Decentralized Flood Forecasting Using Deep Neural Networks

Muhammed Sit; Ibrahim Demir

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Decentralized Flood Forecasting Using Deep Neural Networks

Preprint

Open access

Decentralized Flood Forecasting Using Deep Neural Networks

Muhammed Sit and Ibrahim Demir

ArXiv.org

02/06/2019

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https://arxiv.org/abs/1902.02308View

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

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.

Details

Title: Subtitle: Decentralized Flood Forecasting Using Deep Neural Networks
Creators: Muhammed Sit
Ibrahim Demir
Resource Type: Preprint
Publication Details: ArXiv.org
ISSN: 2331-8422
Language: English
Date posted: 02/06/2019
Academic Unit: Electrical and Computer Engineering; Civil and Environmental Engineering; IIHR--Hydroscience and Engineering; Injury Prevention Research Center
Record Identifier: 9984202143502771

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