Decentralized Flood Forecasting Using Deep Neural Networks

This is a Preprint and has not been peer reviewed. This is version 4 of this Preprint.


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Muhammed Sit , Ibrahim Demir


Predicting flood for any location at times of extreme storms is a longstanding problem that has utmost importance in emergency management. Conventional methods that aim to predict water levels in streams use advanced hydrological models still lack of giving accurate forecasts everywhere. This study aims to explore artificial deep neural networks performance on flood prediction. While providing models that can be used in forecasting stream stage, this paper presents a dataset that focuses on the connectivity of data points on river networks. It also shows that neural networks can be very helpful in time-series forecasting as in flood events, and support improving existing models through data assimilation.



Civil and Environmental Engineering, Engineering, Environmental Engineering, Life Sciences, Other Life Sciences


machine learning, Neural Networks, flood forecasting, flood prediction, gated recurrent units, stream height prediction


Published: 2019-06-22 18:28

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GNU Lesser General Public License (LGPL) 2.1

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