Pastrana-Cortés, J.D.; Gil-Gonzalez, J.; Álvarez-Meza, A.M.; Cárdenas-Peña, D.A.; Orozco-Gutiérrez, Á.A. Scalable and Interpretable Forecasting of Hydrological Time-Series based on Variational Gaussian Processes. Preprints2024, 2024041492. https://doi.org/10.20944/preprints202404.1492.v1
APA Style
Pastrana-Cortés, J.D., Gil-Gonzalez, J., Álvarez-Meza, A.M., Cárdenas-Peña, D.A., & Orozco-Gutiérrez, Á.A. (2024). Scalable and Interpretable Forecasting of Hydrological Time-Series based on Variational Gaussian Processes. Preprints. https://doi.org/10.20944/preprints202404.1492.v1
Chicago/Turabian Style
Pastrana-Cortés, J.D., David Augusto Cárdenas-Peña and Álvaro Angel Orozco-Gutiérrez. 2024 "Scalable and Interpretable Forecasting of Hydrological Time-Series based on Variational Gaussian Processes" Preprints. https://doi.org/10.20944/preprints202404.1492.v1
Abstract
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.
Keywords
Autoregressive models; Gaussian Process; Long Short Term Memory networks; Multi-task forecasting; Predictive Distribution; Streamflow Contributions
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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