Decentralized Flood Forecasting Using Deep Neural Networks

TL;DR

This paper investigates the application of deep neural networks for flood prediction, demonstrating their potential to improve forecasting accuracy and support existing hydrological models in emergency management.

Contribution

It introduces a dataset focused on river network connectivity and evaluates neural networks' effectiveness in flood time-series forecasting.

Findings

Neural networks can effectively predict flood events.

Deep learning models improve flood forecasting accuracy.

Data assimilation enhances model performance.

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.