| Issue |
MATEC Web Conf.
Volume 370, 2022
2022 RAPDASA-RobMech-PRASA-CoSAAMI Conference - Digital Technology in Product Development - The 23rd Annual International RAPDASA Conference joined by RobMech, PRASA and CoSAAMI
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|---|---|---|
| Article Number | 07001 | |
| Number of page(s) | 15 | |
| Section | Pattern Recognition | |
| DOI | https://doi.org/10.1051/matecconf/202237007001 | |
| Published online | 01 December 2022 | |
Towards better flood risk management using a Bayesian network approach
1 Operational Intelligence, Next Generation Enterprises and Institutions, CSIR, Pretoria, South Africa
2 Center for Robotics and Future Production, Manufacturing, CSIR, Pretoria, South Africa
3 Command and Control and Integrative Systems, Defence and Security, CSIR, Pretoria, South Africa
* Corresponding author: gwessels@csir.co.za
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.
© The Authors, published by EDP Sciences, 2022
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