Estimation of 3-Parameter and 4-Parameter Values of Non-Linear Muskingum Model Using Bat Algorithm

Abstract

Flood Routing is widely used technique to determine changes being observed in speed, shape and magnitude of flood wave with time as water flows down the reservoir, stream or river. The most commonly used Hydrological Model I.e. Muskingum Model rely on some parameters that is to be determined from data. Several Optimization Algorithm have been Applied for this Evaluation. Among them, Nature-Inspired Algorithm those based on swarm intelligence approach is gaining attention in recent years due to their robust performance and faster convergence rate. In the present study, Bat Algorithm based on velocity and high ability of living bat to perceive sound and detect prey is used to optimize parameter values of Muskingum Model. The case study of Wilson flood and Karahan flood were selected to calculate 3-parameter and 4-parameter values of Non-Linear Muskingum Model respectively with the objective to analyses the efficiency of this Algorithm. Comparative analysis showed that Bat Algorithm achieved best flood routing accuracy with less computational time compared to optimization Algorithm- Particle swarm optimization algorithm.