Universiti Teknologi Malaysia Institutional Repository

Markov chain analysis to detect water quality level

Rahman, Nurul Nabihah (2013) Markov chain analysis to detect water quality level. Masters thesis, Universiti Teknologi Malaysia, Faculty of Science.

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Abstract

The quantificational analysis on progress of river water quality is important in knowing the dynamic change of water quality level. A mathematical model based on Markov chain is established in order to detect the water quality level in rivers. In this study, the level changes of water quality of River A, River B, River C and River D in 2010 based on water quality parameters of DO, BOD, COD, SS, pH and AN will be determined using Markov chain model. In order to determine the water quality level in rivers, the developing of the Markov chain model has to be conducted. There are three main steps in developing this model. The steps are establishing the transition probability matrix, calculate degree of absolute progress (DAP) and degree of relative progress (DRP).If the orders of water quality in rivers are arranged from the most deteriorated to the most improved, it will start from River B followed by River D, River A and River C. In other words, River B has the least improvement of changes among the rivers meanwhile River C has the most improvement of changes. After the Markov chain analysis to detect the water quality level has been done, the Water Quality Index (WQI) method is applied next in order to do justification of the Markov chain results. Surprisingly, the Markov chain model results match very well with the WQI method results when comparisons for both methods are made. To sum up, the results from Markov chain analysis can be justified by using the WQI method.

Item Type:Thesis (Masters)
Additional Information:Thesis (Sarjana Sains (Matematik)) - Universiti Teknologi Malaysia, 2013; Supervisor : Dr. Fadhilah Yusof
Subjects:Unspecified
Divisions:Science
ID Code:34642
Deposited By: Kamariah Mohamed Jong
Deposited On:23 Jul 2017 18:12
Last Modified:23 Jul 2017 18:15

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