Spatiotemporal Classification Analysis of Long-Term Environmental Monitoring Data in the Northern Part of Lake Taihu, China by Using a Self-Organizing Map

Characterizing the spatiotemporal patterns of water bodies is an important environmental issue in the management and protection of water resources. The primary objective of this study was to assess the spatiotemporal characteristics of environmental monitoring data from Lake Taihu to improve water pollution control practices. A methodologically systematic application of a self-organizing map (SOM) was utilized for data mining in the northern part of Lake Taihu, China. The monitoring data set contained 14 variables from eight monitoring stations during the period 2000-2006. The SOM classified the data set into 10 clusters displaying a markedly different pattern. We determined the spatiotemporal distribution of water quality based on the data frequency at each station monitored monthly in the study area. Based on the SOM analysis results, we suggest that the government should increase the number of monitoring points in the region. Given the relatively poor water quality in the region, unnecessary points should be decreased and different control measures should be implemented during different seasons. The results of this study could assist lake managers in developing suitable strategies and determining priorities for water pollution control and effective water resource management.

[1]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[2]  Feng Zhou,et al.  An integrated SOM-based multivariate approach for spatio-temporal patterns identification and source apportionment of pollution in complex river network. , 2012, Environmental pollution.

[3]  B Khalil,et al.  Statistical approaches used to assess and redesign surface water-quality-monitoring networks. , 2009, Journal of environmental monitoring : JEM.

[4]  M. Dokulil,et al.  Changes of nutrients and phytoplankton chlorophyll-a in a large shallow lake, Taihu, China: an 8-year investigation , 2003, Hydrobiologia.

[5]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[6]  Yong Liu,et al.  Application of Multivariate Statistical Methods to Water Quality Assessment of the Watercourses in Northwestern New Territories, Hong Kong , 2006, Environmental monitoring and assessment.

[7]  Teuvo Kohonen,et al.  Data Management by Self-Organizing Maps , 2008, WCCI.

[8]  Qinglong Wu,et al.  Environmental issues of Lake Taihu, China , 2007, Hydrobiologia.

[9]  P Barbieri,et al.  Multivariate classification and modeling in surface water pollution estimation , 2008, Analytical and bioanalytical chemistry.

[10]  Roohollah Noori,et al.  Chemometric Analysis of Surface Water Quality Data: Case Study of the Gorganrud River Basin, Iran , 2012, Environmental Modeling & Assessment.

[11]  M. Tobiszewski,et al.  Surface water quality assessment by the use of combination of multivariate statistical classification and expert information. , 2010, Chemosphere.

[12]  J. Akunna,et al.  Surface and groundwater water quality assessment using multivariate analytical methods: a case study of the Western Niger Delta, Nigeria , 2008 .

[13]  Christian Jacob,et al.  Self-Organizing Feature Maps , 2010, Encyclopedia of Machine Learning.

[14]  Hai Xu,et al.  Controlling harmful cyanobacterial blooms in a hyper-eutrophic lake (Lake Taihu, China): the need for a dual nutrient (N & P) management strategy. , 2011, Water research.

[15]  P Barbieri,et al.  Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets. , 2007, Water research.

[16]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  A. Kawamura,et al.  Spatiotemporal classification of environmental monitoring data in the Yeongsan River basin, Korea, using self-organizing maps. , 2011, Journal of environmental monitoring : JEM.

[18]  Fang Huang,et al.  Spatio-temporal patterns and source apportionment of pollution in Qiantang River (China) using neural-based modeling and multivariate statistical techniques , 2011 .

[19]  Teuvo Kohonen,et al.  Self-Organizing Maps of Massive Databases , 2001 .

[20]  T. Bamford.  Self-organized maps of sensory events. , 2003, Philosophical transactions. Series A, Mathematical, physical, and engineering sciences.

[21]  Stefan Tsakovski,et al.  Chemical composition of water from roofs in Gdansk, Poland. , 2010, Environmental pollution.

[22]  Berta Galán,et al.  Assessment of Self-Organizing Map artificial neural networks for the classification of sediment quality. , 2008, Environment international.

[23]  Stefan Tsakovski,et al.  Assessment of the water quality of a river catchment by chemometric expertise , 2010 .

[24]  Jayachandra Chilukamari,et al.  A computational model of visual attention , 2017 .

[25]  Tae-Soo Chon,et al.  Self-Organizing Maps applied to ecological sciences , 2011, Ecol. Informatics.

[26]  Wei Meng,et al.  Water quality assessment and source identification of Daliao river basin using multivariate statistical methods , 2009, Environmental monitoring and assessment.

[27]  Boqiang Qin,et al.  Long-term dynamics of phytoplankton assemblages: Microcystis-domination in Lake Taihu, a large shallow lake in China , 2003 .

[28]  A. M. Kalteh,et al.  Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application , 2008, Environ. Model. Softw..

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

[30]  J. Downing,et al.  The nitrogen : phosphorus relationship in lakes , 1992 .

[31]  Stefan Tsakovski,et al.  Multivariate statistical approaches as applied to environmental physics studies , 2006 .

[32]  Tonny J. Oyana.  Visualization of High-Dimensional Clinically Acquired Geographic Data Using the Self-Organizing Maps , 2009 .

[33]  Wei Zhang,et al.  Spatio-temporal patterns and source apportionment of coastal water pollution in eastern Hong Kong. , 2007, Water research.

[34]  P. Shin,et al.  Multiple Discriminant Analysis of Marine Sediment Data , 1999 .

[35]  Panu Somervuo,et al.  How to make large self-organizing maps for nonvectorial data , 2002, Neural Networks.

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