Ahmad Shakir Mohd Saudi | University Sultan Zainal Abidin, (original) (raw)

Papers by Ahmad Shakir Mohd Saudi

Jurnal Teknologi, 2015

In this research, determination of water quality status for Linggi River was carried out by using... more In this research, determination of water quality status for Linggi River was carried out by using non-parametric Mann-Kendall analysis. HACA and PCA has been used to classify the river to obtain the clearest picture of the water quality status. The dataset includes six parameters for six monitoring stations (1997 to 2012). Mann-Kendall trend analysis shows significant improvement trend for all parameters studied except for BOD (WQ1 (P<0.1) and WQ6 (P<0.05)) and SS (WQ4 to WQ6 (P<0.05)). This indicates that even though the WQI getting good, a few parameters such as BOD and SS need to be watched and improved by the local authority to make sure the WQI continuously getting better in the future. HACA grouped the six monitoring stations into three different clusters based on their similarities namely less pollution site (LPS), medium pollution site (MPS) and high pollution site (HPS). HACA grouped one station (WQ1) into LPS, two stations into MPS (WQ2 and WQ3) and three stations into HPS (WQ4, WQ5 and WQ6). PCA was used to investigate the origin of each water quality variable based on the clustered region. Three principal components (PCs) were obtained with 75.3% total variation for HPS, 73.4% for MPS and 68.1% for LPS. The major pollution source for HPS are of anthropogenic source (municipal waste, domestic wastes) while for MPS the major source of pollution was from non point source pollution such as animal husbandry and livestock farms. For the LPS, major sources come from the sea tide effect (natural effect). The identification and classification of different region by this study will help the local authorities make better and more informed decisions about the improvement water quality program for the future.

This study was conducted to determine the most significant parameters for the air-pollutant index... more This study was conducted to determine the most significant parameters for the air-pollutant index (API) prediction in Malaysia using data covering a 7-year period (2006–2012) obtained from the Malaysian Department of Environment (DOE). The sensitivity analysis method coupled with the artificial neural network (ANN) was applied. Nine models (ANN-API-AP, ANN-API-LCO, ANN-API-LO3, ANN-API-LPM10, ANN-API-LSO2, ANN-API-LNO2, ANN-API-LCH4, ANN-APILNmHC and ANN-API-LTHC) were carried out in the sensitivity analysis test. From the findings, PM10 and CO were identified as the most significant parameters in Malaysia. Three artificial neural network models (ANN-API-AP, ANN-API-LO, and ANN-API-DOE) were compared based on the performance criterion [R2, root-mean-square error (RMSE), and squared sum of all errors (SSE)] for the best prediction model selection. The ANN-API-AP, ANN-API-LO, and ANN-APIDOE models have R2 values of 0.733, 0.578, and 0.742, respectively; RMSE values of 8.689, 10.858, and 8.357, respectively; SSE values of 762,767.22, 1,191,280.60, and 705,600.05, respectively. The findings exhibit the ANN-API-LO model has a lower value in R2 and higher values in RMSE and SSE than others. ANN-API-LO model was considered as the best model of prediction because of fewer variables was utilized as input and far less complex than others. Hence, the use of fewer parameters of the API prediction has been highly practicable for air resource management because of its time and cost efficiency.

Rivers is one of the complex natural systems. Classification of the river plan change is very imp... more Rivers is one of the complex natural systems. Classification of the river plan change is very important to know the river problems in early stage, where the classification database can help to understand the behavior of the river in each part. This article discusses about the classification of river plan change at the mainstream of Pahang River, Malaysia. Based on Geographical Information System (GIS) and Remote Sensing (RS) database, analysis of Types Of Lateral Activity (TYLAT) method and Modes of Meander Movement (MOME) method have been used to identify the evolution of the river plan change. The study results indicated, methods of TYLAT are more suitable to use for examining the evolution of river plan change for large and width rivers. While, method of analysis MOME index is more suitable for smaller types of rivers as the upper and middle reaches of the river. From this result, this study can be produced the basic information or database to understanding the characteristics or behavior parts in parts of the main Pahang River. This result also is very important to local authorities to know the early river problems in this area.

This study aims to identify the spatial variation of air pollutant and its pattern in the norther... more This study aims to identify the spatial variation of air pollutant and its pattern in the northern part of Peninsular Malaysia for four years monitoring observation (2008-2011) based on the seven air monitoring stations. Air pollutant variables that used in this study were Nitrogen Dioxide (NO2), Ozone (O3), Carbon Monoxide (CO), and Particulate Matter (PM10) data and had been supplied by Department Of Environment Malaysia (DOE). ANOVA, environmetric techniques (HACA and Descriptive Analysis) and Artificial Neural Network (ANN) approach were used in data analysed. According to ANOVA single test, significance p-value of PM10 (p= 2.5E-268) is smaller than significance alpha level (p=0.05) and it suitable parameter for further analysis in construct the prevention actions compared to O3, NO2 and CO. HACA categorized seven air monitoring station into three cluster group of station such as High Concentrated Site (HCS), Moderate Concentrated Site (MCS), and Low Concentrated Site (LCS). Descriptive statistics show the 25th percentile, median, and 75th percentile boxplot and identified the greater (>500 μg/m3) and smaller (<0.05ppm) outliers, and comparing distributions between each air pollutant. The findings from ANN have verified that the R2 and RMSE value (0.7981 and 5.734, respectively) were categorized as a significant value for the future prediction. In contrast, PM10 levels in Air Pollutant Index equal to 43.59 were 67.91 ug/m3, O3 (0.038 ppm), NO2 (0.019 ppm), and then CO (1.27 ppm) concentration values. This proved that the PM10 concentration was categorized as a main contributor to the air pollutant measurement of statistical method compared with other pollutants.

This study shows the effectiveness of hierarchical agglomerative cluster analysis (HACA), discrim... more This study shows the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), and multiple linear regressions (MLR) for assessment of air quality data and recognition of air pollution sources. 12 months data (January-December 2007) consisting of 14 stations in Peninsular Malaysia with 14 parameters were applied. Three significant clusters - low pollution source (LPS), moderate pollution source (MPS), and slightly high pollution source (SHPS) were generated via HACA. Forward stepwise of DA managed to discriminate eight variables, whereas backward stepwise of DA managed to discriminate nine variables out of fourteen variables. The PCA and FA results show the main contributor of air pollution in Peninsular Malaysia is the combustion of fossil fuel from industrial activities, transportation and agriculture systems. Four MLR models show that PM10 account as the most and the highest pollution contributor to Malaysian air quality. From the study, it can be stipulated that the application of chemometrics techniques can disclose meaningful information on the spatial variability of a large and complex air quality data. A clearer review about the air quality and a novelty design of air quality monitoring network for better management of air pollution can be achieved via these methods.

Living organisms require trace amounts of heavy metals, including cobalt, copper, manganese and z... more Living organisms require trace amounts of heavy metals, including cobalt, copper, manganese and zinc to survive. However, the excessive levels of the metal can be detrimental to the organism. Other heavy metals such as mercury, lead and cadmium have no vital on organisms, and their accumulation in long time period in the bodies can cause serious illness or death. The consumption of fish is recommended because fish is a basic and good nutritious food that has omega-3 fatty acids due to its cardio-protective effects. This present mini-review accounts for the description of heavy metal in fish and the effect of toxic metals on the human health. Besides, the acid digestion method was also discussed in order to identify the best method for applying in the laboratory analysis. The best method used can reduce the contamination error in the results.

This study looks into the downscaling of statistical model to produce and predict hydrological mo... more This study looks into the downscaling of statistical model to produce and predict hydrological modelling in the study area based on secondary data derived from the Department of Drainage and Irrigation (DID) since 1982-2012. The combination of chemometric method and time series analysis in this study showed that the monsoon season and rainfall did not affect the water level, but the suspended solid, stream flow and water level that revealed high correlation in correlation test with p-value < 0.0001, which affected the water level. The Factor analysis for the variables of the stream flow, suspended solid and water level showed strong factor pattern with coefficient more than 0.7, and 0.987, 1.000 and 1.000, respectively. Based on the Statistical Process Control (SPC), the Upper Control Limit for water level, suspended solid and stream flow were 21.110 m3/s, 4624.553 tonnes/day, and 8.224 m/s, while the Lower Control Limit were 20.711 m, 2538.92 tonnes/day and 2.040 m/s. This shows that human development in the area gives high impact towards climate change and flood risk, and not the monsoon season. Prediction was carried out using the Artificial Neural Network (ANN) to classify risks into their own classes, and the rate of accuracy for the prediction was 97.1%. This meant that the points in the time series analysis which were located beyond Upper Control Limit were considered as High Risk class, and the probability for flood occurrence is very high. The other classes classified in this prediction are Caution Zone, Low Risk and No risk. This is important to set a trigger for warning system in the case of emergency response plan during flood.

This study is focusing on constructing the flood risk index in the Johor river basin. The applica... more This study is focusing on constructing the flood risk index in the Johor river basin. The application of statistical methods such as factor analysis (FA), statistical process control (SPC) and artificial neural network (ANN) had revealed the most efficient flood risk index. The result in FA was water level has correlation coefficient of 0.738 and the most practicable variable to be used for the warning alert system. The upper control limits (UCL) for the water level in the river basin Johor is 4.423m and the risk index for the water level has been set by this method consisting of 0-100.The accuracy of prediction has been evaluated by using ANN and the accuracy of the test result was R2 = 0.96408 with RMSE= 2.5736. The future prediction for UCL in Johor river basin has been predicted and the value was 3.75m. This model can shows the current and future prediction for flood risk index in the Johor river basin and can help local authorities for flood control and prevention of the state of Johor.

The map of Tanjung Lumpur to Cherok Paloh from 1996 to 2004 revealed that there were significant ... more The map of Tanjung Lumpur to Cherok Paloh from 1996 to 2004 revealed that there were significant changes on coastal profiles. If the problem remains unsolved within 5 to 10 years, the beaches in the area might be fully eroded. The main objective of this study is to measure erosion of the coastline along Tanjung Lumpur to Cherok Paloh, Pahang during the northeast monsoon (December 2013 to February 2014). Transit set and dry sieving method were used for beach profile and grain size characteristics measurement. GRADISTAT v8 program is used for sedimentological analysis. Cluster analysis was used to show the group of higher eroded, medium eroded and lower eroded. The study found that almost all of the beach profiles had increased in length and the beach slopes were steeper; meanwhile the sedimentological analysis indicated that all the stations were dominated by sandy type during the period of study. The action of higher waves, tides and currents were the biggest contribution to erosion during northeast monsoon. From this study, it can be concluded that almost all stations have undergone erosion during the northeast season.

Flood is a major problem in Johor river basin, which normally happened during monsoon season. How... more Flood is a major problem in Johor river basin, which normally happened during monsoon season. However in this study, it shows that rainfall did not have a strong relationship for the changes of water level compared to suspended solid and stream flow, where both variables have p-values of <0.0001 and these variables also became the main factors in contributing to the flood occurrence based on Factor Analysis result. Time Series Analysis was being carried out and based on Statistical Process Control, the limitation has been set up for mitigation in controlling flood. All data beyond the Upper Control Limit was predicted to have High Risk to face flood and Emergency Response Plan should be implemented to prevent complication and destruction because of flood. The prediction for the risk level was carried out using the application of Artificial Neural Network (ANN), where the accuracy of prediction was very high, with the result of 96% for the level of accuracy in the prediction of risk class.

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.

This study constructs downscaling statistical model in analyzing the hydrological modeling in the... more This study constructs downscaling statistical model in analyzing the hydrological modeling in the study area which face the risk of flood occurrence as the impact of climate change. The combination of chemometric method and time series analysis in this study show that even during the monsoon season, rainfall and stream flow are not the major contribution towards the changing of water level in the study area. Based on Correlation Test, it shows that suspended solid and water level shows high correlation with p-value < 0.05. Factor Analysis being carried out to determine the major contribution to the changes of Water Level and the result shows that Suspended Solid shows a strong factor pattern with value 0.829. Based on Control Chat Builder for time series analysis, the Upper Control Limit for water level and suspended solid are 7.529 m and 1947.049 tons/day and the Lower Control Limit are 6.678 m and 178.135 tons/day. This shows that human development in the area gives high impact towards climate change and risk of flood in the study area which commonly face flood during monsoon season.

Air pollution is becoming a major environmental issue in Malaysia. This study focused on the iden... more Air pollution is becoming a major environmental issue in Malaysia. This study focused on the identification of potential sources of variations in air quality around the study area based on the data obtained from the Malaysian Department of Environment (DOE). Eight air quality parameters in ten monitoring stations for seven years (2006 – 2012) were gathered. The Principal Component Analysis (PCA) method from chemometric technique was applied to identify the source identification of pollution around the study area. The PCA method has identified methane (CH4), non-methane hydrocarbon (NmHC), total hydrocarbon (THC), ozone (O3) and particulate matter under 10 microns (PM10) are the most significant parameters around the study area. From the study, it can be concluded that the application of the PCA method in chemometric techniques can be applied for the source apportionment purpose. Hence, this study indicated that for the future and effective management of the Malaysian air quality, an effort should be placed as a priority in controlling point and non-point pollution sources.

Integrated Chemometric and Artificial Neural Network were being applied in this study to identify... more Integrated Chemometric and Artificial Neural Network were being applied in this study to identify the main contributor for flood, predicting hydrological modelling and risk of flood occurrence at the Kuantan river basin. Based on the Correlation Test analysis, the relationship for Suspended Solid and Stream Flow with Water Level were very high with Pearson correlation of coefficient value more than 0.5. Factor Analysis had been carried out and based on the result, variables such as Stream Flow, Suspended Solid and Water Level turned out to be the major factors and had a strong factor pattern with the results of factor score with >0.7 respectively. Time series analysis was being employed and the limitation had been set up where the Upper Control Limit for Stream Flow, Suspended Solid and Water Level where at this level, it was predicted by using Artificial Neural Network (ANN) to be High Risk Class. The accuracy of prediction from this method stood at 97.8%.

Water, Air, & Soil Pollution, 2014

ABSTRACT This study focused on the pattern recognition of Malaysian air quality based on the data... more ABSTRACT 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 R 2 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.

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.

Air pollution is becoming a major environmental issue in the southern region of Peninsular Malays... more Air pollution is becoming a major environmental issue in the southern region of Peninsular Malaysia. Environmetric techniques (HACA, DA, and PCA/ FA) were used to evaluate the spatial variations in the southern region of Peninsular Malaysia, followed by API prediction comparison using ANN and MLR models. The datasets of air pollutant parameters for 3 years (2005–2007) were applied in this study. HACA clustered three different groups of similarity based on the characteristics of air quality parameters. DA shows all seven parameters (CO, O3, PM 10, SO 2, NO x, NO, and NO2) gave the most signiﬁcant variables after stepwise backward mode. PCA/FA identify that the major source of air pollution is due to combustion of fossil fuels in motor vehicles and industrial activities. The ANN model shows a better prediction compared to the MLR model with R2 values equal to 0.819 and 0.773 respectively. This study concluded that the environmetric techniques and modelling become an excellent tool in API assessment, air pollution source identiﬁcation, apportionment, and interpretation of complex dataset with a view to get better information about the air quality, and can be setbacks in designing an API monitoring network for effective air pollution resources management.

This study intends to show the effectiveness of hierarchical agglomerative cluster analysis (HACA... more This study intends to show the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), factor analysis (FA) and multiple linear regressions (MLR) for assessing the air quality data and air pollution sources pattern recognition. The data sets of air quality for 12 months (January–December) in 2007, consisting of 14 stations around Peninsular Malaysia with 14 parameters (168 datasets) were applied. Three significant clusters - low pollution source (LPS) region, moderate pollution source (MPS) region, and slightly high pollution source (SHPS) region were generated via HACA. Forward stepwise of DA managed to discriminate 8 variables, whereas backward stepwise of DA managed to discriminate 9 out of 14 variables. The method of PCA and FA has identified 8 pollutants in LPS and SHPS respectively, as well as 11 pollutants in MPS region, where most of the pollutants are expected derived from industrial activities, transportation and agriculture systems. Four MLR models show that PM10 categorize as the primary pollutant in Malaysia. From the study, it can be stipulated that the application of chemometric techniques can disclose meaningful information on the spatial variability of a large and complex air quality data. A clearer review about the air quality and a novel design of air quality monitoring network for better management of air pollution can be achieved.

Jurnal Teknologi, 2015

Water, Air, & Soil Pollution, 2014