Mohamad Romizan Osman - Academia.edu (original) (raw)

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Papers by Mohamad Romizan Osman

Jurnal Teknologi, 2015

This study intends to show the effectiveness of indoor air quality (IAQ) at the higher institutio... more This study intends to show the effectiveness of indoor air quality (IAQ) at the higher institution laboratory. The objective is to determine the impact of current IAQ, to study the occupants’ knowledge in the indoor air pollutants and to identify the significance of occupants’ personality regarding the IAQ awareness. 100 respondents had responded to answer the questionnaires given. The questionnaires were analysed using XLSTAT2014 software for descriptive statistic and discriminant analysis in order to fulfil the outlined objectives. The finding shows that 56% of the respondents know about IAQ, while 40% and 4% did not know and not sure about the IAQ, respectively. By gender, there were 20 of male respondents having the IAQ knowledge and 21 of male respondents did not know about the IAQ. Meanwhile, 36 of female respondents have IAQ knowledge, 19 of female respondents did not know the IAQ knowledge and 4 of female respondents were not sure regarding the IAQ knowledge. Furthermore, the IAQ in the laboratory at the higher institution is considered as unhealthy based on the respondents’ complaints of their health problem symptoms. Meanwhile, the results of personality tests show that women have more IAQ awareness compared to men. It indicated that the personalities of the occupants have significance to influence and able to determine their awareness on the IAQ. Hence, it described that IAQ is a significant factor to determine and influence the health of laboratory occupants.

This study addresses the effects of development on water quality in the Kuantan River Basin from ... more This study addresses the effects of development on water quality in the Kuantan River Basin from 2003 to 2008. Chemometrics analysis namely MLR, HACA, DA and PCA was utilised as part of the methods for this study. From the result, MLR was irrefutably proven as an efﬁcient predicting method for missing data. HACA classiﬁed seven stations as Low Polluted Stations (LPS), six stations as Moderate Polluted Stations (MPS) and two stations as High Polluted Stations (HPS). DA result depicted the accuracy rate for all reclassiﬁed data was 83.61 % respectively, while the constituting parameters namely Dissolved Oxygen (DO), Escherichia coli (E. coli), pH, Phosphate (PO4), Chemical Oxygen Demand (COD), and Chloride (Cl), gave the biggest impacts towards water quality by means of forward and backward stepwise methods. The PCA result after varimax rotation indicated that ﬁve varimax factors have presented strong parameter coefﬁcient exceeding 0.7 by E. coli, coliform, Dissolved Solids (DS), Total Solids (TS), Chlorine (Cl), Ammonical Nitrogen (NH3NL), nitrate and pH. The relationship between land use and water quality denoted that after applying Spearman correlation based on 90 % interval population distribution, aspects inﬂuencing the rate of DO was successfully identiﬁed.

This paper describes the application of principal component analysis (PCA) and artificial neural ... more This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005)(2006)(2007)(2008)(2009)(2010)(2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O 3 , PM 10 , NO 2 , CH 4 , NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.

This study focused on the pattern recognition of Malaysian air quality based on the data obtained... more This study focused on the pattern recognition of Malaysian air quality based on the data obtained from the Malaysian Department of Environment (DOE). Eight air quality parameters in ten monitoring stations in Malaysia for 7 years (2005–2011) were gathered. Principal component analysis (PCA) in the environmetric approach was used to identify the sources of pollution in the study locations. The combination of PCA and artificial neural networks (ANN) was developed to determine its predictive ability for the air pollutant index (API).The PCA has identified that CH4, NmHC, THC, O3, and PM10 are the most significant parameters. The PCA-ANN showed better predictive ability in the determination of API with fewer variables, with R2 and root mean square error (RMSE) values of 0.618 and 10.017, respectively. The work has demonstrated the importance of historical data in sampling plan strategies to achieve desired research objectives, as well as to highlight the possibility of determining the optimum number of sampling parameters, which in turn will reduce costs and time of sampling.

Jurnal Teknologi, 2015