Decentralized Flood Forecasting Using Deep Neural Networks

Muhammed Sit; Ibrahim Demir

Decentralized Flood Forecasting Using Deep Neural Networks

Machine Learning 2019-06-25 v2 Machine Learning

Authors: Muhammed Sit , Ibrahim Demir

Abstract

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.

Keywords

time series forecasting disaster response deep neural networks

Cite

@article{arxiv.1902.02308,
  title  = {Decentralized Flood Forecasting Using Deep Neural Networks},
  author = {Muhammed Sit and Ibrahim Demir},
  journal= {arXiv preprint arXiv:1902.02308},
  year   = {2019}
}

Comments

7 pages, 5 figures

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