Theses and dissertations (Accounting and Informatics)

Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/4

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Data mining and machine learning : a study of the CO2 emission trends in South Africa
(2024) Mohamed, Ghulam Masudh; Patel, Sulaiman Saleem; Naicker, Nalindren
This study addresses the pressing global issue of elevated carbon dioxide emissions (CO2E), with a particular focus on South Africa (SA), which ranks amongst the world's top emitters and largest in Africa. By introducing a novel integration of Change-point Analysis (CPA) and Machine Learning (ML) techniques, this research addresses significant gaps in CO2E trend analysis. Unlike previous studies, this research applies CPA methodologies within the distinct context of SA, employing algorithms like cumulative sum (CUSUM) and Bootstrap analysis to pinpoint crucial change-points in CO2E data specific to the country. The Bootstrap analysis determines the confidence levels associated with each detected change. Additionally, this study sought to validate historical trends and predict future patterns using ML models, with a specific focus on employing the AdaBoost ensemble learning technique. Drawing on insights from a Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA)-based systematic review, the research selects input variables based on the factors identified as significant contributors to CO2E, ensuring the models capture the relevant variables effectively. The results of the systematic review highlight energy production and economic growth as key drivers of CO2E, thus validating their selection as input data for constructing the CPA and ML models. To conduct this study, secondary data was obtained from the World Bank's Open Data initiative data repository, a common source for environmental research. This selection was justified by a literature review, which highlighted the reliability and applicability of this data source. The CPA results reveal significant change-points in electricity generation, economic growth, and CO2E, with an average confidence level of 94%, indicating the accuracy of this analytical approach. Moreover, the CPA results emphasise the relationship between economic growth, electricity production, and CO2E in SA. Before forecasting future CO2E trends, the effectiveness of the AdaBoost regressor in enhancing model performance was benchmarked against traditional ML algorithms, including Linear regression, Polynomial regression, Bayesian Linear regression and K-Nearest Neighbors (KNN) regression, to determine the most effective technique for forecasting CO2E. The researcher evaluated model performance using key regression ML performance metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2) score, and an additional accuracy score introduced by the researcher. Notably, the AdaBoost models demonstrated superior performance, with an average RMSE score of 10,143.17 kilotons (kt), MAE score of 9,642.64 kt, R2 of 0.90, and accuracy of 96.74%. The study also revealed that, on average, models that were trained using the AdaBoost algorithm surpassed traditional ML models, in terms of performance. They achieved a reduction in RMSE score by 6,417.29 kt, a decrease in MAE score by 4,358.09 kt, an increase in R2 score by 0.07 and enhanced accuracy by 0.60%. Additionally, a comparative analysis of the repeated holdout methods and cross-validation techniques was conducted, with results revealing that repeated holdout had a more significant impact on model performance. After excluding outliers, the average improvement in crossvalidation results, due to the repeated holdout method, was a decrease of 783.32 kt for RMSE, a reduction of 1,289.39 kt for MAE, and an increase of 0.88% for accuracy. The extent to which the repeated holdout method improved the performance of ML models that were integrated with cross-validation techniques, was correlated with the initial model performance. For ML models with RMSE and MAE scores equal to or exceeding 15,000 kt, the findings indicate that the repeated holdout methods studied should enhance performance by at least 2,000 kt. Similarly, an improvement of nearly 3% or higher in accuracy was noted, when the crossvalidation value for this metric was 94% or lower. The AdaBoost model, integrated with repeated holdout, was selected as the optimal model, as evidenced by the results, for forecasting CO2E in SA from 2021 to 2027. The forecasted CO2E trends validate that energy production and economic growth are indeed the primary drivers of CO2E in SA, as previously highlighted by the CPA model. This underscores the importance of addressing these factors to effectively mitigate carbon emissions in the country. Moreover, the forecasted results indicate that SA is unlikely to meet the global temperature limit of 1.5 degrees Celsius by 2030, given the trajectory showing a shortfall in achieving the target level of 334 million tonnes (Mt) of CO2E, agreed upon in the Paris Agreement. However, the country did meet its CO2E commitments outlined in the 2030 National Development Plan, showing some progress towards environmental sustainability. Nonetheless, the failure to meet these targets at their lower ranges suggests the need for further efforts to reduce carbon emissions, which is crucial for aligning with the Paris Agreement objectives and achieving a zero net emission rate by 2050. This highlights the importance of ongoing initiatives to enhance environmental policies and practices in SA. Future research should focus on integrating load-shedding dynamics into the analysis to examine and confirm its effects on energy production, economic growth, and CO2E in SA. Additionally, future research should focus on forecasting future change-points for the socio-economic indicators or variables utilised in this study. This can help policymakers anticipate fluctuations and devise proactive strategies, to address environmental and economic challenges effectively. It is also recommended that future research consider the output of renewable energy production, when analysing CO2E trends.
Development of a frugal crop planning decision support system for subsistence farmers
(2016-12) Friedland, Adam; Olugbara, Oludayo O.; Duffy, Kevin Jan
This dissertation reports on the original study that undertakes the development of a frugal information system to support subsistence farmers through the use of the Agricultural Production Systems Simulator (APSIM) as a support tool to assist them in optimal strategic decisions making. The field of agriculture is vast and in-depth and a number of critical factors like soil type, rainfall and temperature are involved that farmers have to take into account. Farmers persistently face the challenges of increasing and sustaining yields to meet with the populaces demand with often limited resources, which makes strategic decisions on what to plant, when to plant, where to plant and how to plant in a particular season imperative. The way in which this study attempts to solve this agricultural decision making problem is with the use of the APSIM. This technology platform provides an advanced simulation of agricultural systems that can enable subsistence farmers to simulate a number of variables ranging from plant types, soil, climate and even management interactions. This research presents a frugal web-based crop planning decision support system that subsistence farmers can take advantage with the use of the APSIM. The APSIM platform was used to run simulations for various regions with the results containing the expected level of success along with other useful information for a specified crop in the vicinity, using state of the art software platforms and tools ranging from Google Maps application programming interfaces, Microsoft’s model view controller framework, JavaScript and others. The validity of this system was tested through a number of design science methods including structural testing and illustrative scenarios, show capability of the information system. The results obtained from this evaluation show a small but powerful tool that has the capability of servicing a multitude of farmers with crop management decisions.
An exploration of the views of manufacturing small medium enterprise owners with regards to green tax incentives in the eThekwini region of KwaZulu-Natal
(2017) Kalidin, Uveer; Bhagwan, Raisuyah; Reddy, D.
The study was to explore the awareness of owners of small medium enterprises with regards to green tax incentives; to identify their attitudes towards such green tax incentives and the possible impact it will have on small medium manufacturing enterprises going green.This study used a quantitative research design, specifically a descriptive survey approach. A census sample was drawn through a list provided by the Durban Chamber of Commerce. A survey questionnaire was the primary data collection tool used. At total of 152 questionnaires were distributed and a 104 were received yielding a response rate of 68 %. The data showed that small medium enterprises were concerned about the environment, and acknowledged that their business activities have a negative impact on the environment. The study also established that small medium enterprise owners are concerned with the impact that climate change will have on their business operations. A majority of the sample considered green taxation to be an important driver that would allow businesses to become eco-friendly. Owners were found to support the utilization of green taxation incentives and were considering using green taxation as part of future business decision making, thus ensuring that meet corporate social responsibility. It was concluded that there was a need for the government to put more focus into creating awareness of global warming and climate change. There was also a need to find more innovative ways of promoting eco-consciousness and green sustainable practices and the need to draft and identify new green taxation legislature that caters for public and small medium enterprises.
Intelligent decision support systems for managing the diffusion of social computing in school-based ubiquitous learning
(2022-01-06) Sam, Caitlin; Naicker, Nalen; Rajkoomar, Mogiveny
The past decade has seen an explosion in social media applications. Most adolescents in South Africa have access to social media applications despite the country’s economic inequalities. The drive for social media applications is important to enhance human connectedness. In unprecedented times social computing can be utilised in school-based learning to benefit learners. Climate change has propagated extreme weather patterns which has increased the occurrence of natural disasters and diseases. The emergence of the novel Coronavirus resulted in most countries implementing nation-wide forms of lockdown to curb the spread of infection. Consequently, these adverse phenomena across the globe are disruptive to conventional schoolbased education. Ubiquitous learning (u-learning) relates to learning that occurs at any place without time constraints. In some schools, u-learning has become a conventional learning approach and pedagogy but there are various education and technology attributes that must be addressed for the penetration of social computing in schools. Therefore, there is a need to guide learners and school-based instructors on their preferences of digital access and social media applications. The main aim of the study was to investigate social media-driven Intelligent Decision Support Systems using live data, to assist instructors and learners manage the diffusion of social computing in school-based ubiquitous learning. In pursuing this study, a quantitative research methodology was used for the collection of data from learners and instructors from the schools in the eThekwini Region, namely, Umlazi District and Pinetown District of KwaZulu-Natal Province, South Africa. A survey was conducted to elicit data from participants on their use of social computing for u-learning. The approximate target population size was 129 421 individuals with a sample size of 384 participants. There were 260 respondents with an acceptable response rate of 67,71%. The study derived attributes for ranking the social media applications and Principal Component Analysis which is an unsupervised Machine Learning algorithm reduced the dimensionality of the attributes. The multi-criteria decision-making algorithm, Fuzzy Technique of Order Preference Similarity Ideal Solution was implemented to rank the social media applications in line with the dimensionality reduced criteria based on the subjective decisions of expert decision makers. Data Envelopment Analysis, another multi-criteria analysis method was utilised to score the efficiency of the devices for u-learning. The results showed the most precise, mathematically approved social media applications and devices that can support u-learning in schools. An automated application based on research evidence using Intelligent Decision Support Systems was designed as a research output.