Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks

Muhammed Sit; Bekir Demiray; Ibrahim Demir

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Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks

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Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks

Muhammed Sit, Bekir Demiray and Ibrahim Demir

ArXiv.org

07/07/2021

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https://arxiv.org/abs/2107.07039View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

The frequency and impact of floods are expected to increase due to climate change. It is crucial to predict streamflow, consequently flooding, in order to prepare and mitigate its consequences in terms of property damage and fatalities. This paper presents a Graph Convolutional GRUs based model to predict the next 36 hours of streamflow for a sensor location using the upstream river network. As shown in experiment results, the model presented in this study provides better performance than the persistence baseline and a Convolutional Bidirectional GRU network for the selected study area in short-term streamflow prediction.

Details

Title: Subtitle: Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks
Creators: Muhammed Sit
Bekir Demiray
Ibrahim Demir
Resource Type: Preprint
Publication Details: ArXiv.org
ISSN: 2331-8422
Number of pages: 4 pages
Comment: Accepted to Tackling Climate Change with Machine Learning workshop at ICML 2021
Language: English
Date posted: 07/07/2021
Academic Unit: Electrical and Computer Engineering; Civil and Environmental Engineering; IIHR--Hydroscience and Engineering; Injury Prevention Research Center
Record Identifier: 9984202250402771

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