Correlated Time-Series in Multi-Day-Ahead Streamflow Forecasting Using Convolutional Networks
Felipe Oliveira Barino, Vinícius N. H. Silva, Andrés Barbero, Leonardo de Mello Honório, Alexandre Bessa dos Santos
Abstract
Information about future streamflow is important for hydropower production planning, especially for damless hydro-power plants. The river flow is a reflection of various hydrological, hydrogeological, and meteorological factors, which increases the direct modeling difficulty, and favors the use of data-driven methods. In this paper, we propose the use of one-dimensional convolutional neural networks (1d-CNN) for multi-day ahead river flow forecasting and we present a multi-input model using correlated-input time-series. The proposed model was applied at the Madeira River, the Amazon's largest and most important tributary, near the Santo Antônio damless hydro-power plant. We compared the proposed correlated-input 1d-CNN to a single-input 1d-CNN model and some baseline models. Furthermore, we conclude that 1d-CNN performed better than all baseline models and that the correlated-input forecasting model is 5 times smaller than the single-input equivalent with accuracy improvements.