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After years of drought, the rainy season is always welcomed. Unfortunately, this can also herald widespread flooding which can result in loss of livelihood, property, and human life. In this study a Bayesian network is used to develop a flood prediction model for a Tshwane catchment area prone to flash floods. This causal model was considered due to a shortage of flood data. The developed Bayesian network was evaluated by environmental domain experts and implemented in Python through pyAgrum. Three what-if scenarios are used to verify the model and estimation of probabilities which were based on expert knowledge. The model was then used to predict a low and high rainfall scenario. It was able to predict no flooding events for a low rainfall scenario, and flooding events, especially around the rivers, for a high rainfall scenario. The model therefore behaves as expected.

Most developing countries suffer from inadequate health care facilities and a lack of medical practitioners as most of them emigrate to developed countries. The outbreak of the COVID-19 pandemic has left these countries more vulnerable to facing the worse outcome of the pandemic. This necessitates the need for a system that continuously monitors patient status and detects how their physiological variables will change over time. As a result, it will reduce the rate of mortality and mitigate the need for medical practitioners to monitor patients continuously. In this work, we show how an autoencoder and extreme gradient boosting can be merged to forecast physiological variables of a patient and detect anomalies and their level of divergence. An accurate detection of current and future anomalies will enable remedial action to be taken by medical practitioners at the right time and possibly save lives.

Robots are increasingly being used in the industry. Businesses that use robots can produce products and provide services at lower costs and with higher quality. Some industries, like automotive manufacturing, have become dependent on robots. The impact of robots on society and the greater economy is not clear. Robots threaten the jobs of low skilled workers and even middle-skilled workers. While researchers and governments are trying to understand the impact of robots on the economy, it is commonly accepted that robots will be used more widely across all industries. With this in mind, it is useful to consider the current research in robotics at South African research institutions. This paper is such a review. It is not exhaustive, but it provides a sense of the robotics research being done in South African research institutions. It appears that research institutions do not work on common themes, yet many research groups relate their work to Industry 4.0. The review suggests that each research group is working on topics of interest to them. The implication of this is that a wide variety of robotic themes are being researched in South Africa.