Stochastic modeling for river pollution of sungai Perlis
River pollution has been recognized as a contributor to a wide range of health problems and disorders in human. It can pose health dangers to humans who come into contact with it, either directly or indirectly. Therefore, it is most important to measure the concentration of biochemical oxygen demand...
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| Format: | Thesis |
| Language: | English |
| Published: |
2014
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| Online Access: | http://eprints.utm.my/id/eprint/48623/1/NurulIzzatyMohdYunusMFS2014.pdf http://eprints.utm.my/id/eprint/48623/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:79519?queryType=vitalDismax&query=Stochastic+modeling+for+river+pollution+of+sungai+Perlis&public=true |
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| Summary: | River pollution has been recognized as a contributor to a wide range of health problems and disorders in human. It can pose health dangers to humans who come into contact with it, either directly or indirectly. Therefore, it is most important to measure the concentration of biochemical oxygen demand (BOD) as a water quality parameter since the parameter has long been the basic means for determining the degree of water pollution in rivers. In this study, BOD is used as a parameter to estimate the water quality at Sungai Perlis. It has been observed that Sungai Perlis is polluted due to lack of management and improper use of resources. Therefore, it is of importance to model the Sungai Perlis water quality in order to describe and predict the water quality systems. The BOD concentration secondary data set is used which was extracted from the Drainage and Irrigation Department Perlis State website. The first order differential equation from Streeter – Phelps model was utilized as a deterministic model. Then, the model was developed into a stochastic differential equation (SDE) model. Results from this study shows that the SDE model is more adequate to describe and predict the BOD concentration and the water quality systems in Sungai Perlis by having smaller value of mean squared error (MSE). |
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