Abstract
Various time series forecasting methods have been successfully applied for the water-stage forecasting problem. Graphical time series models are a class of multivariate time series to model the spatio-temporal dependencies between the sensors. Constructing graph-based models involve data pre-processing and correlation analysis to capture the dynamics of different water flow scenarios, which is not scalable for a large network of sensors. This paper presents a novel approach to model spatio-temporal dependencies across river network stations using a partial correlation graph. We also provide a method to enrich this partial correlation graph by eliminating the spurious correlations. We demonstrate the utility of enriched partial correlation graphs in multivariate forecasting for various scenarios and state-of-the-art multivariate forecasting models. We observe that the forecasting techniques that use information from the enriched partial correlation graph outperform standard time series forecasting approaches for river network forecasting.
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Data Availability
The data used for this experiment is available at https://github.com/SatyaKatragadda/RiverStageForecasting
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Funding
This project is funded by NSF grant CNS-1429526
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Problem formulation: S.V.; Conceptualization: S.V.; Methodology: S.V., R.G., and V.R.; Software: S.V. and S.K.; Validation: S.K. and S.V.; Formal analysis: S.K., V.R., and S.V.; Resources: R.G.; Data curation: S.K. and S.V.; Writing–original draft preparation: S.V., R.G., and S.K.; Writing–review and editing: S.K. and R.G.; Visualization: S.K.; Supervision: R.G.; Project administration: R.G.; All authors reviewed the manuscript.
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Venna, S.R., Katragadda, S., Raghavan, V. et al. River Stage Forecasting using Enhanced Partial Correlation Graph. Water Resour Manage 35, 4111–4126 (2021). https://doi.org/10.1007/s11269-021-02933-0
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DOI: https://doi.org/10.1007/s11269-021-02933-0