Norazian Mohamed Noor
Thumbnail Image
Preferred name
Norazian Mohamed Noor
Official Name
Norazian, Mohamed Noor
Alternative Name
Noor, Norazian Mohamed
Norazian, M. N.
Noor, Norazian Mohamad
Noor, N. M.
Mohamed, Norazian N.
Mohamed, N. N.
Zizi, Na Mohd
Main Affiliation
Scopus Author ID
25221616600
Researcher ID
M-6956-2019

Research Output

Filters

1
36
Reset filters

Settings
Now showing 1 - 10 of 36
No Thumbnail Available
Publication

Enhancing ecosystem biodiversity through air pollution concentrations prediction using support vector regression approaches

2023 , Syaidatul Umairah Solehah , Aida Wati Zainan Abidin , Saiful Nizam Warris , Wan Nur Shaziayani , Balkish Mohd Osman , Nurain Ibrahim , Norazian Mohamed Noor , Ahmad Zia Ul-Saufie

Air is the most crucial element for the survival of life on Earth. The air we breathe has a profound effect on our ecosystem biodiversity. Consequently, it is always prudent to monitor the air quality in our environment. There are few ways can be done in predicting the air pollution index (API) like data mining. Therefore, this study aimed to evaluate three types of support vector regression (linear, SVR, libSVR) in predicting the air pollutant concentration and identify the best model. This study also would like to calculate the API by using the proposed model. The secondary daily data is used in this study from year 2002 to 2020 from the Department of Environment (DoE) Malaysia which located at Petaling Jaya monitoring station. There are six major pollutants that have been focusing in this work like PM10, PM2.5, SO2, NO2, CO, and O3. The root means square error (RMSE), mean absolute error (MAE) and relative error (RE) were used to evaluate the performance of the regression models. Experimental results showed that the best model is linear SVR with average of RMSE = 5.548, MAE = 3.490, and RE = 27.98% because had the lowest total rank value of RMSE, MAE, and RE for five air pollutants (PM10, PM2.5, SO2, CO, O3) in this study. Unlikely for NO2, the best model is support vector regression (SVR) with RMSE = 0.007, MAE = 0.006, and RE = 20.75% in predicting the air pollutant concentration. This work also illustrates that combining data mining with air pollutants prediction is an efficient and convenient way to solve some related environment problems. The best model has the potential to be applied as an early warning system to inform local authorities about the air quality and can reliably predict the daily air pollution events over three consecutive days. Besides, good air quality plays a significant role in supporting biodiversity and maintaning healthy ecosystems. © 2023 Universitatea "Alexandru Ioan Cuza" din Iasi. All rights reserved.

View
No Thumbnail Available
Publication

Morphological changes analysis using 3D bathymetric surveys in Chilia Branch - Bystroe Channel bifurcation area

2023 , Georgeta Tudor , György Deák , Marius Raischi , Miruna Arsene , Elena Holban , Norazian Mohamed Noor , Roshazita Che Amat

The Bystroe Channel project with transboundary environmental impact in the Danube Delta, area of great ecological significance that has already a strong anthropic footprint, requires close monitoring of water quality parameters in order to determine their tendencies and their impact on the ecosystem components. Riverbed bathymetry surveys using multibeam echo-sounders are of high interest due to the data resolution and coverage capabilities that surpass the single-beam methods. Two riverbed elevation datasets, recorded in consecutive years, have been used to carry out morphological comparative analysis for the area where Chilia branch bifurcates in Bystroe Channel and Old Stambul. The analysis has been performed both on the bathymetry grids as a whole and on 3 longitudinal and 9 transversal river sections, the morphological changes values being in majority included in [-0.5 m;+0.5 m], with a minimum of -2.4 m and a maximum of 2.2 m, showing the bifurcation influence on the erosion/deposition processes results.

View
No Thumbnail Available
Publication

Modified linear regression for predicting ambient particulate pollutants (PM₁₀) during High Particulate Event

2023 , Izzati Amani Mohd Jafri , Norazian Mohamed Noor , Nur Alis Addiena A. Rahim , Syaza Ezzati Baidrulhisham , Norazrin Ramli , Ahmad Zia Ul-Saufie , György Deák Habil

Particulate Matter (PM₁₀) is one of the most significant contributors towards haze or high particulate event (HPE) that occurs in Malaysia. HPE can severely affect human health, environment and economic so it is important to create a reliable prediction model in predicting future PM₁₀ concentration especially during HPE. Therefore, the aim of this study is to investigate the performance of modified linear regression models in predicting the next-day Particulate Matter (PM₁₀+24) concentration at two areas in the peninsular Malaysia namely, Bukit Rambai and Nilai. Hourly air quality dataset during historic HPE in 1997, 2005, 2013 and 2015 were used for analysis. Pearson correlation was used to select the input of the PM₁₀ prediction model where only parameters with moderate (0.6 > r > 0.3) and strong (r > 0.6) correlation with PM₁₀ concentration were selected as independent variables input in creating the multiple linear regression (MLR) model. The performance of modified linear regression model was evaluated by using several performance indicator which is Prediction Accuracy (PA), Index of Agreement (d 2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results show that the modified MLR (parameter with r > 0.6 included as input) gave the best prediction model for the next-day PM₁₀ concentration in both Bukit Rambai and Nilai.

View
No Thumbnail Available
Publication

Assessing the self-healings properties of nano -Ca(OH)₂ - TiO₂ - ZnO materials used in monuments conservation works

2023 , Moncea Mihaela-Andreea , Deak Gyogy , Gheorghe (Dumitru) Florina-Diana , Norazian Mohamed Noor , Nur Liza Rahim

Consolidates based on Ca(OH)₂ have been prepared as macro- and/or nanoparticles dispersed in different alcohols, regardless of the precursor nature. Their efficiency is related to the small dimensions of the particles and the dispersion media, which allows their penetration into the deteriorated material substrates, as well as to an increased reactivity towards CO₂. Besides the consolidating effect there is a strong need for the newly developed materials to also tackle other properties like self cleaning and antimicrobial activity, especially with regards to white monuments. In this context the present work highlights the effect of treating with stable alcoholic suspension containing nano - Ca(OH)₂ - TiO₂ - ZnO the physical support models consisting of mortar cubic samples from hydraulic / aerial lime, a commonly used materials in consolidation works.

View
No Thumbnail Available
Publication

A hydrosuction siphon system to remove particles using fan blades

2023-01 , Mohammed Hamid Rasool , Mohd Remy Rozainy Mohd Arif Zainol , Norazian Mohamed Noor , Mohd Sharizal Abdul Aziz , Mohd Hafiz Zawawi , Muhammad Khairi A. Wahab , Mohd Azmeer Abu Bakar

Sedimentation in dam reservoirs can cause problems that lead to loss of storage capacity and decrease in the flood control volume. Hydrosuction sediment removal is one of the methods used to remove sediments from within a reservoir using the suction energy provided by the effective head. In this study, a new tool has been developed by attaching the reservoir to a suction pipe intake point and using a simple fan blade mechanism for the hydrosuction sediment removal system. This mechanism is used to create a vortex flow to suspend the settled particles. This paper investigated the effects of the fan blade angles, effective head, and inlet height from the surface of layer particles on the performance and efficiency of fan blades hydrosuction sediment removal (FBHSSR) and hydrosuction sediment removal (HSSR) systems based on the geometric scour hole parameters. Results from the experimental tests indicated the effectiveness of the FBHSSR system, with the fan blade angles of 30°, 45°, and 60° leading to approximately 800%, 200%, and 117%, respectively, removed particles greater than those of the HSSR system. Furthermore, the maximum depth and diameter of the scour hole were increased by 206%, 200%, and 137% and 135, 112%, and 117%, respectively, for each angle. The effective head or experiment time also enhanced system performance by increasing the suction discharge, but no change was observed in terms of efficiency. The critical inlet heights for the FBHSSR and HSSR systems are 1 time and 2.54 times, respectively, more than the diameter of the suction pipe. Thus, it can be concluded that using fan blades in HSSR systems is a good approach to improve the properties of the scour hole.

View
No Thumbnail Available
Publication

Predicting Particulate Matter (PM₁₀) during High Particulate Event (HPE) using Quantile Regression in Klang Valley, Malaysia

2023 , Nur Alis Addiena A. Rahim , Norazian Mohamed Noor , Izzati Amani Mohd Jafri , Norazrin Ramli , Mohamad Anuar Kamaruddin , György Habil Deák

Particulate matter (PM₁₀) is the key indicator of air quality index (API) during high particulate event (HPE). The presence of PM₁₀ is believed to have an adverse effect on human health and environment. Therefore, the prediction of future PM₁₀ concentration is very important because it can aid the local authorities to implement precautionary actions to limit the impact of air pollution. This study aims to compare the performances of two predictive models, which include Multiple Linear Regression (MLR) and Quantile Regression (QR) in predicting the next-day PM10 concentration during HPE. The hourly dataset of PM₁₀ concentration with other trace gases and weather parameters at Kelang and Petaling Jaya from the year of historic haze event in Malaysia (1997, 2005, 2013 and 2015) were obtained from Department of Environment (DOE) Malaysia. Three performance measures namely Mean Absolute Error (MAE), Normalised Absolute Error (NAE) and Root Mean Squared Error (RMSE) were calculated to evaluate the performances of the predictive models. From the results, QR model at quantile 0.3 and 0.6 was chosen as the best predictive tools for predicting the next day PM₁₀ concentration during haze event in Kelang and Petaling Jaya, respectively. showed better performance for the prediction of next-day PM₁₀ concentration in Kelang. These results indicate that QR can be used as one of predictive tool to forecast air pollution concentration especially during unusual condition of air quality.

View
No Thumbnail Available
Publication

Investigation of indoor air quality by incorporating sewage sludge waste into fired clay brick

2023 , Nurul Salhana Abdul Salim , Aeslina Abdul Kadir , Norazian Mohamed Noor

Generally, the production of sewage sludge waste has increased due to rapid growth of the population. Due to that, the disposal method has become crucial issues nowadays. Therefore, this research focuses on the investigation of indoor air quality of fired clay brick incorporating with sewage sludge. The bricks were incorporated with 0% and 5% of two type of sewage sludge and fired at 1050°C with heating rates 1°C/min. The experiment of Indoor Air Quality was obtained by measuring gases emissions of total volatile organic compound (TVOC), carbon dioxide (CO2), carbon monoxide (CO), ozone (O3), formaldehyde (HCHO) and particulate matter (PM10) and were compared with Industry Code of Practice on Indoor Air Quality requirement standard. From the result shows that by incorporation of 5% of sewage sludge into fired clay brick complied with the standard requirement for building material usage with no negative effect to the environment. As the conclusions, the use of sewage sludge as clay replacement reduces the waste disposal in landfills and produce the low-cost building materials.

View
No Thumbnail Available
Publication

Performance of Bayesian Model Averaging (BMA) for short-term prediction of PM10 concentration in the Peninsular Malaysia

2023 , Norazrin Ramli , Hazrul Abdul Hamid , Ahmad Shukri Yahaya , Ahmad Zia Ul-Saufie , Norazian Mohamed Noor , Ain Nihla Kamarudzaman , György Deák , Nor Amirah Abu Seman @ Haji Ahmad

In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years’ worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models’ performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.

View
No Thumbnail Available
Publication

Comparative analysis of machine learning techniques for SO₂ prediction modelling

2023 , Wan Nur Shaziayani , Norazian Mohamed Noor , Muhamad Azan Yaakob , Ahmad Zia Ul-Saufie

Sulphur dioxide (SO₂) is produced both naturally and by human activity. The primary natural resource is derived from volcanoes. The burning of fossil fuels is the primary anthropogenic source (especially coal and diesel). Therefore, a reliable and accurate predicting method is essential for an early warning system for SO₂ atmospheric concentration. There are still limited studies in Malaysia that use machine learning methods to predict SO₂ concentrations. With the aid of machine learning, this study seeks to develop and predict future SO₂ concentrations for the next day using the maximum daily data from Klang, Selangor. RapidMiner Studio is the data mining tool used for this research work. Based on the results, it showed that the SVM model was the best guide to be used compared with the other five models (GLM, DL, DT, GBT, and RF). The performance indicators showed that the SVM model was adequate for the next day's prediction (R2 = 0.77, SE = 8.26, REL = 18.69%, AE = 1.46, and RMSE = 2.82). The developed model in this research can be used by Malaysian authorities as a public health protection measure to give Malaysians an early warning about the problem of air pollution. The goal of predictive modelling is to make a reasonable prediction of the variable of interest, and frequently, to determine how much the independent variable contributed to the dependent variable. The results also showed that the previous SO₂ concentrations were one of the most influential parameters used to predict the future SO₂ concentrations.

View
No Thumbnail Available
Publication

Evaluation of the ecological risk and the development of antimicrobial resistance due to the presence of the Macrolide antibiotics Azithromycin and Clarithromycin in Romanian aquatic environment

2023 , Mihaela Ilie , Florica Marinescu , Gina Ghita , Ioana Savin , Georgeta Tudor , Lucian Luminaroiu , Petrache-Ionut Gheorghe , Georgiana Dumitrescu , Norazian Mohamed Noor

The presence of antibiotics in the aquatic environment can result in the emergence of antibiotic-resistant pathogens. In this paper, our aim was to identify, quantify and distribute macrolide antibiotics in the aquatic environment in the river basins of Arges-Vedea, Buzau-Ialomita and Dobrogea-Litoral and of the Danube River. In the Arges-Vedea river basin area, clarithromycin was detected most frequently, i.e. 58.62%, in the Buzau-Ialomita river basin area with a frequency of 92.31% azithromycin was detected, and in the Dobrogea - Litoral river basin area clarithromycin was detected with a frequency of 35.48%. The highest concentration of azithromycin, 559 ng/L and of clarithromycin, 502 ng/L was recorded in the Buzau-Ialomita river basin. The ecotoxicological RQecotox risk was also estimated, as well as the risk of developing antibiotic resistance of RQAMR pathogens, by the ratio of Measured Environmental Concentration (MEC) and Predicted No Effect Concentrations (PNEC). The study also aimed to investigate the prevalence of antibiotic resistance in potentially pathogenic bacteria isolated from aquatic environments. Thus, for β-lactam antibiotics, E. coli strains isolated from the Danube River exhibited a very high level of resistance to ampicillin (51%) and high level to azithromycin (43%), cefazolin (38%), amoxicillin+clavulanic acid (36%) and cefoxitin (26%). Low and respectively, low level resistance was noticed for aztreonam (6%) and imipenem (4%).

View