Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application
Author
Summary, in English
The use of artificial neural networks (ANNs) in problems related to water resources has received steadily increasing interest over the last decade or so. The related method of the self-organizing map (SOM) is an unsupervised learning method to analyze, cluster, and model various types of large databases. There is, however, still a notable lack of comprehensive literature review for SOM along with training and data handling procedures, and potential applicability. Consequently, the present paper aims firstly to explain the algorithm and secondly, to review published applications with main emphasis on water resources problems in order to assess how well SOM can be used to solve a particular problem. It is concluded that SOM is a promising technique suitable to investigate, model, and control many types of water resources processes and systems. Unsupervised learning methods have not yet been tested fully in a comprehensive way within, for example water resources engineering. However, over the years, SOM has displayed a steady increase in the number of applications in water resources due to the robustness of the method.
Department/s
Publishing year
2008
Language
English
Pages
835-845
Publication/Series
Environmental Modelling & Software
Volume
23
Issue
7
Document type
Journal article review
Publisher
Elsevier
Topic
- Water Engineering
Keywords
- artificial neural networks
- self-organizing map
- water resources
- review
Status
Published
ISBN/ISSN/Other
- ISSN: 1364-8152