EVOLUTIONARY COMPUTING IN HYDROLOGY
Many hydrologic processes are believed to be highly complex, nonlinear, time-varying, and spatially distributed. Hence, the governing mechanisms are not easily described by simple models. With unprecedented growth in instrumentation technology, recent investigations in hydrology are supported with immense quantities of data. In order to take full advantage of the information contained in such data, scientists are increasingly relying on a suite of data-driven techniques to understand the complex hydrologic processes. Evolutionary computing (EC) techniques, with a host of optimization and modeling tools, can contribute significantly to achieve the objectives of this knowledge-discovery exercise in hydrology. This chapter discusses the utility of these EC techniques in attempting data analysis and modeling problems associated with hydrologic systems. It introduces the concept and working principle of EC techniques in general and reviews their applications to different domains of hydrology. The study also illustrates different case studies of utilizing ‘genetic programming’ (GP) technique as a modeling, data assimilation, and model emulation tool.