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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / AAAI-21 Special Programs and Special Track

Forecasting Reservoir Inflow via Recurrent Neural ODEs

February 1, 2023

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Authors

Fan Zhou

School of Information and Software Engineering, University of Electronic Science and Technology of China


Liang Li

School of Information and Software Engineering, University of Electronic Science and Technology of China


DOI:

10.1609/aaai.v35i17.17763


Abstract:

Forecasting reservoir inflow is critical for making many policies, ranging from flood control and agriculture irrigation to water ecology management, hydropower generation, and landslide prevention. Prior studies mainly exploit autoregressive models -- e.g., recurrent neural networks (RNN) and its many variants -- to model the flow time series's temporal pattern. However, existing approaches rely on regular and accurate inflow observations, which either fail to predict multi-scale inflow (e.g., an hour, a day, or a month ahead prediction) or ignore the uncertainty of observations due to confounding factors such as snowmelt and precipitation. To address the limitations, we propose a novel inflow forecasting model by incorporating the uncertainty of the observations into the RNN model and the continuous-time dynamics of the latent states with neural ordinary differential equations (ODE). Our method, called FlowODE, explicitly encodes the stochasticity of hidden conditions in addition to the temporal dependencies among inflow observations. Moreover, FlowODE explores a continuum of layers instead of discrete RNNs to model the hidden states' dynamics, allowing us to infer the inflow at any time horizon flexibly. We conduct extensive experiments on the real-world datasets collected from two large-scale hydropower dams. The results show that our method consistently outperforms previous inflow forecasting models while providing adaptable predictions and a flexible balance between prediction accuracy and computational cost.

Topics: AAAI

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HOW TO CITE:

Fan Zhou||Liang Li Forecasting Reservoir Inflow via Recurrent Neural ODEs Proceedings of the AAAI Conference on Artificial Intelligence (2021) 15025-15032.

Fan Zhou||Liang Li Forecasting Reservoir Inflow via Recurrent Neural ODEs AAAI 2021, 15025-15032.

Fan Zhou||Liang Li (2021). Forecasting Reservoir Inflow via Recurrent Neural ODEs. Proceedings of the AAAI Conference on Artificial Intelligence, 15025-15032.

Fan Zhou||Liang Li. Forecasting Reservoir Inflow via Recurrent Neural ODEs. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.15025-15032.

Fan Zhou||Liang Li. 2021. Forecasting Reservoir Inflow via Recurrent Neural ODEs. "Proceedings of the AAAI Conference on Artificial Intelligence". 15025-15032.

Fan Zhou||Liang Li. (2021) "Forecasting Reservoir Inflow via Recurrent Neural ODEs", Proceedings of the AAAI Conference on Artificial Intelligence, p.15025-15032

Fan Zhou||Liang Li, "Forecasting Reservoir Inflow via Recurrent Neural ODEs", AAAI, p.15025-15032, 2021.

Fan Zhou||Liang Li. "Forecasting Reservoir Inflow via Recurrent Neural ODEs". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.15025-15032.

Fan Zhou||Liang Li. "Forecasting Reservoir Inflow via Recurrent Neural ODEs". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 15025-15032.

Fan Zhou||Liang Li. Forecasting Reservoir Inflow via Recurrent Neural ODEs. AAAI[Internet]. 2021[cited 2023]; 15025-15032.


ISSN: 2374-3468


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