Dani Irwan

International Islamic University Malaysia

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Noor Ezlin Ahmad Basri

National University of Malaysia

Kohei Watanabe

Chukyo University

Ali Najah Ahmed

Sunway University

Ahmed El‐Shafie

United Arab Emirates University

Mohammed F.M. Abushammala

Middle East College

Sarmad Dashti Latif

Komar University of Science and Technology

Mohsen Sherif

United Arab Emirates University

Nouar AlDahoul

New York University Abu Dhabi

Mohammad K. Younes

Applied Science Private University

Michelle Sapitang

Universiti Tenaga Nasional

Cooperative Institutions

Universiti Tenaga Nasional

National University of Malaysia

Teikyo University

University of Malaya

International Islamic University Malaysia

Komar University of Science and Technology

United Arab Emirates University

Cairo University

University of Technology Malaysia

Universiti Teknologi MARA

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Methane Oxidation in Landfill Cover Soils: A Review

Asian Journal of Atmospheric Environment (2014)

Mohammed F.M. Abushammala Noor Ezlin Ahmad Basri Dani Irwan Mohammad K. Younes

Migration of methane ($CH_4$) gas from landfills to the surrounding environment negatively affects both humankind and the environment. It is therefore essential to develop management techniques to reduce $CH_4$ emissions from landfills to minimize global warming and to reduce the human risks associated with $CH_4$ gas migration. Oxidation of $CH_4$ in landfill cover soil is the most important strategy for $CH_4$ emissions mitigation. $CH_4$ oxidation occurs naturally in landfill cover soils due to the abundance of methanotrophic bacteria. However, the activities of these bacteria are influenced by several controlling factors. This study attempts to review the important issues associated with the $CH_4$ oxidation process in landfill cover soils. The $CH_4$ oxidation process is highly sensitive to environmental factors and cover soil properties. The comparison of various biotic system techniques indicated that each technique has unique advantages and disadvantages, and the choice of the best technique for a specific application depends on economic constraints, treatment efficiency and landfill operations.

Landfill Gas

Oxidation process

10.5572/ajae.2014.8.1.001

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Citations (51)

Prediction of atmospheric carbon monoxide concentration utilizing different machine learning algorithms: A case study in Kuala Lumpur, Malaysia

Environmental Technology & Innovation (2023)

Sarmad Dashti Latif Mustafa Almalayih Ayman Yafouz Ali Najah Ahmed Nur’atiah Zaini

Insidious toxin carbon monoxide (CO) can imitate a wide range of different disease states. Clinicians have, and will continue to have, serious concerns about the impact of CO imbalances on the human body. Carbon monoxide concentration has been exceeding the allowable levels in Malaysia. Owing to this, the main objective of this research is to propose a carbon monoxide (CO) prediction model based on machine learning techniques. Three years of historical data were used as input to develop the proposed models to predict carbon monoxide concentrations on a 12-hour and 24-hour basis. Four different machine learning technique models were used for the prediction which are Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Automated Neural Network – Multi-Layer Perceptron (ANN-MLP). The input parameters used are wind speed, humidity, Ozone (O3), Nitric oxide (NOx), Sulfur dioxide (SO2), and Nitrogen Dioxide (NO2). For each location, in this study, the uncertainty of the models utilized has been implemented to ensure the robustness of the performance. Furthermore, Taylor Diagram has been constructed to distinguish the performance of each model. The results indicate that ANN-MLP outperformed the all-other models involved in this study and showed efficiency in predicting Carbone monoxide concentration. By using ANN-MLP, the highest determination coefficient R2 were achieved which are 0.7190, 0.8914 and 0.7441 for the first station (S1), second station (S2) and the third station (S3) respectively by using 24-hour dataset. Meanwhile, by using a 12-hour dataset, 0.7490 for S1, 0.8942 for S2 and 0.8127 for S3. The uncertainty analysis of the ANN-MLP has 0.99 of confidence level and the lowest d-factor achieved, at S2 by using 12-hour dataset, is 0.000250455. These results ensure the effectiveness and robustness of ANN-MLP to predict carbon monoxide in the tropospheric layer. Not applicable.

Multilayer perceptron

10.1016/j.eti.2023.103387

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Citations (3)

Modeling of methane oxidation in landfill cover soil using an artificial neural network

Journal of the Air & Waste Management Association (2013)

Mohammed F.M. Abushammala Noor Ezlin Ahmad Basri Rahmah Elfithri Mohammad K. Younes Dani Irwan

Knowing the fraction of methane (CH4) oxidized in landfill cover soils is an important step in estimating the total CH4 emissions from any landfill. Predicting CH4 oxidation in landfill cover soils is a difficult task because it is controlled by a number of biological and environmental factors. This study proposes an artificial neural network (ANN) approach using feedforward backpropagation to predict CH4 oxidation in landfill cover soil in relation to air temperature, soil moisture content, oxygen (O2) concentration at a depth of 10 cm in cover soil, and CH4 concentration at the bottom of cover soil. The optimum ANN model giving the lowest mean square error (MSE) was configured from three layers, with 12 and 9 neurons at the first and the second hidden layers, respectively, log-sigmoid (logsig) transfer function at the hidden and output layers, and the Levenberg-Marquardt training algorithm. This study revealed that the ANN oxidation model can predict CH4 oxidation with a MSE of 0.0082, a coefficient of determination (R 2) between the measured and predicted outputs of up to 0.937, and a model efficiency (E) of 0.8978. To conclude, further developments of the proposed ANN model are required to generalize and apply the model to other landfills with different cover soil properties. Implications: To date, no attempts have been made to predict the percent of CH4 oxidation within landfill cover soils using an ANN. This paper presents modeling of CH4 oxidation in landfill cover soil using ANN based on field measurements data under tropical climate conditions in Malaysia. The proposed ANN oxidation model can be used to predict the percentage of CH4 oxidation from other landfills with similar climate conditions, cover soil texture, and other properties. The predicted value of CH4 oxidation can be used in conjunction with the Intergovernmental Panel on Climate Change (IPCC) First Order Decay (FOD) model by landfill operators to accurately estimate total CH4 emission and how much it contributes to global warming.

Landfill Gas

Backpropagation

10.1080/10962247.2013.842510

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Citations (19)

River Water Quality Monitoring using Machine Learning with Multiple Possible In-Situ Scenarios

Environmental and Sustainability Indicators (2025)

Dani Irwan Saerahany Legori Ibrahim Sarmad Dashti Latif Chris Aaron Anak Winston Ali Najah Ahmed

10.1016/j.indic.2025.100620

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Interrelationship between Affluence and Household Size on Municipal Solid Waste Arising: Evidence from Selected Residential Areas of Putrajaya

Dani Irwan Noor Ezlin Ahmad Basri Kohei Watanabe

Municipal solid waste (MSW) has always been an unavoidable byproduct of human habitation and activities. As the world now sees an exponential growth in population, so does it sees an alarming increase in the quantity of generated MSW. If managed and disposed of improperly, MSW is a major cause of adverse environmental conditions. Rapid development, urbanization, changes in consumption patterns and elevated levels of affluence in recent decades have only exacerbated the issue, especially in developing countries such as Malaysia. Hence, the impetus to handle these problems and to manage MSW in an efficient yet environmentally sound manner is reaching an apogee currently. Determining per capita MSW generation rate and understanding it’s influencing factors is one step towards efficient MSW management. The objectives of this study are twofold; to determine current per capita residential MSW arising rate and subsequently to discern if a correlation exists between MSW generation rate, affluence and household size. Three discrete housing neighborhoods in Putrajaya were selected as the areas under study. To capture varying socioeconomic levels, the selected study areas consists of bungalow, semidetached and terraced houses. Primary data was obtained by door-to-door weighing of MSW for 12 consequent days which makes up a sampling phase. This was conducted concurrently in all study areas, with a total of 3 sampling phases done over a 1 year period. A face-to-face survey was then performed on all households under study to obtain relevant socioeconomic data. From the analysis done, it is found that generally, household size has an inverse relationship on MSW arising. The affect of affluence on MSW discharge rate is found to be positive. From this study, concerted efforts to reduce MSW arising can be better focused on selected target groups and demographics, bringing us a step closer to sustainable waste management practices.

Consumption

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Influence of Income Level and Age on Per Capita Household Solid Waste Generation in Putrajaya, Malaysia

Jurnal Teknologi (2013)

Dani Irwan Noor Ezlin Ahmad Basri Kohei Watanabe Mohammed F.M. Abushammala

Municipal solid waste (MSW) has always been an unavoidable byproduct of human habitation and activities. It has continued to be a problem as we are forced to find ways to properly manage it. As the world now sees an exponential growth in population, so does it sees an alarming increase in the quantity of generated MSW. If managed and disposed of improperly, MSW is a major cause of adverse environmental conditions. Rapid development, urbanization, changes in consumption patterns and elevated levels of affluence in recent decades have only exacerbated the issue, especially in transitionary countries such as Malaysia. Hence, the impetus to handle these problems and to manage MSW in an efficient yet environmentally sound manner is reaching an apogee currently. Determining per capita MSW generation rate and understanding its influencing factors is one step towards efficient MSW management. The objectives of this study is to determine current per capita residential MSW arising rate and subsequently to discern if a relationship exists between MSW generation rate, affluence and age of the residents of nominated households. Three discrete housing neighborhoods in Putrajaya were selected as the areas under study. To capture varying socioeconomic levels, the selected study areas consists of bungalow, semidetached and terraced houses. Primary data was obtained by door-to-door weighing of MSW for 12 consequent days which makes up a sampling phase. This was conducted concurrently in all study areas, with a total of 3 sampling phases done over a 1 year period. A face-to-face survey was then performed on all households under study to obtain relevant socioeconomic data. From this study, it is determined that on average, the bungalow houses under study generated 0.47 kg/cap/day of MSW, semidetached housing area produces 0.31 kg/cap of MSW daily and terraced houses had an MSW output of 0.26 kg/cap/day. This shows that affluence has a positive affect on MSW discharge rate as households that earn a higher income tend to produce more waste. However, the link between age and MSW discharge rate is found to be inconclusive. From this study, concerted efforts to reduce MSW arising can be better focused on selected target groups and demographics, bringing us a step closer to formulating and implementing sustainable waste management practices.

Per capita income

Household income

10.11113/jt.v65.2186

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Citations (8)

Optimal operation of multi-reservoir systems for increasing power generation using a seagull optimization algorithm and heading policy

Energy Reports (2021)

Mohammad Ehteram Fatemeh Barzegari Banadkooki Chow Ming Fai Mohsen Moslemzadeh Michelle Sapitang

Power supply is a key issue for decision-makers. The reservoir operation of multi-reservoir systems is an important aspect to consider in efforts to increase power generation. This research studies a multi-reservoir system comprising of the Khersan-I (KHI), Karoon-III (KAIII) and Karoon-IV (KAIV) with the intent being to increase power generation. To achieve this, the Two-Point Heading Rule was integrated with a new optimization algorithm, namely the Seagull Optimization Algorithm (SEOA). The Two-Point Heading Rule was used based on four distinct scenarios, namely Two-Point Heading Rule (1), Two-Point Heading Rule (2), Two-Point Heading Rule (3) and Two-Point Heading Rule (4). The Seagull Optimization Algorithm was then used to find two heading parameters of the TPHRs. The Seagull Optimization Algorithm was subsequently benchmarked against the Salp Swarm Algorithm (SSA), Bat Algorithm (BA) and the Shark Optimization Algorithm (SOA). Various inflow scenarios consisting of the first inflow scenario (dry condition), the second inflow scenario (normal) and the third inflow scenario (wet condition) were considered for the optimal operation of this multi-reservoir system. The results indicated that the global solution of the MSOO based on NLP for Two-Point Heading Rule (1) under the first inflow scenario and was 3.22 while the average solution of Seagull Optimization Algorithm, Salp Swarm Algorithm, Shark Optimization Algorithm, and Bat Algorithm in respective order was 3.25, 3.93, 4.87 and 6.03. The results indicated that the global solution of the MSOO based on NLP for Two-Point Heading Rule (1) under the second inflow scenario was 2.14 while the average best solution of Seagull Optimization Algorithm, Salp Swarm Algorithm, Shark Optimization Algorithm, and Bat Algorithm in respective order was 2.16, 2.98, 3.96, and 4.89. It can be concluded that the SEOA outperformed all of the other algorithms. It was also found that the SEOA based on the Two-Point Heading Rule (3) under the third inflow scenario provided the most power generation for the KHI and KAIV systems. A multi-criteria decision was utilized to choose the best algorithm and heading policy. The ensuing results indicate that the SEOA had the best performance out of all the algorithms based on Two-Point Heading Rule (3) and the third inflow scenario.

Inflow

10.1016/j.egyr.2021.06.008

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Citations (21)

Door-to-door measurement of household waste arisings in selected towns in Malaysia

Journal of Material Cycles and Waste Management (2015)

Kohei Watanabe Dani Irwan Noor Ezlin Ahmad Basri

Household Waste

10.1007/s10163-015-0379-6

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Citations (3)

Predicting Water Quality with Artificial Intelligence: A Review of Methods and Applications