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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #119079
Title: ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY I: PRELIMINARY CONCEPTS

Author
item GOVINDARAJU, R - PURDUE UNIV
item RAO, A - PURDUE UNIV
item LEIB, DAVID - KANSAS WATER OFFICE
item NAJJAR, Y - KANSAS STATE UNIV
item GUPTA, H - UNIV OF ARIZONA
item Hjelmfelt Jr, Allen

Submitted to: Journal Hydrologic Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/20/1999
Publication Date: N/A
Citation: N/A

Interpretive Summary: The first of a two part final report by the Am. Soc. Civil Engineers, Task Committee on applications of Artificial Neural Networks in Hydrology covers the fundamentals needed to carry out applications. This tool from artificial intelligence has been highly touted, but its applicability to hydrology not determined. The task committee was charged with making such an assessment. The results are described in Part II. This research will benefit water resources engineers and planners who will use it in analyzing the effects of improved practices on the response of a watershed to rainfall events.

Technical Abstract: In this two-part series, the writers investigate the role of artificial neural networks (ANNs) in hydrology. ANNs are gaining popularity, as is evidenced by the increasing number of papers on this topic appearing in hydrology journals, especially over the last decade. In terms of hydrologic applications, this modeling tool is still in its nascent stages. .The practicing hydrologic community is just becoming aware of the potentia of ANNs as an alternative modeling tool. This paper is intended to serve as an introduction to ANNs for hydrologists. Apart from descriptions of various aspects of ANNs and some guidelines on their usage, this paper offers a brief comparison of the nature of ANNs and other modeling philosophies in hydrology. A discussion on the strengths and limitations of ANNs brings out the similarities they have with other modeling approaches, such as the physical model.