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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1206442
- author
- Kalteh, Aman Mohammad
LU
; Hjorth, Peder
LU
and Berndtsson, Ronny
LU
- organization
- publishing date
- 2008
- 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
- 2016-04-01 12:05:00
- date last changed
- 2022-11-09 22:16:35
@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}},
keywords = {{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}},
doi = {{10.1016/j.envsoft.2007.10.001}},
volume = {{23}},
year = {{2008}},
}