Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application A.M. Kalteh * , P. Hjorth, R. Berndtsson Department of Water Resources Engineering, Lund University, P. O. Box 118, S-221 00 Lund, Sweden Received 13 October 2006; received in revised form 3 October 2007; accepted 4 October 2007 Available online 19 November 2007 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. How- ever, over the years, SOM has displayed a steady increase in the number of applications in water resources due to the robustness of the method. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Artificial neural networks; Self-organizing map; Review; Water resources 1. Introduction Modelling of hydrological processes that are embedded with high complexity, dynamism, and non-linearity in both spatial and temporal scales is of prime importance for hydrologists and water resources engineers. In many cases, however, the lack of physical understanding of the complex processes in- volved creates problems to find efficient models. Over the last decades artificial neural networks (ANNs) have been subject to an increasing interest in water resources problems. This has led to a tremendous surge in research activities (ASCE, 2000b; Maier and Dandy, 2000; Dawson and Wilby, 2001; Alp and Cigizoglu, 2007; Darsono and Labadie, 2007; Iliadis and Maris, 2007; Raduly et al., 2007). The increasing number of applications of ANNs in modelling of hydrological processes is related to their ability to relate input and output variables in complex systems without any requirement of a detailed under- standing of the physics of the process involved (Dawson and Wilby, 2001). According to ASCE (2000a,b), an ANN is a mas- sively parallel-distributed information processing system re- sembling biological neural networks of the human brain and capable of solving large-scale complex problems such as pat- tern recognition, non-linear modelling, classification, and con- trol. The feed-forward multi-layer perceptron (MLP) is the most widely used ANN for prediction and forecasting of water resources variables (Maier and Dandy, 2000). Detailed reviews of ANNs along with assessments of their application in water resources and hydrology can be found in Maier and Dandy (2000), ASCE (2000a,b), and Dawson and Wilby (2001). The self-organizing map (SOM; also called Kohonen map or topol- ogy preserving feature map) is a kind of ANN method which is capable of clustering, classification, estimation, prediction, and data mining (Alhoniemi et al., 1999; Vesanto and Alhoniemi, 2000; Kohonen, 2001) in a wide-spread range of disciplines regarding signal recognition, organization of large collections * Corresponding author. Present address: Department of Forestry, Faculty of Natural Resources, Guilan University, P.O. Box 1144, Sowmehe Sara, Guilan, Iran. E-mail address: aman_mohammad.kalteh@tvrl.lth.se (A.M. Kalteh). 1364-8152/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.envsoft.2007.10.001 Available online at www.sciencedirect.com Environmental Modelling & Software 23 (2008) 835e845 www.elsevier.com/locate/envsoft