Velocity predictions in compound channels with vegetated floodplains using genetic programming

Abstract

Data collection and storage methods have improved vastly over recent years, however the processes of information and knowledge extraction from data have not mirrored this. The application of computer supported scientific knowledge discovery processes to carefully collected observations aims to improve the understanding of the processes that generated or produced these data. In this paper, these new techniques have been applied to the complex and poorly understood phenomena of flow through idealised vegetation. The ability to predict, with improved accuracy, velocities within wetlands and other vegetated areas would be advantageous as these regions are increasingly being recognised for their natural flood alleviation properties.

In this study, laboratory data collected in a flume with steady flows over a deep channel with relatively shallow vegetated floodplains were used to induce the formulation of expressions using a data driven discovery technique, namely genetic programming (GP). The objective of the study was not only to gain an understanding of the effect of vegetation on velocity distributions across a channel but moreover to demonstrate an alternative discovery process. The performance of the genetic program is reported for three variations of the GP. The reported results of the experiments were found to be encouraging and further work is detailed.

Keywords:

• Evolutionary computation
• hydrodynamic processes
• floodplain vegetation