Statistical assessment of trophic conditions: squared Euclidean distance approach
The classification of trophic conditions of water bodies may often face contradictory cases where a given lake is classified into a trophic category from a trophic variable, whereas it is classified into another trophic category from other trophic variables. To solve this problem, this paper propose...
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| Format: | Article |
| Language: | English |
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Prince of Songkla University
2003-05-01
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| Series: | Songklanakarin Journal of Science and Technology (SJST) |
| Subjects: | |
| Online Access: | http://www.sjst.psu.ac.th/journal/25-3-pdf/09trophiceuclideanstatistic.pdf |
| _version_ | 1811254910497325056 |
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| author | Chatchai Ratanachai Parichart Visuthismajarn Monte Kietpawpan |
| author_facet | Chatchai Ratanachai Parichart Visuthismajarn Monte Kietpawpan |
| author_sort | Chatchai Ratanachai |
| collection | DOAJ |
| description | The classification of trophic conditions of water bodies may often face contradictory cases where a given lake is classified into a trophic category from a trophic variable, whereas it is classified into another trophic category from other trophic variables. To solve this problem, this paper proposes a new methodology based on the concepts of squared Euclidean distance and the boundary values recommended by the OECD (Organization for Economic Cooperation and Development). This methodology requires that a trophic variable data set of a water body under consideration and such boundary values be compared by a measure of similarity computed by using basic statistical techniques to determine the trophic condition of a given water body. The methodology has been tested by applying it to two sample data sets: the Pattani Dam Reservoir and the North Adriatic Sea data sets, which were taken from Kietpawpan (2002) and Zurlini (1996), respectively. The squared Euclidean distance analysis were then applied to the above data sets in order to classifytrophic conditions, based on four trophic variables comprising total nitrogen, total phosphorus, chlorophylla, and Secchi depth. Our results show that the squared Euclidean distance analysis is a useful methodology for preliminarily classifying trophic conditions and solving contradictory classifications, which often arise when applying the present OECD methodology alone. |
| first_indexed | 2024-04-12T17:14:42Z |
| format | Article |
| id | doaj.art-2b1f900633a64641b01e6e938a3f1576 |
| institution | Directory Open Access Journal |
| issn | 0125-3395 |
| language | English |
| last_indexed | 2024-04-12T17:14:42Z |
| publishDate | 2003-05-01 |
| publisher | Prince of Songkla University |
| record_format | Article |
| series | Songklanakarin Journal of Science and Technology (SJST) |
| spelling | doaj.art-2b1f900633a64641b01e6e938a3f15762022-12-22T03:23:41ZengPrince of Songkla UniversitySongklanakarin Journal of Science and Technology (SJST)0125-33952003-05-01253359365Statistical assessment of trophic conditions: squared Euclidean distance approachChatchai RatanachaiParichart VisuthismajarnMonte KietpawpanThe classification of trophic conditions of water bodies may often face contradictory cases where a given lake is classified into a trophic category from a trophic variable, whereas it is classified into another trophic category from other trophic variables. To solve this problem, this paper proposes a new methodology based on the concepts of squared Euclidean distance and the boundary values recommended by the OECD (Organization for Economic Cooperation and Development). This methodology requires that a trophic variable data set of a water body under consideration and such boundary values be compared by a measure of similarity computed by using basic statistical techniques to determine the trophic condition of a given water body. The methodology has been tested by applying it to two sample data sets: the Pattani Dam Reservoir and the North Adriatic Sea data sets, which were taken from Kietpawpan (2002) and Zurlini (1996), respectively. The squared Euclidean distance analysis were then applied to the above data sets in order to classifytrophic conditions, based on four trophic variables comprising total nitrogen, total phosphorus, chlorophylla, and Secchi depth. Our results show that the squared Euclidean distance analysis is a useful methodology for preliminarily classifying trophic conditions and solving contradictory classifications, which often arise when applying the present OECD methodology alone.http://www.sjst.psu.ac.th/journal/25-3-pdf/09trophiceuclideanstatistic.pdfeutrophicationOECDsquared Euclideantrophic state classification |
| spellingShingle | Chatchai Ratanachai Parichart Visuthismajarn Monte Kietpawpan Statistical assessment of trophic conditions: squared Euclidean distance approach Songklanakarin Journal of Science and Technology (SJST) eutrophication OECD squared Euclidean trophic state classification |
| title | Statistical assessment of trophic conditions: squared Euclidean distance approach |
| title_full | Statistical assessment of trophic conditions: squared Euclidean distance approach |
| title_fullStr | Statistical assessment of trophic conditions: squared Euclidean distance approach |
| title_full_unstemmed | Statistical assessment of trophic conditions: squared Euclidean distance approach |
| title_short | Statistical assessment of trophic conditions: squared Euclidean distance approach |
| title_sort | statistical assessment of trophic conditions squared euclidean distance approach |
| topic | eutrophication OECD squared Euclidean trophic state classification |
| url | http://www.sjst.psu.ac.th/journal/25-3-pdf/09trophiceuclideanstatistic.pdf |
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