River Stage Forecasting using Enhanced Partial Correlation Graph

Siva R Venna, Satya Katragadda, Vijay Raghavan and Raju Gottumukkala ()
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Siva R Venna: University of Louisiana at Lafayette
Satya Katragadda: University of Louisiana at Lafayette
Vijay Raghavan: University of Louisiana at Lafayette
Raju Gottumukkala: University of Louisiana at Lafayette

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 12, No 12, 4126 pages

Abstract: 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.

Keywords: River stage forecasting; River networks; ARIMA; Graph analysis; Timeseries forecasting; Partial correlation graph (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11269-021-02933-0

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