Evaluation of water quality using pattern recognition technique in Melaka River Basin, Malaysia

The unorganised expansion and uncontrolled urbanisation development had led to environmental issues involved with river water pollution that occurred within the Melaka River basin. There are three objectives study to solve the water quality of the Melaka River basin through (1) to identify the poten...

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Main Author:	Ang, Kean Hua
Format:	Thesis
Language:	English
Published:	2018
Subjects:	Rivers - Environmental aspects - Case studies - Malaysia Land use Land cover
Online Access:	http://psasir.upm.edu.my/id/eprint/76213/1/FPAS%202018%2012%20-%20IR.pdf
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spelling	my-upm-ir.762132020-01-20T03:43:52Z Evaluation of water quality using pattern recognition technique in Melaka River Basin, Malaysia 2018-05 Ang, Kean Hua The unorganised expansion and uncontrolled urbanisation development had led to environmental issues involved with river water pollution that occurred within the Melaka River basin. There are three objectives study to solve the water quality of the Melaka River basin through (1) to identify the potential sources of Melaka River pollution in correlation with the temporal land use classification, (2) to determine the land use land cover changes in 2015 and predict the future land use land cover for 2022, and (3) to verify pattern recognition based on the land use classification between 2015 and 2022 in the determination of pollutant sources within the Melaka River. The results indicated HCA have two cluster areas (C1 and C2). The DA indicated 12 variables were found to be the most significant parameters with a high variation in the spatial distribution. The PCA found that the C1 was attributed to contamination from the agricultural and residential activities; while the C2 included the pollution sources from the agricultural, residential, industrial, animal husbandry, and sewage treatment activities. In the Pearson correlation analysis and ANOVA analysis shown Vegetation Area (VA), Non-Industrial Area (NIA), Industrial Area (IA), Open Space Area (OSA), and Farming Area (FA) were correlated with majority of the physico-chemical and biological variables. LULC incorporated with CA-Markov chain model analysis showed NIA (67.50%), IA (29.15%) and FA (3.35%) were increased from 2001 to 2015 and 2022; while the VA (89.59%), OSA (3.32%) and WB (7.08%) continued to decrease. In GIS, the LISA analysis indicates 2015 showed the clustered area of C1 with S1 as the main contributor; while C2 of S5 as the main contributor. The Moran I analysis from high-to-low pollutant sources are NIA (0.80) > VA (0.59) > IA (0.42) > OSA (0.40). In 2022, the contamination from high-to-low are VA (0.59) > NIA (0.58) > IA (0.50) > OSA (0.41) in the Melaka River. The hotspot analysis for year 2015 indicated C1 of S1 as the main contributor; while C2 of S5 as main contributor. General G-statistic analysis from high-to-low are IA (1.099 x 10-3) > NIA (5.39 x 10-4) > VA (6.9 x 10-5). In 2022, the hotspot analysis in C1 indicated S7 as the main contributor; while C2 remains the same as S5 which is the main contributor to have pollutant sources from high-to-low are IA (3.012 x 10-3) > NIA (5.95 x 10-4) > VA (7.6 x 10-5). In conclusion, Moran I and general G-statistic had gained support especially in benefiting from the PCA in recognising the pattern of pollutant sources (e.g. land use classes) with precise details. Simultaneously, LISA and hotspot analysis have an advantage over HCA by recognising the clustered area more specifically, which was based on the pattern of pollutant sources. Rivers - Environmental aspects - Case studies. - Malaysia Land use Land cover 2018-05 Thesis http://psasir.upm.edu.my/id/eprint/76213/ http://psasir.upm.edu.my/id/eprint/76213/1/FPAS%202018%2012%20-%20IR.pdf text en public doctoral Universiti Putra Malaysia Rivers - Environmental aspects - Case studies. - Malaysia Land use Land cover
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Rivers - Environmental aspects - Case studies - Malaysia Land use Land cover
spellingShingle	Rivers - Environmental aspects - Case studies - Malaysia Land use Land cover Ang, Kean Hua Evaluation of water quality using pattern recognition technique in Melaka River Basin, Malaysia
description	The unorganised expansion and uncontrolled urbanisation development had led to environmental issues involved with river water pollution that occurred within the Melaka River basin. There are three objectives study to solve the water quality of the Melaka River basin through (1) to identify the potential sources of Melaka River pollution in correlation with the temporal land use classification, (2) to determine the land use land cover changes in 2015 and predict the future land use land cover for 2022, and (3) to verify pattern recognition based on the land use classification between 2015 and 2022 in the determination of pollutant sources within the Melaka River. The results indicated HCA have two cluster areas (C1 and C2). The DA indicated 12 variables were found to be the most significant parameters with a high variation in the spatial distribution. The PCA found that the C1 was attributed to contamination from the agricultural and residential activities; while the C2 included the pollution sources from the agricultural, residential, industrial, animal husbandry, and sewage treatment activities. In the Pearson correlation analysis and ANOVA analysis shown Vegetation Area (VA), Non-Industrial Area (NIA), Industrial Area (IA), Open Space Area (OSA), and Farming Area (FA) were correlated with majority of the physico-chemical and biological variables. LULC incorporated with CA-Markov chain model analysis showed NIA (67.50%), IA (29.15%) and FA (3.35%) were increased from 2001 to 2015 and 2022; while the VA (89.59%), OSA (3.32%) and WB (7.08%) continued to decrease. In GIS, the LISA analysis indicates 2015 showed the clustered area of C1 with S1 as the main contributor; while C2 of S5 as the main contributor. The Moran I analysis from high-to-low pollutant sources are NIA (0.80) > VA (0.59) > IA (0.42) > OSA (0.40). In 2022, the contamination from high-to-low are VA (0.59) > NIA (0.58) > IA (0.50) > OSA (0.41) in the Melaka River. The hotspot analysis for year 2015 indicated C1 of S1 as the main contributor; while C2 of S5 as main contributor. General G-statistic analysis from high-to-low are IA (1.099 x 10-3) > NIA (5.39 x 10-4) > VA (6.9 x 10-5). In 2022, the hotspot analysis in C1 indicated S7 as the main contributor; while C2 remains the same as S5 which is the main contributor to have pollutant sources from high-to-low are IA (3.012 x 10-3) > NIA (5.95 x 10-4) > VA (7.6 x 10-5). In conclusion, Moran I and general G-statistic had gained support especially in benefiting from the PCA in recognising the pattern of pollutant sources (e.g. land use classes) with precise details. Simultaneously, LISA and hotspot analysis have an advantage over HCA by recognising the clustered area more specifically, which was based on the pattern of pollutant sources.
format	Thesis
qualification_level	Doctorate
author	Ang, Kean Hua
author_facet	Ang, Kean Hua
author_sort	Ang, Kean Hua
title	Evaluation of water quality using pattern recognition technique in Melaka River Basin, Malaysia
title_short	Evaluation of water quality using pattern recognition technique in Melaka River Basin, Malaysia
title_full	Evaluation of water quality using pattern recognition technique in Melaka River Basin, Malaysia
title_fullStr	Evaluation of water quality using pattern recognition technique in Melaka River Basin, Malaysia
title_full_unstemmed	Evaluation of water quality using pattern recognition technique in Melaka River Basin, Malaysia
title_sort	evaluation of water quality using pattern recognition technique in melaka river basin, malaysia
granting_institution	Universiti Putra Malaysia
publishDate	2018
url	http://psasir.upm.edu.my/id/eprint/76213/1/FPAS%202018%2012%20-%20IR.pdf
_version_	1747813137349869568

Evaluation of water quality using pattern recognition technique in Melaka River Basin, Malaysia

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