Artificial Neural Networks in Hydrology. I: Preliminary Concepts

TLDR

Artificial neural networks are gaining traction in hydrology, but remain in early stages as the community begins to recognize their potential as an alternative modeling tool. This paper serves as an introductory guide to ANNs for hydrologists, outlining their concepts and practical use. The authors describe ANN fundamentals, provide usage guidelines, compare them with other hydrologic modeling philosophies, and discuss their strengths, limitations, and similarities to physical models.

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 potential 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.