Review of Machine Learning Methods for River Flood Routing

Water, Journal Year: 2024, Volume and Issue: 16(2), P. 364 - 364

Published: Jan. 22, 2024

River flood routing computes changes in the shape of a wave over time as it travels downstream along river. Conventional models, especially hydrodynamic require high quality and quantity input data, such measured hydrologic series, geometric hydraulic structures, hydrological parameters. Unlike physically based machine learning algorithms, which are data-driven do not much knowledge about underlying physical processes can identify complex nonlinearity between inputs outputs. Due to their higher performance, lower complexity, low computation cost, researchers introduced novel methods single application or hybrid achieve more accurate efficient routing. This paper reviews recent river

Language: Английский

Evaluating the Accuracy of Machine Learning, Deep Learning and Hybrid Algorithms for Flood Routing Calculations

Pure and Applied Geophysics, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 26, 2024

Language: Английский

Flood modeling using HEC-RAS 2D and IBER 2D: a comparative study

Water Science & Technology Water Supply, Journal Year: 2024, Volume and Issue: 24(9), P. 3061 - 3076

Published: Aug. 8, 2024

ABSTRACT Floods have become a major risk in urban areas. Identifying areas at of flooding has crucial to reducing this and protecting lives property. Various softwares are used identify flooding. In work, comparative study was made between two well-known software flood modeling, HEC-RAS 2D IBER 2D. The comparison included water depth, flow velocity, the extent El Bayadh City Algeria for return periods 50, 100, 1,000 years. Despite existence some differences results depths velocities compared, showed good agreement softwares. gave higher values than three estimating provided velocity estimation. flooded almost identical, relative deviation compared varies 1 3%. A map produced most vulnerable This can be help mitigate

Language: Английский