (LSTM) and extended LSTM (xLSTM) models. The xLSTM integrates exponential gating mechanisms to better capture long-range temporal dependencies. We evaluate predictive performance across multiple forecasting horizons (30, 90, 180, and 365 days) and benchmark the results against a classical statistical model (ARIMA). The xLSTM consistently outperforms baseline models in short-term forecasts but exhibits a decline in accuracy at longer horizons, highlighting the limitations of purely data-driven approaches for extended predictions. To operationalize model outputs, we integrate the forecasts into a real-time decision-support dashboard that aligns predictions with reservoir operation thresholds established in the Catalan Drought Management Plan. This research provides both a methodological contribution to deep learning for hydrological forecasting and a practical framework for data-driven drought preparedness in climate-sensitive regions. |
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