Review
Version 1
Preserved in Portico This version is not peer-reviewed
Review of Machine Learning Methods for River Flood Routing
Version 1
: Received: 30 December 2023 / Approved: 30 December 2023 / Online: 3 January 2024 (03:41:55 CET)
A peer-reviewed article of this Preprint also exists.
Li, L.; Jun, K.S. Review of Machine Learning Methods for River Flood Routing. Water 2024, 16, 364. Li, L.; Jun, K.S. Review of Machine Learning Methods for River Flood Routing. Water 2024, 16, 364.
Abstract
River flood routing computes changes in shape of a flood wave over time as it travels downstream along a river. Conventional flood routing models, especially hydrodynamic models require high quality and quantity of input data such as measured hydrologic time series, geometric data, hydraulic structures and hydrological parameters. Unlike physically based models, machine learning algorithms, which are data driven models, do not require much knowledge about underlying physical processes and can identify complex nonlinearity between inputs and outputs. Due to the higher performance, less complexity, and low computation cost, novel machine learning methods as a single application or hybrid application were introduced by researchers to achieve more accurate and efficient flood routing. This paper reviews the recent application of machine learning methods in river flood routing.
Keywords
machine learning; river flood routing; hydrologic model; hydrodynamic model
Subject
Engineering, Civil Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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