Theses & Dissertations

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https://hdl.handle.net/20.500.14170/2002

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Now showing 1 - 5 of 58
  • Publication
    Spatial max-stable with cyclic generalizes extreme value model for extreme ground level ozone
    ( 2024)
    Siti Aisyah Zakaria
    The high level of ground-level ozone (GLO) concentration has serious adverse effects on health problems and affects the environment. This study integrates the spatial max-stable processes with the cyclic generalized extreme value (GEV) model to analyze and forecast extreme GLO levels. Spatial extreme provides a framework for analyzing and modelling the behaviour of rare events considering the extreme data pattern and the characteristics of several stations. One key component in this approach is selecting an appropriate GEV marginal distribution based on the data structure of each monitoring station. The choice of marginal distribution depends on whether there is an apparent trend in the extreme data series. In cases where the data exhibit strong seasonal variation, a stationary model may not be appropriate. This study acknowledges the seasonal variation in GLO data at various monitoring stations, influenced by the interchange of monsoons. Therefore, a seasonal variation model is considered as the marginal distribution for the spatial extreme model. Additionally, the study extends the non-stationary model from univariate cases to the spatial extreme model. This model incorporates a cyclic pattern in location parameters to complement the GEV distribution as a new marginal distribution within the max-stable process, a standard dependency model for spatial extreme observed at different locations. The clustering process using hierarchical cluster analysis (HCA) found that all the stations can be grouped into two clusters depending on the same characteristic of the weekly maxima data. The extremal coefficient between 1 to 2 indicates that the stations are dependent on each other’s within the cluster. Validation of the developed model is crucial for accurate predictions. Synthetic data approximating real data characteristics are generated to validate the model and facilitate predictions of future extreme cases based on return values for specific return periods. Return levels, indicating the average amount of extreme events within a specified return period, are used to predict GLO concentration levels across different locations, enhancing the understanding of GLO concentration patterns based on location categories. The presentation of return level results in return level mapping further aids in visualizing and interpreting the predictions for all monitoring stations. The main finding of this study indicates that the return level of GLO concentration increased as the return period increased. The results show that most of the return levels exceed the guideline of MAAQG for 8-hour average that is 0.06 ppm. Notably, the station in Kota Bahru (CA22) stands out with the lowest return levels, while the Shah Alam (CA25) station exhibits the highest estimated values. This high GLO concentration in Shah Alam may be attributed to its urban location, marked by high traffic density, industrial operations, and diverse meteorological influences. In conclusion, this study is highly significant as it offers valuable insights that can be applied in the fields of environmental and climatology, specifically regarding GLO in peninsular Malaysia. The methodology detailed in this study can be adapted for the analysis of other extreme datasets.
  • Publication
    New algorithm for improving prediction performance in modified radial basis function network
    ( 2020)
    Lim Eng Aik
    In neural networks, the accuracies of the networks are primarily relying on two critical factors, which are the centers and networks weight values. The feed-forward network known as Radial basis function network (RBFN) capable of performing nonlinear approximation on an unknown dataset, classification, pattern recognition, control system, and image processing. However, there are some disadvantages of the RBFN network, such as longer computation time for large datasets, less efficient weight updating, and center selection algorithms that cause low accuracy are identified. Limited data points or overload data points can affect the training of RBFN. Hence, proper size for dataset is required to ensure RBFN is trained using suitable dataset size to lessen the computational time without a significant influence on the accuracy. For RBFN weight updating, the gradient descent (GD) algorithm easily trapped in local minima by random weight generated during the initial stage of training. Meanwhile, the center's selection using the K-means algorithm is known for its sensitivity and high dependency to initial center selection from the input dataset. Therefore, this work proposed solutions for these mentioned disadvantages through modification on a few parts of the RBFN algorithm to improve their performance. First, this work proposed a new dataset reduction formula to obtain a suitable number of a dataset for network training. Next, a modified steepest descent algorithm was proposed for RBFN weight updating during training. Then, a new distance-weighted K-means algorithm is proposed for obtaining more accurate initial centers for RBFN. Finally, this work proposed a new model through a combination of quantum evolutionary algorithm (QEA) and RBFN known as QRBFN. This proposed RBFN demonstrated its abilities in global search and local optimization to effectively provide better accuracy in prediction results. All proposed modified RBFN was tested against the standard RBFN in predictions accuracy on four nonlinear models from literature, and four real-world datasets that consist two time-series datasets (Air pollutant dataset and forex pair EURUSD dataset), and other two datasets are Biochemical Oxygen Demand (BOD) dataset, and Phytoplankton growth dataset. The proposed dataset reduction formula was conducted through experiments where data was tested by a 5 percent step size reduction. The results of this proposed RBFN are compared for root mean square error (RMSE) and area under curve (AUC) values with standard RBFN. The proposed dataset reduction case yielded average results over a 50 percent decrease in time usage and a 20 percent reduction in RMSE. Meanwhile, all proposed RBFN yielded better results and robustness with an average improvement percentage of more than 40 percent in RMSE and AUC results.
  • Publication
    Trigonometric b-spline based approach for solving initial and boundary value problems of dispersive equations
    ( 2019)
    Hamad Mohammed Salih
    Various type of numerical methods are developed by employing spline function for solving dispersive PDEs such as finite difference method (FDM), finite element method (FEM) and finite volume method (FVM). Each method inherits certain drawback like complexity, high computational cost and required the trial function or limitation to certain cases. Due to those constraints, FDM incorporated with B-spline function is introduced to solve partial differential equation (PDE). The main aim of this thesis is to explore the accurate and reliable solution to dispersive PDEs. The cubic trigonometric B-spline method (CuTBS), cubic hybrid B-spline method (CuHBS) and two new methods namely quintic trigonometric B-spline method (QuTBS) and hybrid quintic B-spline (QuHBS) method are chosen with the finite difference scheme to solve the third order dispersive PDEs called Modified Regularised Long Wave equation (MRLW), Benjamin -Bona- Mahony-Burgers equation (BBM-Burgers) and Modified Equal Width equation (MEW). The proposed methods produce the numerical solutions that are found to be better or in good compliance with those present methods in literature. Comparison of the maximum error ( L ) and the Euclidean error ( 2 L ) from the literatures are also done for each example. The performance of the proposed methods are identified to be more accurate than CuTBS and QuTBS method. In order to analyze the stability of the proposed methods, Von-Neumann stability analysis is applied to the linearized schemes. The schemes have been identified to be unconditionally stable. The highlights of the proposed method can be counted as follows: The diagonal matrix obtained from these methods helps in computing accurate solution and can be employed to easily solve PDEs with certain conditions. These methods have an edge over various methods as it approximates the solution at all point in the domain rather than the grid points. The main contribution of this thesis are the development of the quantic splines methods and the applicability to solving dispersive partial differential equations.
  • Publication
    Stability analysis on convection boundary layer stagnation-point flows over a permeable stretching/shrinking surface
    ( 2019)
    Fatinnabila Kamal
    In this thesis, several problems of convection boundary layer flow and heat transfer towards a stretching/shrinking surface along with stability analysis for viscous, nanofluid and micropolar fluids are investigated. There are five problems considered, namely (i) stagnation-point flow and heat transfer over a permeable stretching/shrinking sheet with heat source effect; (ii) magnetohydrodynamic stagnation-point flow towards a permeable stretching/shrinking sheet with slip and heat source/sink effects; (iii) effect three-dimensional stagnation-point flow and heat transfer over a permeable stretching/shrinking sheet with heat source effects in viscous fluid; (iv) MHD stagnation-point flow towards a permeable stretching/shrinking sheet in a nanofluid with chemical reaction; and (v) stagnation-point flow and heat transfer in a micropolar fluid towards a nonlinearly permeable stretching/shrinking sheet. The study starts with the formulations of the mathematical models for every problems. Next, in solving these problems, the governing nonlinear partial differential boundary layer equations are transformed into ordinary differential equations using similarity transformations before being solved numerically using the boundary value problem solver, bvp4c built in Matlab software. The numerical results are then presented in tables and graphs for the skin friction coefficient, the local Nusselt number and the local Sherwood number as well as the velocity, temperature and concentration profiles. The effects of governing parameters have been analysed such as the heat source parameter, the chemical reaction parameter, the suction/injection parameter, the micropolar parameter and the stretching/shrinking parameter. It is observed that the suction/injection effect increase the skin friction coefficient, the local Nusselt number, and the local Sherwood number. Heat source effect has decrease the heat transfer rate. Furthermore, the effect of chemical reaction effect has decrease the local Sherwood number while Micropolar parameter has decrease the skin friction coefficient and heat transfer rate. Further, dual solutions are found for a certain range of the stretching/shrinking parameter. A stability analysis has been carried out to determine which solution is stable for dual solutions exist in all problems considered. The first solution is found to be stable and physically reliable, whereas the second solution is unstable as time passes, thus impractical in the real world applications for a long run.
  • Publication
    Development of linear programming models of water price compliant to the regulation of Ministry of Home Affairs, Indonesia
    ( 2024)
    Tan Kim Hek
    The water tariff and price in Medan, North Sumatera was last reviewed in 2017. The tariff, which is the structure mechanism for determining the water price, must be reviewed once every five years as stipulated in the Regulation of the Ministry of Home Affairs, Indonesia, No. 23, 2006. Currently, the water tariff and price based on the that regulation without any secondary affirmation. Futhermore, the inaccurate water tariff and price that currently being implemented may lead to suboptimal revenue and profit thus affected the business financially. In Medan, the Perusahaan Daerah Air Minum (PDAM) Tirtanadi, is the water operator responsibles for treatment, supplies and distributes the clean and treated water thus it has obligation to charge the customers the price based on the stipulated water regulation. Therefore, the aim of this research is to develop a comprehensive linear programming (LP) model for optimizing the water pricing in Medan within the regulatory framework of the Ministry of Home Affairs, Indonesia. This study is motivated by the pressing need to address the complex interplay between economic efficiency and social equity in water resource management. With Medan's diverse geographical and socio-economic landscape, achieving a balanced water pricing model that meets regulatory compliance while ensuring equitable access, economic viability, and sustainable resource use represents a significant challenge. The research employs a robust LP approach to model water pricing strategies, incorporating various constraints such as production cost, social cost, subsidy, revenue and profit that complied to the water regulatory guidelines. By analysing different water pricing based on customers group and block, this study aims to identify optimal water pricing byrecognising the current error in tariff structure and ensure the potential of new water price in Medan. Key contributions of this research include the development of a novel LP framework that integrates environmental, economic, and social considerations into water pricing decisions. The study also provides empirical insights into the effectiveness of different water pricing mechanisms, offering valuable policy recommendations for water resource management in Medan and similar contexts. Through a rigorous analysis of water pricing strategies, this research contributes to the broader field of water resource management by advancing understanding of how LP can be effectively applied to resolve complex decision-making problems in the context of regulatory compliance and sustainable development goals. This research not only addresses a critical gap in the literature on water pricing and management but also offers practical tools for policymakers, water utilities, and stakeholders involved in the governance of water resources. By proposing a scalable and adaptable LP model, the study underscores the potential for analytical approaches to inform and improve water pricing policies, ultimately contributing to the sustainable management of vital water resources in Medan.