Parameter estimation of a nonlinear hydrologic model for channel flood routing with the bat algorithm

Abstract

Flood routing is a methodology to predict the changes of the flow of water as it moves through a natural river, an artificial channel, or a reservoir. It is widely used in fields such as flood prediction, reservoir design, geographic planning, and many others. One of the most popular and widely used flood routing techniques is the Muskingum model, as it is conceptually simple and only depends on a few parameters that can be estimated from historical inflow/outflow records. However, the estimation of such parameters for the nonlinear case is still a challenging task. In this paper we present a method based on a powerful swarm intelligence technique called bat algorithm to solve the parameter estimation problem of the nonlinear Muskingum model for channel routing. The method is applied to an illustrative example used as a benchmark in the field with very good results. We also show that our method outperforms other state-of-the-art methods in the field such as PSO.