Using self-organizing map for coastal water quality classification: Towards a better understanding of patterns and processes

Publication date: 1 July 2018
Source:Science of The Total Environment, Volumes 628–629
Author(s): Tao Li, Guihua Sun, Chupeng Yang, Kai Liang, Shengzhong Ma, Lei Huang
Self-organizing map (SOM) was used to explore the spatial characteristics of water quality in the middle and southern Fujian coastal area. Nineteen water quality variables (temperature, salinity, pH, dissolved oxygen, alkalinity, chemical oxygen demand, nutrients NH4-N, H2SiO3, PO4⁻, NO2⁻, and NO3⁻, heavy metals/metalloid Cu, Zn, As, Cd, Pb, Hg, and Cr⁶⁺, and oil) were measured in the surface, middle, and bottom water layers at 94 different sampling sites. Patterns of water quality variables were visualized by the SOM planes, and similar patterns were observed for those variables that correlated with each other, indicating a common source. pH, COD, As, Hg, Pb, and Cr⁶⁺ likely originated from industries, while nutrients NH4-N, NO2⁻, NO3⁻, and PO4³⁻ were mainly attributed to agriculture and aquaculture. The k-means clustering in the SOM grouped the water quality data into nine clusters, which revealed three representative water types, ranging from low salinity to high salinity with different levels of heavy metal/metalloid pollution and nutrient pollution. Spatial changes in water quality reflected the impacts of natural factors (riverine outflows, tides, and alongshore currents), as well as anthropogenic activities (mariculture, industrial and urban discharges, and agricultural effluents). Principal component analysis (PCA) confirmed the clustering results obtained by SOM, while the latter provides a more detailed classification and additional information about the dominant variables governing the classification processes. The results of this study suggest that SOM is an effective tool for a better understanding of patterns and processes driving water quality.

Graphical abstract

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