2004Unpublished venueRequires access

Rainfall-Runoff Modelling

Robert Abrahart, P.E. Kneale, Linda M. See

Open publisher page 9 citations

Abstract

In this chapter we look at the position of rainfall-runoff models in the broader context of the already wide choice of models for hydrological forecasters. Why should a modeller choose this approach? The last decade has seen the gradual introduction of informatic tools, such as artificial neural networks (NN), into hydrology, hydrogeology and water resources planning and management. The applications of these techniques have been many and various, but a broad appreciation of their potential has been slow to develop. One reason for the apparent reluctance to consider such an approach may be unfamiliarity with the concept of, and nomenclature associated with, NN. Essentially, NN are excellent computational devices for pattern recognition and classification; it is their origins in artificial intelligence and cognitive sciences that have given rise to what is unfamiliar terminology in the hydrological sciences.

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What this paper is about

In this chapter we look at the position of rainfall-runoff models in the broader context of the already wide choice of models for hydrological forecasters. Why should a modeller choose this approach? The last decade has seen the gradual introduction of informatic tools, such as artificial neural networks (NN), into hydrology, hydrogeology and water resources planning and management. The applications of these techniques have been many and various, but a broad appreciation of their potential has been slow to develop. One reason for the apparent reluctance to consider such an approach may be unfamiliarity with the concept of, and nomenclature associated with, NN. Essentially, NN are excellent computational devices for pattern recognition and classification; it is their origins in artificial intelligence and cognitive sciences that have given rise to what is unfamiliar terminology in the hydrological sciences.

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Available abstract

In this chapter we look at the position of rainfall-runoff models in the broader context of the already wide choice of models for hydrological forecasters. Why should a modeller choose this approach? The last decade has seen the gradual introduction of informatic tools, such as artificial neural networks (NN), into hydrology, hydrogeology and water resources planning and management. The applications of these techniques have been many and various, but a broad appreciation of their potential has been slow to develop. One reason for the apparent reluctance to consider such an approach may be unfamiliarity with the concept of, and nomenclature associated with, NN. Essentially, NN are excellent computational devices for pattern recognition and classification; it is their origins in artificial intelligence and cognitive sciences that have given rise to what is unfamiliar terminology in the hydrological sciences.

Key concepts: Environmental science, Surface runoff, Hydrology (agriculture), Geology, Geotechnical engineering, Ecology, Biology

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