Review of Machine Learning Methods for River Flood Routing

Abstract

River flood routing computes changes in the shape of a flood wave over time as it travels downstream along a river. Conventional flood routing models, especially hydrodynamic models, require a 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 their higher performance, lower complexity, and low computation cost, researchers introduced novel machine learning methods as a single application or hybrid application to achieve more accurate and efficient flood routing. This paper reviews the recent application of machine learning methods in river flood routing.

Explicit Scheme for a Hydrological Channel Routing: Mathematical Model and Practical Application

“…Several adaptations of the Muskingum-Cunge method have been proposed, considering continuous flow and modified inflows [19,20]. Recently, machine learning techniques have been integrated to enhance distributed routing in rivers [21,22]. Linear and nonlinear analysis using the lumped routing method of Muskingum was analyzed [23,24].…”