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

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.

[1]  Drasko Furundzic,et al.  Application example of neural networks for time series analysis: : Rainfall-runoff modeling , 1998, Signal Process..

[2]  Holger R. Maier,et al.  Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river , 2005 .

[3]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[4]  N. Null.  Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

[5]  A. W. Minns,et al.  The classification of hydrologically homogeneous regions , 1999 .

[6]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[7]  Amin Elshorbagy,et al.  Spiking modular neural networks: A neural network modeling approach for hydrological processes , 2006 .

[8]  Ronny Berndtsson,et al.  Interpolating monthly precipitation by self-organizing map (SOM) and multilayer perceptron (MLP) , 2007 .

[9]  John W. Labadie,et al.  Neural-optimal control algorithm for real-time regulation of in-line storage in combined sewer systems , 2007, Environ. Model. Softw..

[10]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[11]  Ashu Jain,et al.  Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques , 2006 .

[12]  Juha Vesanto,et al.  SOM-based data visualization methods , 1999, Intell. Data Anal..

[13]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[14]  Gwo-Fong Lin,et al.  Identification of homogeneous regions for regional frequency analysis using the self-organizing map , 2006 .

[15]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[16]  Niels Schütze,et al.  Self‐organizing maps with multiple input‐output option for modeling the Richards equation and its inverse solution , 2005 .

[17]  Tommy W. S. Chow,et al.  Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density , 2004, Pattern Recognit..

[18]  R Govindaraju,et al.  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY: II, HYDROLOGIC APPLICATIONS , 2000 .

[19]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[20]  Young-Seuk Park,et al.  Patternizing communities by using an artificial neural network , 1996 .

[21]  Ravi Kothari,et al.  Spatial characterization of remotely sensed soil moisture data using self organizing feature maps , 1999, IEEE Trans. Geosci. Remote. Sens..

[22]  Y. Hong,et al.  Self‐organizing nonlinear output (SONO): A neural network suitable for cloud patch–based rainfall estimation at small scales , 2005 .

[23]  Holger R. Maier,et al.  Input determination for neural network models in water resources applications. Part 1—background and methodology , 2005 .

[24]  Christian W. Dawson,et al.  Hydrological modelling using artificial neural networks , 2001 .

[25]  Holger R. Maier,et al.  Optimal division of data for neural network models in water resources applications , 2002 .

[26]  Kuolin Hsu,et al.  Self‐organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis , 2002 .

[27]  Holger R. Maier,et al.  Determining Inputs for Neural Network Models of Multivariate Time Series , 1997 .

[28]  H. Maier,et al.  The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters , 1996 .

[29]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[30]  Stan Openshaw,et al.  Using computational intelligence techniques to model subglacial water systems , 1999, J. Geogr. Syst..

[31]  Sovan Lek,et al.  A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination , 2001 .

[32]  R. Céréghino,et al.  Spatial analysis of stream invertebrates distribution in the Adour-Garonne drainage basin (France), using Kohonen self organizing maps , 2001 .

[33]  T. Kohonen.  Analysis of a simple self-organizing process , 1982, Biological Cybernetics.

[34]  Michael Obach,et al.  Modelling population dynamics of aquatic insects with artificial neural networks , 2001 .

[35]  Liem T. Tran,et al.  Self-Organizing Maps for Integrated Environmental Assessment of the Mid-Atlantic Region , 2003, Environmental management.

[36]  Hajime Murao,et al.  A hybrid neural network system for the rainfall estimation using satellite imagery , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[37]  Hikmet Kerem Cigizoglu,et al.  Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data , 2007, Environ. Model. Softw..

[38]  Kuolin Hsu,et al.  Improved streamflow forecasting using self-organizing radial basis function artificial neural networks , 2004 .

[39]  R. Abrahart,et al.  Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments , 2000 .

[40]  Lazaros S. Iliadis,et al.  An Artificial Neural Network model for mountainous water-resources management: The case of Cyprus mountainous watersheds , 2007, Environ. Model. Softw..

[41]  Krist V. Gernaey,et al.  Artificial neural networks for rapid WWTP performance evaluation: Methodology and case study , 2007, Environ. Model. Softw..

[42]  Philip J. Sallis,et al.  Self-organising map methods in integrated modelling of environmental and economic systems , 2006, Environ. Model. Softw..

[43]  Olli Simula,et al.  Process Monitoring and Modeling Using the Self-Organizing Map , 1999, Integr. Comput. Aided Eng..

[44]  Juha Vesanto,et al.  Data exploration process based on the self-organizing map , 2002 .