Nuratiah Zaini

• PhD in Engineering
• Lecturer at Universiti Tenaga Nasional

The hydropower Plant in Terengganu is one of the major hydroelectric dams currently operated in Malaysia. For better operating and scheduling, accurate modelling of natural inflow is vital for a hydroelectric dam. The rainfall-runoff model is among the most reliable models in predicting the inflow based on the rainfall events. Such a model's reliab...

Public transport should improve availability and versatility and capable to reduce the dependency on private vehicle at once. The statistics shows that in 2020 recorded more than 32.30 million of active vehicle in this country, and these numbers were increased by 3.73% from previous years. The aim of this study is to conduct the assessment on the p...

Modeling wind speed has a significant impact on wind energy systems and has attracted attention from numerous researchers. The prediction of wind speed is considered a challenging task because of its natural nonlinear and random characteristics. Therefore, machine learning models have gained popularity in this field. In this paper, three machine lea...

One of the largest hydropower facilities currently in operation in Malaysia is the Terengganu hydroelectric facility. As a result, for hydropower generation to be sustainable, future water availability in hydropower plants must be known. Therefore, it is necessary to precisely estimate how the river flow will alter as a result of changing rainfall...

Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration...

Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As...

In developing countries, public transit use is still lacking as opposed to developed counties. Therefore, it is essential to consider consumers’ public transportation service quality expectations to increase overall customer experience and Mass Rapid Transit (MRT) ridership in Malaysia. Thus, this article’s objective is to suggest thresholds for as...

Carbon monoxide (CO) is one of the dangerous air pollutants due to its negative impact on human health. Therefore, accurate forecasting of CO concentration is essential to control air pollution. This study aims to forecast the concentration of CO using sequences to sequence models namely convolutional neural network and long short-term memory (CNN-...

Accurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perce...

This study assessed the physical and chemical parameters related to indoor air quality (IAQ) at Universiti Tenaga Nasional (UNITEN) and the IAQ of selected campus buildings were compared with guidelines provided by DOSH. This study is especially important as students spend most of their time indoors thus making them more vulnerable to exposure to i...

It is crucial to keep an eye on the water levels in reservoirs in order for them to perform at peak, as they are one of the, if not, the most vital part in water resource management. The water stored is essential in providing water supply, generating hydropower as well as preventing overlasting droughts. Thus, efficient forecasting models are essen...

To accurately predict tropospheric ozone concentration(O3), it is needed to investigate the variety of artificial intelligence techniques’ performance, such as machine learning, deep learning and hybrid models. This research aims to effectively predict the hourly ozone trend via fewer input variables. This ozone prediction attempt is performed on d...

Growing population could be one of the main factors which affect the daily increase in road movement . A developing country like Malaysia still requires transport system planning and development. In 2012, nearly 1.5 million daily and public transport trips in Klang Valley represented almost 30% of the daily main public transportation. Development o...

Traffic congestion is one of the issues raised as the development and urbanization moving forward. Bus services still considered as an public transport option to move from one location to another. Since 1957, bus services have been a primary selection not just a big cities like Kuala Lumpur and Georgetown as well as small town in like Alor Setar, K...

The prediction of tropospheric ozone concentrations is vital due to ozone's passive impacts on atmosphere , people's health, flora and fauna. However, ozone prediction is a complex process and the wide range of traditional models is incapable to obtain an accurate prediction. "Artificial intelligence", "machine learning" and "ozone prediction model...

Urban areas are exposed to high traffic volume that can associate to the source of noise pollution. Noise pollution is ranked as the third most harmful environmental pollution after air and water. The objective of this study is to investigate the level of traffic noise pollution at the residential area of Puchong, Selangor. This study is carried ou...

Reliable forecasting of water level is essential for flood prevention, future planning and warning. This study proposed to forecast daily time series water level for Malaysia’s rivers based on deep learning technique namely long short-term memory (LSTM). The deep learning neural network is based on artificial neural network (ANN) and part of broade...

p>The prediction of tropospheric ozone concentrations is very important due to negative effects of ozone on human health, atmosphere and vegetation. Ozone Prediction is an intricate procedure and most of the conventional models cannot provide accurate prediction. Machine Learning techniques have been widely used as an effective tool for prediction....

High level of tropospheric ozone concentration, exceeding allowable level has been frequently reported in Malaysia. This study proposes accurate model based on Machine Learning algorithms to predict Tropospheric ozone concentration in major cities located in Kuala Lumpur and Selangor, Malaysia. The proposed models were developed using three-year of...

An energy system in a country has complex impacts on its economy. A retrenchment of energy supply influences economic activity such as distribution and saving of energy, as well as changes in technology to emphasize energy efficiency. Despite the advantages of hydropower developments in power generation over the past decades, highly controversial i...

Nowadays, Malaysia is facing real problem on traffic issues such as traffic congestion, deficiency of land for parking space and increasing vehicle volume. Cycling is one of alternative approach of transportation to overcome traffic issues. This objective of this study is to forecast the travel demand using E-bike in University and comparison of ca...

The Malaysian government has taken an initiative to mitigate public transport issues in the Klang Valley through the Land Public Transport Commission (SPAD). Several issues were raised by passengers in Klang Valley in regard to public transport, such as long waiting time and poor management of bus service hours. This study is aimed to investigate t...

Multi-purpose advanced systems are considered a complex problem in water resource management, and the use of data-intelligence methodologies in operating such systems provides major advantages for decision-makers. The current research is devoted to the implementation of hybrid novel meta-heuristic algorithms (e.g., the bat algorithm (BA) and partic...

Accurate forecasting of streamflow is desired in many water resources planning and management, flood prevention and design development. In this study, the accuracy of two hybrid model, support vector machine - particle swarm optimization (SVM-PSO) and bat algorithm - backpropagation neural network (BA-BPNN) for monthly streamflow forecasting at Kua...

Water treatment is the process to make the water reusable to consumers. There are several process to treat water such as chlorination, sedimentation and filtration. Water filtration is known to separate substance or object in contaminated water. Method of filtration is vary depending on the location of the targeted material whether it is dissolved...

Streamflow prediction has a significance influence on improving water supply management and flood prevention. The applications of artificial intelligence (AI) have been proved to have better performance as compared to conventional statistical method in streamflow prediction. Therefore, this study proposed on the development of streamflow prediction...

In accessing quality bus service in Putrajaya, 6 attributes were chosen as suggested by experts and summary review of four guidelines in bus transit manual from New Zealand, United Kingdom, United State and Australia. Six (6) attributes was identified, namely Services Hours, Load Factor for passenger, Comparison Car and Bus Travel , Frequency of Bu...

The Transit Capacity and Quality of Service Manual (TCQSM) was chosen based on the review with four manuals and guidelines for the method will be applied. Six (6) attributes, namely Hours of Service, Passenger Load, Transit Auto Travel Time, Service Frequency, Punctuality Performance and Service Coverage were chosen for evaluation.[6][7][8]. A tota...

The application of artificial intelligence techniques for river flow forecasting can further improve the management of water resources and flood prevention. This study concerns the development of support vector machine (SVM) based model and its hybridization with particle swarm optimization (PSO) to forecast short term daily river flow at Upper Ber...

Rainfall and river flow are one of the most difficult elements of hydrological cycle to predict. This is due to tremendous range of variability it displays over a wide range of scale both in terms of space and time. The situation is further aggravated by the fact that rainfall-runoff is also very difficult to measure at scales of interest to hydrol...