โ Scribed by ASCE Task Committee on Application of Artificial Neural Networks in Hydrology
No coin nor oath required. For personal study only.
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.
๐ SIMILAR VOLUMES
The implementation of artificial neural networks into hardware will only show their true potential since the networks need full parallel processing for real-time applications. By this way neural networks are a genuine challenge to microelectronics: not only many synapses have to be integrated with a