Scalable and Interpretable Forecasting of Hydrological Time Series Based on Variational Gaussian Processes

Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models...

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Bibliographic Details
Published in:Water (Basel) 2024-07, Vol.16 (14), p.2006
Main Authors: Pastrana-Cortés, Julián David, Gil-Gonzalez, Julian, Álvarez-Meza, Andrés Marino, Cárdenas-Peña, David Augusto, Orozco-Gutiérrez, Álvaro Angel
Format: Article
Language:English
Subjects:
Aquatic resources
Colombia
Comparative analysis
Decision making
Electric power production
Forecasting
Gaussian processes
Hydroelectric power
Hydrology
Management
Parameter estimation
Stream flow
Streamflow
Time series
Water
Water resources
Water-power
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Summary:Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models often suffer from complexity and require extensive parameterization. Data-driven models like Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) networks offer simplicity and performance but cannot quantify uncertainty. This work introduces Sparse Variational Gaussian Processes (SVGPs) for forecasting streamflow contributions. The proposed SVGP model reduces computational complexity compared to traditional Gaussian Processes, making it highly scalable for large datasets. The methodology employs optimal hyperparameters and shared inducing points to capture short-term and long-term relationships among reservoirs. Training, validation, and analysis of the proposed approach consider the streamflow dataset from 23 geographically dispersed reservoirs recorded during twelve years in Colombia. Performance assessment reveals that the proposal outperforms baseline Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) models in three key aspects: adaptability to changing dynamics, provision of informative confidence intervals through Bayesian inference, and enhanced forecasting accuracy. Therefore, the SVGP-based forecasting methodology offers a scalable and interpretable solution for multi-output streamflow forecasting, thereby contributing to more effective water resource management and hydroelectric planning.
ISSN:2073-4441
2073-4441
DOI:10.3390/w16142006