Revealing patterns in coastal water quality data using statistical analysis

768 642 57 5 6975 document 57 revealingPatternss52_Muttil_.pdf application/pdf 627653 2011-09-09 10:20:36 https://vuir.vu.edu.au/768/1/revealingPatternss52_Muttil_.pdf 768 1 application/pdf text en public

revealingPatternss52_Muttil_.pdf

http://eprints.org/relation/hasVolatileVersion https://vuir.vu.edu.au/id/document/10531 http://eprints.org/relation/hasVersion https://vuir.vu.edu.au/id/document/10531 http://eprints.org/relation/hasIndexCodesVersion https://vuir.vu.edu.au/id/document/10531 10531 3 1733968 document 10531 indexcodes.txt text/plain ac1cfc252c5c0df9bdd2bc27d610bac8 MD5 7258 2013-02-06 16:27:52 https://vuir.vu.edu.au/768/2/indexcodes.txt 768 2 2 text/plain other Generate index codes conversion from application/pdf to indexcodes en public

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http://eprints.org/relation/isVersionOf https://vuir.vu.edu.au/id/document/57 http://eprints.org/relation/isVolatileVersionOf https://vuir.vu.edu.au/id/document/57 http://eprints.org/relation/issmallThumbnailVersionOf https://vuir.vu.edu.au/id/document/57 archive 241 disk0/00/00/07/68 2008-03-27 2021-08-27 07:40:13 2008-09-03 01:49:10 conference_item show Muttil Nitin Chau Kwok-wing Revealing patterns in coastal water quality data using statistical analysis pub 290000 tafeengineering harmful algal blooms, data mining, box plots, multivariate statistical analysis, factor analysis, water quality modelling, Hong Kong A major impact of eutrophication is the stimulation of algal growth and the production of harmful algal blooms (HABs). HABs can have profound negative effects on the environment, which include severe dissolved oxygen depletion, fish kills, discoloration of marine water, beach closures, etc. Owing to the extremely complicated ecological processes, previous research efforts indicate that it is far from accurately unravelling the causality and dynamics of HABs and predicting their occurrence with acceptable accuracy and lead-time. Thus, techniques to better understand the complex interrelated processes in the ecosystem are needed. In this study, visual data mining using box plots and multivariate statistical analysis using factor analysis are employed for a spatio-temporal analysis of coastal water quality data from Tolo Harbour, Hong Kong (Figure 1). To study spatial patterns, data from the seven monitoring stations (shown in Figure 1) are used. To study temporal patterns, two datasets from different periods were used: one when the water quality in Tolo Harbour was at its worst (in the late eighties) and the second dataset being more recent, when the water quality had improved significantly. This study demonstrates the use of data mining techniques to exhibit prominent spatial and temporal patterns and to reveal dominant variables governing key ecological processes and thus provide further insight into the HAB dynamics. 2007-12 published http://mssanz.org.au/MODSIM07/papers/53_s52/revealingPatternss52_Muttil_.pdf public paper 7 MODSIM 2007 International Congress on Modelling and Simulation Christchurch, New Zealand 10-13 December 2007 conference UNSPECIFIED TRUE Includes reference: p. 2966 FALSE 2-s2.0-80052924143 2023-07-01 04:04:00