Faculty of Science (ETDs)

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First principle study of inorganic metal halide perovskites for solar cells application
(University of the Witwatersrand, Johannesburg, 2024-08) Maleka, Prettier Morongoa; Maphanga, Regina R.; Ntwaeaborwa, Odireleng Martin
All-inorganic halide perovskites have received significant attention as semiconductor materials due to their outstanding opto-electronic properties, which have achieved power conversion efficiency (PCE) of up to 25% in perovskite solar cells. Their exceptional characteristics include long diffusion lengths for electrons and holes, tuneable band gap, high absorption coefficients, small effective masses, high carrier mobility, and simple reproducible process. Despite these excellent properties, metal halide perovskites have drawbacks that negatively affect the PCE and stability of the perovskite solar cell devices. This study investigated all-inorganic halide perovskite, CsPbI3, by employing the first-principle density functional theory (DFT) method. Firstly, the effect of mixing halides on X-site was investigated to probe the structural stability and opto-electronic properties. The structural, electronic, optical, mechanical and thermodynamics properties of CsPbI3 – xBrx were investigated using three exchange correlation functionals, namely, LDA, GGA-PBE and SCAN meta-GGA. The findings revealed that mixed halide perovskites have an ideal direct energy band gap for suitable photovoltaic applications. For GGA-PBE and SCAN meta GGA exchange correlation functionals, the determined energy band gap ranges from 1.33 eV and 1.877 eV, whereas the LDA band gap ranges between 0.960 eV and 1.137 eV. The electronic band gaps predicted by GGA-PBE and SCAN meta GGA exchange correlation, which offer better precision compared to LDA suggest that Br-doped CsPbI3 – xBrx perovskite is suitable for photons absorption from near-infrared to visible regions of the spectrum. The modification of the band gap is an essential feature of photovoltaics, as it enables the optimization of solar cell performance. In addition, the systems CsPbI3, CsPbI2Br, CsPbIBr2, and CsPbBr3 exhibited exceptional mechanical and thermodynamic stability. Secondly, perovskites that are considered for photovoltaic applications contain toxic element lead (Pb) on the B-site, which limits application of these perovskites in photovoltaic devices. In this study, substitution of toxic Pb with a smaller percentage of selected transition metals was investigated in order to alleviate the toxicity problems. Thus, CsPbI3 doped with 12.5 % concentration of transition metal, Mn, Fe, Ni and Zn was investigated using DFT. The results showed that transition metal doped-CsPbI3 perovskites enhanced the absorption of this material, although they are all indirect band gaps semiconductors. All the materials were found to be mechanically stable. Lastly, cluster expansion which is a method that is capable of describing the concentration dependent thermodynamic properties of materials while maintaining DFT accuracy, was used to predict new (CsPbI/Br)3 structures. The cluster expansion method generated 42 new stable (CsPb)xIyBrz (where x = 1 to 3 and y and z = 1 to 8) structures and these were ranked the meta-stable structures based on their formation enthalpies. Monte Carlo calculations showed that CsPbI0.5Br0.5 composition separates into different phases at 300K, but changes to homogeneous phase at 700 K, suggesting that a different phase of CsPbI3 may exist at higher temperature. Among the 42 predicted structures, randomly selected structures around iodide rich, 50:50 and bromine rich sites were studied further by determining their electronic, optical, mechanical and thermodynamic properties using DFT. The materials possess similar properties as cubic Br doped CsPbI3 perovskites. The mechanical properties of these compounds revealed that they are ductile in nature and mechanically stable. In summary, the thesis present a novel work on introduction of impurities into CsPbI3 perovskite material as well as compositional engineering to alter its electronic and optical properties for solar cells application.
Optimisation of Kick Latency for Enhanced Performance of Robots in the RoboCup Three-Dimensional League through Proximal Policy Optimisation (PPO)
(University of the Witwatersrand, Johannesburg, 2024-07) Nekhumbe, Humbulani Colbert; Ranchod, Pravesh
This study aimed to enhance the kicking ability of Nao robots in the three-dimensional RoboCup simulation by addressing a crucial challenge observed in the University of Witwatersrand RoboCup team. The focal challenge revolved around a noticeable delay and slow movement manifested by the robot during ball kicks, leading to vulnerabilities in ball possession against opposing teams. To surmount this challenge, the implementation of Proximal Policy Optimisation (PPO), a methodology pioneered by OpenAI, was advocated. The precise objective was to optimise kick parameters, with a primary emphasis on curtailing kick latency. This optimisation aimed to ensure swift and accurate execution across various kicking scenarios, encompassing actions like propelling the ball into the opponent’s territory to bolster ball possession and thwart adversary manoeuvres. Harnessing the iterative advancements embedded in PPO, the successor to Trust Region Policy Optimisation (TRPO), the endeavour was to refine the kicking behaviour of Nao robots. This optimisation process significantly reduced the observed kick delay, and this made the robot more agile and effective at competing in the complex three-dimensional RoboCup simulation environment. The study’s outcomes highlighted substantial progress in reducing kick latency and improving the adaptability of robotic soccer players, opening up possibilities for further exploration in reinforcement learning for autonomous agents.
Search for high-mass resonances in the Zgamma channel and Quality assurance of Scintillation detector modules of Tile Calorimeter Phase-I Upgrade of the ATLAS detector
(University of the Witwatersrand, Johannesburg, 2024-09) Mokgatitswane, Gaogalalwe; Ruan, Xifeng; Solovyanov, Oleg; Mellado, Bruce
This thesis presents a search for narrow, high-mass resonances decaying to a Z boson and a photon (Zy) in the final state. The analysis utilizes the full Run 2 dataset collected by the ATLAS experiment at the CERN Large Hadron Collider (LHC), corresponding to an integrated luminosity of 140 fb-1 of proton-proton col- lisions at a center-of-mass energy of ps = 13 TeV. The search focuses on a mass range of 220 GeV and 3400 GeV, aiming to identify deviations from the expected background arising from Standard Model processes. A small excess is observed at 250 GeV within the area of interest, with a combined significance of 2.1 standard deviations, indicating the need for further investigation with more data. Upper limits are set on the production cross-section times branching ratio for resonances decaying to Zy across the investigated mass range. When considering spin-0 resonances produced through gluon-gluon fusion, the observed limits at a 95% confidence level range from 65.5 fb to 0.6 fb. For spin-2 resonances produced via gluon-gluon fusion (with quark-antiquark initial states), the limits vary between 77.4 (76.1) fb and 0.6 (0.5) fb. The thesis also highlights the successful Phase-I upgrade of the Tile Calorimeter in the ATLAS detector, ensuring its continued performance. This involved the replacement of degraded Gap-Crack scintillators and Minimum Bias Trigger Scintillators (MBTS) with non-irradiated ones, along-side optimising their geometry, all in preparation for data taking during LHC Run 3. These upgrade endeavors encompassed the design of new Gap-Crack and MBTS counters, including extensions to higher rapidity, the assembly of these counters, their rigorous qualification, and characterization using radioactive sources (90Sr and 137Cs), along with their seamless integration onto the ATLAS detector.
Incorporating complex adaptive systems concepts in ontology driven Bayesian network models : towards resolving wicked problems
(University of the Witwatersrand, Johannesburg, 2024-08) Semwayo, Daniel Tembinkosi; Ajoodha, Ritesh
Wicked problems are complex ill defined problems very difficult to solve tractably using analytical methods and interventions. They include problems like pandemics, climate change effects, traffic jams, and financial market crashes. Attempts at solving such problems using analytical methods tend to produce counter-intuitive, unpredictable pathological outcomes. Wicked problems emerge, in part from the character of complex adaptive systems, and from stakeholder disagreements on their definition and resolution. We argue that baseline Bayesian models do not have adequate constructs to provide compact, and tractable modelling support for wicked problems. Applying an iterative and rigorous abductive design science research methodology, an ontology driven Bayesian modelling framework is applied to design the Granular Niche probabilistic Bayesian model, a formal, ontologically sound, and explainable artificial intelligence model, incorporating complex adaptive systems theory concepts: context; granularity; and perspective, as constructs. Using evaluation metrics from applicable kernel theories comparative evaluation of the model is carried against baseline Bayesian models. The results indicate that the novel model out-performs baseline Bayesian models against the following evaluation criteria: i) complex adaptive systems’ representation accuracy and precision; ii) structure learning; iii) parameter estimation; iv) knowledge discovery; and v) explicitly modelling and reconciling divergent multiple stakeholder perspectives of a given wicked problem.
Magnetic field strength estimations for the main phases of solar cycles 13-24 using magnetohydrodynamic Rossby waves in the lower tachocline
(University of the Witwatersrand, Johannesburg, 2024-09) Morris, Tania Mari; Duba, Thama
The magnetic field strength (MFS) estimates used by the existing space weather prediction models (SWPMs) are inaccurate. Consequently, it has been indicated that there is a need to find solutions to rectify the wrong assumption that the magnetic field remains constant in strength and location throughout the solar cycle. This study explores a solution to this problem by increasing the granularity and accuracy of the previous MFS estimations by calculating them for the main phases of the solar cycle (solar minimum and maximum) per hemisphere for solar cycles 13-24. A dispersion relation of the fast magnetohydrodynamic (MHD) Rossby wave was derived analytically in spherical coordinates that included a toroidal magnetic field and latitudinal differential rotation, which adequately captures the dynamics of the lower tachocline. Secondly, a change to the methodology of calculating the MFS that utilises the established connection between the observed Rieger-type periodicity (RTP) in solar activity of 150-190 days and the fast MHD Rossby wave in the lower tachocline was proposed. Furthermore, a new magnetic field profile (MFP) of Bφ = B0 sin(6Θ) was introduced to improve the model results. This MFP has a maximum and minimum value at the same latitudes associated with sunspot appearances during these extreme solar cycle phases. Consequently, the MFS values were calculated at a latitude of 29◦ (solar min) and 16◦ (solar max) using the hemispheric RTP data. The average MFS (RTP) for the solar min and solar max in the dominant hemisphere was established to be 8 kG (212 days) and 76 kG (163 days), respectively. For the non-dominant hemisphere, the average MFS was established to be 5 kG (213 days) and 50 kG (183 days) for the solar min and max, respectively. The results of this study show a significant difference in the results based on latitude. The findings have also revealed that the periodicity of increased solar activity associated with a specific MFS is affected not only by the solar cycle strength and hemispheric asymmetry but also by the solar cycle phase (or latitude) considered. Additionally, we strongly argue that this study’s MFS results represent reality more closely than previously calculated results. Therefore, we propose that the MFS estimates reported in this study should be considered for the input to various existing space weather prediction models.
Towards Lifelong Reinforcement Learning through Temporal Logics and Zero-Shot Composition
(2024-10) Tasse, Geraud Nangue; Rosman, Benjamin; James, Steven
This thesis addresses the fundamental challenge of creating agents capable of solving a wide range of tasks in their environments, akin to human capabilities. For such agents to be truly useful and be capable of assisting humans in our day-to-day lives, we identify three key abilities that general purpose agents should have: Flexibility, Instructability, and Reliability (FIRe). Flexibility refers to the ability of agents to adapt to various tasks with minimal learning; instructability involves the capacity for agents to understand and execute task specifications provided by humans in a comprehensible manner; and reliability entails agents’ ability to solve tasks safely and effectively with theoretical guarantees on their behavior. To build such agents, reinforcement learning (RL) is the framework of choice given that it is the only one that models the agent-environment interaction. It is also particularly promising since it has shown remarkable success in recent years in various domains—including gaming, scientific research, and robotic control. However, prevailing RL methods often fall short of the FIRe desiderata. They typically exhibit poor sample efficiency, demanding millions of environment interactions to learn optimal behaviors. Task specification relies heavily on hand-designed reward functions, posing challenges for non-experts in defining tasks. Moreover, these methods tend to specialize in single tasks, lacking guarantees on the broader adaptability and behavior robustness desired for lifelong agents that need solve multiple tasks. Clearly, the regular RL framework is not enough, and does not capture important aspects of what makes humans so general—such as the use of language to specify and understand tasks. To address these shortcomings, we propose a principled framework for the logical composition of arbitrary tasks in an environment, and introduce a novel knowledge representation called World Value Functions (WVFs) that will enable agents to solve arbitrary tasks specified using language. The use of logical composition is inspired by the fact that all formal languages are built upon the rules of propositional logics. Hence, if we want agents that understand tasks specified in any formal language, we must define what it means to apply the usual logic operators (conjunction, disjunction, and negation) over tasks. The introduction of WVFs is inspired by the fact that humans seem to always seek general knowledge about how to achieve a variety of goals in their environment, irrespective of the specific task they are learning. Our main contributions include: (i) Instructable agents: We formalize the logical composition of arbitrary tasks in potentially stochastic environments, and ensure that task compositions lead to rewards minimising undesired behaviors. (ii) Flexible agents: We introduce WVFs as a new objective for RL agents, enabling them to solve a variety of tasks in their environment. Additionally, we demonstrate zero-shot skill composition and lifelong sample efficiency. (iii) Reliable agents: We develop methods for agents to understand and execute both natural and formal language instructions, ensuring correctness and safety in task execution, particularly in real-world scenarios. By addressing these challenges, our framework represents a significant step towards achieving the FIRe desiderata in AI agents, thereby enhancing their utility and safety in a lifelong learning setting like the real world.
Evaluation of radiation damage on lutetium-aluminium and gold for practical applications using proton irradiation as a surrogate for neutrons
(University of the Witwatersrand, Johannesburg, 2024-10) Temaugee, Samuel Terungwa; Mavunda, Risimati D.; Usman, Iyabo T.
An understanding of the interaction of energetic radiations from particles such as protons, neutrons, and photons, with the microstructure of materials is crucial for predicting their bulk morphological response in extreme radiation environments. Exposure to these radiation species could lead to changes in the microstructural properties that, in turn, affect the mechanical and physical properties of the macroscopic matter. This thesis investigated the resilience of materials, specifically Au and Lu-Al, to radiation damage, employing computational simulation methods and experimental techniques. The study aims to provide critical insights into the radiation damage sturdiness of Au and Lu-Al, considering their application in reactor technology and other extreme radiation environments. Monte Carlo-based methods were employed to calculate radiation damage in the samples resulting from neutron and proton irradiation, utilizing MCNP6.2 and SRIM-2013, respectively. The objective was to compare ion beam irradiation with neutrons with a view to utilizing proton irradiation as a surrogate for neutron irradiation. Three different state-of-the-art characterization techniques—X-ray diffraction (XRD), High-Resolution Transmission Electron Microscopy (HRTEM), and Flash Differential Calorimetry(F-DSC)—were employed to evaluate damage in the materials before and after proton irradiation using the CLASS Accelerator at MIT, USA. The results of the study indicated that protons within the energy range 0.1 to 1.0 MeV produced similar types of damage in the materials as would neutrons (spectrum 0< E≤20 MeV at SAFARI reactor), suggesting protons as an alternative to neutron irradiation. Defect characterization in the materials using XRD and HRTEM techniques revealed dislocation loops and lines in both Lu-Al and Au, as well as Stacking Faults Tetrahedra (SFT) in the Au material. These defects with proton irradiation were similar to those observed with neutron irradiation in Au and other aluminum alloys. The estimated defect number density ranged from 0.7 to 4.8 × 1014 m−2, showing an increase with rising displacements per atom (dpa) or proton fluence post-irradiation. Lu-Al exhibited higher defect density values than Au, consistent with Monte Carlo simulations. Furthermore, results from the Flash DSC technique revealed significant changes in the characteristics of the power-temperature profiles (melting curves) of Lu-Al as dpa increased, offering insights into radiation-induced processes such as phase transition and precipitate stability at specific defect annealing temperatures. These findings are crucial for radiation damage studies for the binary alloy and warrant further investigation. The observed microstructural defect densities were relatively high, and prolonged exposure of the materials to higher doses could lead to further changes in microstructural properties, consequently influencing the physical and mechanical properties of the macroscopic material.
Physical property studies, tunnel numerical simulations and in-mine seismic experiments to image the gold orebody at South Deep Gold Mine
(University of the Witwatersrand, Johannesburg, 2024-09) Mulanduli, Omphulusa; Manzi, Musa
The investigation endeavors to assess the physical characteristics of deep borehole cores within the Upper Elsburg Reefs (UER) of the South Deep gold mine of the West Rand goldfield. Specifically, these cores are sourced from three boreholes situated approximately 2.6 km beneath the surface within the confines of the South Deep gold mine. The focal point of this study lies in non-destructive testing methods aimed at elucidating the intrinsic attributes of these rocks, with particular attention directed towards seismic velocities and densities. These measurements hold paramount importance in conducting numerical simulations to designing the in-mine (or tunnel) seismic reflection surveys acquired at South Deep gold mine, as part of the ERA-Min3 FUTURE (Fiber-optic sensing and UAV-platform techniques for innovative mineral exploration) project. Cultivating a profound comprehension of the seismic velocities and densities across diverse rock formations can significantly augment the interpretation of seismic reflections, thereby facilitating more refined assessments of subsurface geology and structural configurations. In pursuit of this goal, our study endeavors to delve into the fundamental acoustic properties of the gold-rich UER, with the overarching aim of deepening our understanding of its seismic reflectivity. To realize this objective, a comprehensive array of physical measurements, encompassing ultrasonic velocities and bulk densities, were conducted on drill-core specimens. To accurately portray the physical attributes of the lithological units under scrutiny, a total of twenty-four samples were subjected to exhaustive analysis for density and seismic velocity utilizing a spectrum of methodologies. Density determinations were procured through a diverse set of techniques, including dimensional assessments, employment of the KT20 MagSus tool, and utilization of the SNOWREX AHW-3 Professional Weighing Scale boasting a heightened sensitivity of 0.01 g. Ultrasonic measurements were undertaken employing the Proceq Pundit PL 2000 ultrasonic pulser velocity tester, equipped with two pairs of transducers boasting a center frequency of 54 kHz. The in-mine seismic survey was acquired to delineate geological structures that crosscut and displace the orebody. The study locale encompasses three distinct rock formations: the UER, gold-bearing conglomerate units (termed reefs), basaltic lava, and dyke specimens. The UER primarily comprises quartzites, exhibiting a P-wave velocity range of 5202-5802 m/s, an S-wave range of 3037-4768 m/s, and bulk densities spanning from 2.66 - 2.71 g/cm³. Conglomerate reefs exhibit a P-wave velocity range of 4467-5970 m/s, an S-wave range of 4040-4854 m/s, and bulk densities ranging from 2.67-2.94 g/cm³. Lava samples extracted from the boreholes showcase a P-wave velocity range of 5916 - 6711 m/s, an S-wave range of 3275-5659 m/s, and bulk densities spanning from 2.75-2.90 g/cm³. Singular dyke samples were encountered, exhibiting a P-wave velocity of 5921.5 m/s, an S-wave velocity of 5385 m/s, and a density of 2.85 g/cm³. The study employed the synth-seis code to simulate 1D seismic responses based on borehole data collected from the mine, aiming to validate findings from velocity and density measurements. Analysis of the seismograms indicated notable contrasts between conglomerates and quartzites, particularly evident in density and S-wave measurements, suggesting potential for improved rock discrimination with alternative seismic sources. Additionally, 2D numerical simulations were conducted to model wave propagation in the Upper Elsburg Reef (UER), revealing discrepancies between simulated and synthetic seismogram results, indicating potential limitations in seismic imaging. Furthermore, ray tracing was used to design a seismic survey inside the mine along the tunnel floor to image VCR (Ventersdorp Contact Reef) orebody and other geological structures. The real seismic survey was finally conducted inside the tunnel (SDT1), demonstrated the value of in-mine reflection seismic surveys for mapping geological structures at significant depths, which would otherwise be costly and logistically challenging. Despite noise interference from mine operations, processing algorithms enabled extraction of reflections and structural mapping from the dataset, underscoring the importance of such surveys in mining exploration and planning.
Applications of Recurrent Neural Networks in Modeling the COVID-19 Pandemic
(University of the Witwatersrand, Johannesburg, 2024-03) Hayashi, Kentaro; Mellado, Bruce
This study attempted to introduce moving averages and a feature selection method to the forecasting model, with the aim of improving the fluctuating values and unstable accuracy of the risk index developed by the University of Witwatersrand and iThemba LABS and used by the Gauteng Department of Health. It was confirmed that the introduction of moving averages improved the fluctuation of the values, with the seven-day moving average being the most effective. For feature selection, Correlation-based Feature Selection(CFS), the simplest of the filter methods with low computational complexity, was introduced as it is not possible to spend as much time as possible on daily operations due to providing information timely. The introduction of CFS was found to enable efficient feature selection.
Capability of multi-remote sensing satellite data in detecting and monitoring cyanobacteria and algal blooms in the Vaal dam, South Africa
(University of the Witwatersrand, Johannesburg, 2024-03) Obaid, Altayeb Adam Alsafi; Adam, Elhadi M.I.; Ali, Khalid A.
Vaal Dam is a large dam in South Africa. It is the primary source of potable water for the metropolitan and industrial areas of Gauteng province and other surrounding areas. The dam's surface area is about 320 km². It’s the second biggest dam in South Africa in terms of surface area, and it drains a catchment area of approximately 38,000 km². The dam's total capacity is about 2.603 × 10⁶ m³ (Haarhoff and Tempelhoff, 2007). The dam catchment area holds various anthropogenic activities, including major agricultural activities, mining, and some industrial activities (Obaid et al., 2023, Du Plessis, 2017), as well as many formal and informal settlements. The dam water is strongly affected by such activities, releasing chemical, physical, and biological contaminants and dissolved urban effluents, most of which enrich the nutrients that reach the dam water in some way. Water resources assessment and monitoring are crucial practices due to their direct contribution to the effective use of such resources. They require precise information about the water quantity and quality. Monitoring of inland water resources has been conducted using in-situ sampling and in-vitro measurement of the water quality constituents. However, these methods have limitations such as high cost, labor-intensive limited spatial and temporal coverage, and time consumption. Over the last few years, remote sensing has been examined for water quality monitoring as a cost- effective system. This research has tested satellite remote sensing to detect some water quality parameters in the Vaal Dam of South Africa. The main objective of this research is to examine the recent generation multispectral satellite sensors, Sentinel-2 MSI, and Landsat-8 OLI data to detect and assess chlorophyll-a and cyanobacteria in the Vaal Dam, South Africa to be used as a cost-effective monitoring tool. To achieve the objective, the research first aimed to understand how the spatial and temporal dynamics of land use, and land cover (LULC) impact algal growth in the dam reservoir. Land use land cover classification was conducted in the catchment area of the Vaal Dam using a pixel-based classification method. Landsat data for the period from 1986 to 2021 were classified using a random forest (RF) classifier in seven-year intervals (1986, 1993, 2000, 2007, 2014, and 2021). Applying the RF classifier revealed that overall classification accuracies (OA) ranged from 87% in the 2014 classified image to 95% in the 2007 image. The change-detection analysis revealed the continuous increase of the settlement class owing to the continuous population growth. A lot of anthropogenic activities associated with population growth have been recognized to release contaminants into the surrounding environment and might end up reaching the water resources causing significant deterioration. As a result, Vaal Dam encounters significant nutrient input from multiple sources within its catchment. This situation raised the frequency of the Harmful Algal Blooms (HABs) within the dam reservoir during recent years. The study also performed a time series analysis for the potential nutrients expected to be the enhancing factors for algal blooms in the Vaal Dam. Using chlorophyll−a (Chl−a) as a proxy of HABs, along with the concentrations of potential nutrients, statistical measures, and water quality data were applied to understand the trend of selected water quality parameters. These parameters were: Chl−a, total phosphorus (TP), nitrate and nitrite nitrogen NO₃NO₂_N), organic nitrogen (KJEL_N), ammonia nitrogen (NH₄_N), dissolved oxygen (DO) and the water temperature. The results reveal that the HAB productivity in the Vaal Dam is influenced by the levels of TP and KJEL_N, which exhibited a significant correlation with Chl−a concentrations. From the Long- term analysis of Chl−a and its driving factors, some very high values of Chl−a concentrations and its driving factors TP and KJEL_N were recorded in erratic individual dates which suggested some nutrients rich in wastes find their way to the dam. Another important notice was that the average Chl-a concentration significantly increased during the period of the study (1986 to 2023) it increased from 4.75 μg/L in the first decade (1990–2000) to 10.51 μg/L in the second decade (2000–2010) and reaching 16.7 μg/L in the last decade (2010–2020). Additionally, Chl−a data extracted from Landsat-8 satellite images was utilized to visualize the spatial distribution of HABs in the reservoir. The satellite data analysis during the last decade revealed that the spatial dynamics of HABs are influenced by the dam’s geometry and the levels of discharge from its two feeding rivers, with higher concentrations observed in meandering areas of the reservoir, and within zones of restricted water circulation. These spatial distribution patterns of HABs are associated with spatial variations of algal species in term of domination through the seasons of the year. The research also examined the utility of remote sensing techniques for mapping algal blooms using the current generation Sentinel-2 and Landsat-8 data. The effectiveness of some band ratio indices in the blue-green and red-near infrared wavelengths was tested. The results suggested that the blue-green band ratio of Landsat-8 [Rrs(560)/Rrs(443)], and red/NIR of Sentinel-2 [Rrs(705)/Rrs(665)] were found to be the best indices for Chl-a retrieval in the Vaal Dam. Results for the Landsat OLI dataset showed R² = 0.89; RMSE = 0.36 μg/L, P < 0.05, and the Sentinel MSI dataset revealed R² = 0.75; RMSE = 0.48 μg/L, P < 0.05 which is a high degree of accuracy. As the potential toxicity comes from the cyanobacterial bloom, the study examines different models to assess and map cyanobacteria concentration in the dam reservoir. Sentinel-2 and in-situ hyperspectral data have been used. None of the Sentinel-2 band ratios showed a significant correlation with the laboratory-measured values of the cyanobacteria. The in-situ measured Hyperspectra showed strong correlations between the band ratios Rrs(705)/Rrs(655) and Rrs(705)/Rrs(620), and the measured cyanobacteria (R² = 0.96 and R² = 0.95 respectively). Chlorophyll−a concentration was retrieved using band ratio indices in the red-NIR region. The strongest correlation was found between the retrieved Chl−a of band ratio Rrs(705)/Rrs(665) and the laboratory-measured Chl−a concentrations for both reflectance datasets. This correlation resulted in an R² value of 0.78 for Sentinel-2 reflectance data and an R² value of 0.93 for in-situ hyperspectral data. A Semi-analytical algorithm for estimating the Chl−a and phycocyanin (PC) pigments has also been examined. The algorithm uses the ratio of the calculated Chl−a absorption at 665 and phycocyanin absorption at 620 nm to their specific absorption coefficients a∗ (655) and a∗ (620) to estimate the concentration of Chl−a and phycocyanin respectively. It resulted in a strong correlation with measured chlorophyll-a, R² = 0.95. The algorithm also strongly correlated with measured cyanobacteria using the absorption to specific absorption ratio at 620 nm (R² = 0.97). However, the estimated values of cyanobacteria using a Semi-analytical algorithm resulted in cyanobacterial concentration values a little bit higher compared to the measured ones, hence, some factors used by the model need to be adjusted to the Vaal Dam site for better estimations. This research revealed that using band ratio indices of Landsat-8 and Sentinel-2 data are valuable tools for mapping chlorophyll-a in the Vaal Dam, a key indicator of phytoplankton biomass. Furthermore, using the semi-analytical algorithm with hyperspectral data is key for estimating the cyanobacteria concentration in the dam water. Models developed in this research will significantly improve near-real-time and long-term chlorophyll-a monitoring of the Vaal Dam. It will effectively help researchers and environmental agencies monitor changes in algal biomass of the dam water to address public health issues related to water quality. It helps to identify areas of high nutrient input and assess the effectiveness of water quality management strategies. It is of prime importance that the developments within the catchment of the Vaal Dam be carefully considered as it is one of the primary sources of dam water. The research recommends implementing the existing regulatory policies for effluent dispersal within the catchment to protect ecosystem functioning and water resources from further deterioration in their quality. It also recommends regular monitoring to detect real-time changes in HABs using satellite remote sensing.

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