2019_Prediction Models for Improved Marine Water Quality Index in Mangrove Estuarine in Malaysia
| Format: | General Document |
|---|
| _version_ | 1860797997209616384 |
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| building | INTELEK Repository |
| collection | Online Access |
| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 |
| copyright | Copyright©PWB2025 |
| country | Malaysia |
| date | 2019-03-05 |
| format | General Document |
| id | 15350 |
| institution | UniSZA |
| internalnotes | Sila masukkan subject wajib Dissertations, Academic. Terima kasih... |
| originalfilename | 15350_15818ca22a2a55c.pdf |
| person | Mohd Saiful Bin Samsudin |
| recordtype | oai_dc |
| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=15350 |
| sourcemedia | Server storage Scanned document |
| spelling | 15350 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=15350 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 General Document Malaysia Library Staff (Top Management) Library Staff (Management) Library Staff (Support) Terengganu Faculty of Bio-resources & Food Industry English application/pdf 1.5 Server storage Scanned document Universiti Sultan Zainal Abidin UniSZA Private Access Universiti Sultan Zainal Abidin SAMBox 2.3.4; modified using iTextSharp™ 5.5.10 ©2000-2016 iText Group NV (AGPL-version) 15350_15818ca22a2a55c.pdf 301 2019_Prediction Models for Improved Marine Water Quality Index in Mangrove Estuarine in Malaysia 2019-03-05 Marine Pollution – Malaysia Mohd Saiful Bin Samsudin Copyright©PWB2025 Anthropogenic activities often generate effects, which also interfere in the ecosystem functioning. The ecosystem of mangrove estuarine receives several types of inputs such as urban, industrial and agricultural wastes, which results in significant alteration in the marine water quality. Available literature reveals that a few works have been carried out on the water quality and ecology of mangrove estuary in recent years. But these studies fail to explain the anthropogenic impact on the marine water quality in the mangrove estuarine systems. Thus, this led to the lack of information of marine water quality status at mangroves estuarine zone. Extensive variety of methods accessible have drawn-out prospects to study marine water quality related to land use change at mangrove estuary in greater detail. Hence, to improve and utilize the standardization of surface marine water quality prediction models, the status, progress, frame structure, assessing indicators, and authentication of the mangrove and estuarine ecosystem must be evaluated before choosing the best type of prediction models. This research aims to assess the marine water quality index (MWQI) for mangrove estuary which can profile the magnitude of pollution in selected mangrove estuaries of Peninsular Malaysia. The objectives of this study are to develop MWQI prediction and forecaster model using historical data of marine water quality and land use, to validate the distribution and variation of physico-chemical and heavy metals in the study area and to assess the prediction model of marine water quality pattern in selected mangrove river estuary zone in Peninsular Malaysia. MWQI is used as a method to reflect the marine water quality status and have seven main parameters (Dissolved Solid (DO), Nitrate (NO3), Phosphate (PO4), Unionized Ammonia (NH3), Faecal Coliform, Oil and Grease (O&G), and Total Suspended Solid (TSS)). Moreover, the addition of 6 dissolved metal was also analysed in this study (Pb, Cu, Cr, Cd, As and Zn). The prediction models of MWQI in mangrove estuarine zones were constructed using secondary data. The 2011-2015 data employed from Department of Environment Malaysia and involved 13 parameters from six monitoring stations in Peninsular Malaysia. The selected parameters were then used to develop prediction models for the MWQI using Spatial Discriminant Analysis (SDA) combining with artificial neural network (ANN). Primary data for water samples were collected in July 2016 until January 2017 during dry season and wet season at six sampling stations in Semerak river estuary and Setiu river estuary. Modeling of the land use-water quality relations has been conducted for the study areas. Spatial discriminant analysis (SDA) combining with artificial neural network (ANN) had recommended seven significant parameters to develop the MWQI prediction model which were DO, TSS, O&G, PO4, Cd, Cr and Zn. The SDA-ANN model gives the high value of R2 for training (0.9044) and validation (0.7113) results were chosen as the best model in the mangrove estuarine zone. The SDA-ANN model had also demonstrated low of RMSE value (5.224). In summary, this work suggested that SDA-ANN was an effective tool to compute the MWQ in mangrove estuarine zone and a powerful alternative prediction model. The prediction model from the secondary data can be used for primary data precisely also for the land use changes at the study area. Subsequently, a model combination from land use changes from CA-Markov and MWQI was used as future MWQI forecasting namely as MWQI-LUCR forecaster model. The assessment of surface water quality with referring to MWQI in mangroves distribution is needed to enhance the alternative MWQI model which will beneficial to government agencies for management and conservation strategies. In addition, exploitation of statistical approach and modeling methods will help the government agencies to avoid the negatives effects to water quality in mangrove areas. Dissertations, Academic Sila masukkan subject wajib Dissertations, Academic. Terima kasih... Marine Water Quality Water Quality Prediction Models Mangrove Estuarine Ecosystems Thesis |
| spellingShingle | 2019_Prediction Models for Improved Marine Water Quality Index in Mangrove Estuarine in Malaysia |
| state | Terengganu |
| subject | Marine Pollution – Malaysia Dissertations, Academic |
| summary | Anthropogenic activities often generate effects, which also interfere in the ecosystem functioning. The ecosystem of mangrove estuarine receives several types of inputs such as urban, industrial and agricultural wastes, which results in significant alteration in the marine water quality. Available literature reveals that a few works have been carried out on the water quality and ecology of mangrove estuary in recent years. But these studies fail to explain the anthropogenic impact on the marine water quality in the mangrove estuarine systems. Thus, this led to the lack of information of marine water quality status at mangroves estuarine zone. Extensive variety of methods accessible have drawn-out prospects to study marine water quality related to land use change at mangrove estuary in greater detail. Hence, to improve and utilize the standardization of surface marine water quality prediction models, the status, progress, frame structure, assessing indicators, and authentication of the mangrove and estuarine ecosystem must be evaluated before choosing the best type of prediction models. This research aims to assess the marine water quality index (MWQI) for mangrove estuary which can profile the magnitude of pollution in selected mangrove estuaries of Peninsular Malaysia. The objectives of this study are to develop MWQI prediction and forecaster model using historical data of marine water quality and land use, to validate the distribution and variation of physico-chemical and heavy metals in the study area and to assess the prediction model of marine water quality pattern in selected mangrove river estuary zone in Peninsular Malaysia. MWQI is used as a method to reflect the marine water quality status and have seven main parameters (Dissolved Solid (DO), Nitrate (NO3), Phosphate (PO4), Unionized Ammonia (NH3), Faecal Coliform, Oil and Grease (O&G), and Total Suspended Solid (TSS)). Moreover, the addition of 6 dissolved metal was also analysed in this study (Pb, Cu, Cr, Cd, As and Zn). The prediction models of MWQI in mangrove estuarine zones were constructed using secondary data. The 2011-2015 data employed from Department of Environment Malaysia and involved 13 parameters from six monitoring stations in Peninsular Malaysia. The selected parameters were then used to develop prediction models for the MWQI using Spatial Discriminant Analysis (SDA) combining with artificial neural network (ANN). Primary data for water samples were collected in July 2016 until January 2017 during dry season and wet season at six sampling stations in Semerak river estuary and Setiu river estuary. Modeling of the land use-water quality relations has been conducted for the study areas. Spatial discriminant analysis (SDA) combining with artificial neural network (ANN) had recommended seven significant parameters to develop the MWQI prediction model which were DO, TSS, O&G, PO4, Cd, Cr and Zn. The SDA-ANN model gives the high value of R2 for training (0.9044) and validation (0.7113) results were chosen as the best model in the mangrove estuarine zone. The SDA-ANN model had also demonstrated low of RMSE value (5.224). In summary, this work suggested that SDA-ANN was an effective tool to compute the MWQ in mangrove estuarine zone and a powerful alternative prediction model. The prediction model from the secondary data can be used for primary data precisely also for the land use changes at the study area. Subsequently, a model combination from land use changes from CA-Markov and MWQI was used as future MWQI forecasting namely as MWQI-LUCR forecaster model. The assessment of surface water quality with referring to MWQI in mangroves distribution is needed to enhance the alternative MWQI model which will beneficial to government agencies for management and conservation strategies. In addition, exploitation of statistical approach and modeling methods will help the government agencies to avoid the negatives effects to water quality in mangrove areas. |
| title | 2019_Prediction Models for Improved Marine Water Quality Index in Mangrove Estuarine in Malaysia |
| title_full | 2019_Prediction Models for Improved Marine Water Quality Index in Mangrove Estuarine in Malaysia |
| title_fullStr | 2019_Prediction Models for Improved Marine Water Quality Index in Mangrove Estuarine in Malaysia |
| title_full_unstemmed | 2019_Prediction Models for Improved Marine Water Quality Index in Mangrove Estuarine in Malaysia |
| title_short | 2019_Prediction Models for Improved Marine Water Quality Index in Mangrove Estuarine in Malaysia |
| title_sort | 2019_prediction models for improved marine water quality index in mangrove estuarine in malaysia |