Standard Article

22 Evolutionary Computing in Hydrological Sciences

Part 2. Hydroinformatics

  1. Dragan Savic,
  2. Soon-Thiam Khu

Published Online: 15 APR 2006

DOI: 10.1002/0470848944.hsa016

Encyclopedia of Hydrological Sciences

Encyclopedia of Hydrological Sciences

How to Cite

Savic, D. and Khu, S.-T. 2006. Evolutionary Computing in Hydrological Sciences. Encyclopedia of Hydrological Sciences. 2:22.

Author Information

  1. University of Exeter, Centre for Water Systems, School of Engineering, Computer Science and Mathematics, Exeter, UK

Publication History

  1. Published Online: 15 APR 2006

Abstract

With the advent of data acquisition techniques and data processing capabilities (computers + algorithms), evolutionary computing (EC) has found its way into a wide range of applications in hydrological science as demonstrated in this article. These applications range from the use of EC to assist in the understanding of hydrological processes, to improve the performance of simulation models for almost all aspects of hydrologic applications, and most recently, to quantify risk and uncertainty with the aim of providing decision support.

In this article, an attempt is made to provide a detailed review of the applications of EC in different hydrological science topics. References are divided into six main categories: hydrologic processes research, rainfall-runoff modeling, reservoir operations and control, groundwater systems, urban water systems, and water quality studies. These are broad categorizations, and many applications of EC span across multiple groupings.

Evolutionary Computing is a continuously and rapidly growing area of academic research for computer science, mathematics, economics and management, engineering, physical science, and many others. As such, it is futile to try to provide a comprehensive overview of the currently available EC tools and techniques and therefore, no such attempt is made in this article.

Keywords:

  • evolutionary computing;
  • evolutionary algorithms;
  • genetic programming;
  • genetic algorithms;
  • hydrological science;
  • water resources systems