Application example of neural networks for time series analysis: rainfall-runoff modeling
Signal Processing - Special issue on neural networks
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Environmental Modelling & Software
Artificial neural networks for rapid WWTP performance evaluation: Methodology and case study
Environmental Modelling & Software
Environmental Modelling & Software
Process Monitoring and Modeling Using the Self-Organizing Map
Integrated Computer-Aided Engineering
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Review of the Self-Organizing Map (SOM) approach in water resources: Commentary
Environmental Modelling & Software
Environmental Modelling & Software
A data-driven approach for modeling post-fire debris-flow volumes and their uncertainty
Environmental Modelling & Software
Environmental Modelling & Software
Mathematics and Computers in Simulation
Data-driven modeling approaches to support wastewater treatment plant operation
Environmental Modelling & Software
The self-organizing map tree (SOMT) for nonlinear data causality prediction
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Modelling of water quality: an application to a water treatment process
Applied Computational Intelligence and Soft Computing
A hybrid artificial intelligence sales-forecasting system in the convenience store industry
Human Factors in Ergonomics & Manufacturing
Data-driven modeling of surface temperature anomaly and solar activity trends
Environmental Modelling & Software
Conceptual evaluation of continental land-surface model behaviour
Environmental Modelling & Software
Hybrid modeling of spatial continuity for application to numerical inverse problems
Environmental Modelling & Software
Environmental Modelling & Software
Forecasting conditional climate-change using a hybrid approach
Environmental Modelling & Software
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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.