Flood routing by linear Muskingum method using two basic floods data using particle swarm optimization (PSO) algorithm

Abstract

The Muskingum method is one of hydrological approaches that has been used for flood routing for many years thanks to its simplicity and reasonable accuracy over other methods. In engineering works, the calculation of the Peak section of a flood hydrograph is crucially important. In the present study, using the Particle Swarm Optimization (PSO) algorithm, instead of using a single basic flood, the parameters of the linear Muskingum method (X, K, Δt) are calculated by computed arithmetic and geometric means relevant to two basic floods in the form of eight different models for calculating the downstream hydrograph. The results indicate that if the numerical values of the calculated flood inflow are placed in the interval of the inflow and the basic flood which the parameters X, K, Δt are from, the computation accuracy in approximating the outflow flood related to the peak section of the inflow hydrograph increases for all the mentioned models. In other words, if the arithmetic mean of X, K and the geometric mean of Δt, relevant to the two basic floods, are used instead of using values of X, K, Δt of a single basic flood, the computational accuracy in estimating the flood peak section of the hydrograph in downstream has the highest increase among all the eight models. Thus, the Mean Relative Error (MRE) relevant to the peak section of the inflow hydrograph of the third flood (observational flood) obtained by the first and second basic floods was equal to 4.89% and 2.91%, respectively, while in case of using the arithmetic mean of X and K and the geometric mean of Δt, related to the first and second basic floods (the best models presented in this study), this value is equal to 1.66%.

Multi-Spatial Resolution Rainfall-Runoff Modelling—A Case Study of Sabari River Basin, India

“…The Clark's unit hydrograph (UH) method [53] was adopted to compute the direct runoff. An exponential recession model was employed to estimate baseflow, and the Muskingum method [54] was used for the channel flow routing.…”

Investigation on the Drag Coefficient of the Steady and Unsteady Flow Conditions in Coarse Porous Media

“…Particle swarm optimization (PSO) algorithm is a population-based evolutionary algorithm and is used in civil engineering and water resources optimization problems such as reservoir performance (Nagesh Kumar and Janga Reddy, 2007), water quality management (Afshar et al 2011, Lu et al 2002, Chau, 2005 and optimization of the Muskingum method coefficients (Chu and Chang 2009, Moghaddam et al 2016, Bazargan and Norouzi 2018, Norouzi and Bazargan 2020, Norouzi and Bazargan 2021. Therefore, in the present study, the particle swarm optimization (PSO) algorithm was used to optimize the coefficients of the Forchheimer binomial equation (Eq.…”

Investigation on the Drag Coefficient of the Steady and Unsteady Flow Conditions in Coarse Porous Media

“…Particle swarm optimization (PSO) algorithm is a population-based evolutionary algorithm and is used in civil engineering and water resources optimization problems such as reservoir performance (Nagesh Kumar and Janga Reddy, 2007), water quality management (Afshar et al 2011, Lu et al 2002, Chau, 2005 and optimization of the Muskingum method coefficients (Chu and Chang 2009, Moghaddam et al 2016, Bazargan and Norouzi 2018, Norouzi and Bazargan 2020.…”