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Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application

Kalteh, Aman Mohammad LU ; Hjorth, Peder LU and Berndtsson, Ronny LU (2008) In Environmental Modelling & Software 23(7). p.835-845
Abstract
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... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
artificial neural networks, self-organizing map, water resources, review
in
Environmental Modelling & Software
volume
23
issue
7
pages
835 - 845
publisher
Elsevier
external identifiers
  • wos:000254966700001
  • scopus:40749144865
ISSN
1364-8152
DOI
10.1016/j.envsoft.2007.10.001
language
English
LU publication?
yes
id
89af6307-7d64-4335-a1f9-aa96599ce4a2 (old id 1206442)
date added to LUP
2008-09-19 14:06:10
date last changed
2017-11-05 03:38:29
@article{89af6307-7d64-4335-a1f9-aa96599ce4a2,
  abstract     = {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.},
  author       = {Kalteh, Aman Mohammad and Hjorth, Peder and Berndtsson, Ronny},
  issn         = {1364-8152},
  keyword      = {artificial neural networks,self-organizing map,water resources,review},
  language     = {eng},
  number       = {7},
  pages        = {835--845},
  publisher    = {Elsevier},
  series       = {Environmental Modelling & Software},
  title        = {Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application},
  url          = {http://dx.doi.org/10.1016/j.envsoft.2007.10.001},
  volume       = {23},
  year         = {2008},
}