Methods for the atomic absorption determination of Pt, Pd, Rh, and Au in the presence of one or all of the other noble metals were developed. The reproducibility of the analytical results was satisfactory. The effect of various interfering solutions on the analytical results were examined

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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.

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