dc.contributor.author |
Wessels, Gert J
|
|
dc.contributor.author |
Botha, Natasha
|
|
dc.contributor.author |
Koen, Hildegarde S
|
|
dc.contributor.author |
Van Eden, Beatrice
|
|
dc.date.accessioned |
2023-01-17T06:56:17Z |
|
dc.date.available |
2023-01-17T06:56:17Z |
|
dc.date.issued |
2022-11 |
|
dc.identifier.citation |
Wessels, G.J., Botha, N., Koen, H.S. & Van Eden, B. 2022. Towards better flood risk management using a Bayesian network approach. http://hdl.handle.net/10204/12579 . |
en_ZA |
dc.identifier.uri |
https://doi.org/10.1051/matecconf/202237007001
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/12579
|
|
dc.description.abstract |
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. |
en_US |
dc.format |
Fulltext |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://www.matec-conferences.org/articles/matecconf/abs/2022/17/contents/contents.html |
en_US |
dc.source |
23rd Annual International RAPDASA Conference joined by RobMech, PRASA and CoSAAMI, Somerset-West, Cape Town, 9-11 November 2022 |
en_US |
dc.subject |
Flood prediction model |
en_US |
dc.subject |
Tshwane catchment area |
en_US |
dc.subject |
Flooding |
en_US |
dc.subject |
Industrial AI |
en_US |
dc.title |
Towards better flood risk management using a Bayesian network approach |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.description.pages |
15 |
en_US |
dc.description.note |
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/). |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.cluster |
Manufacturing |
en_US |
dc.description.cluster |
Defence and Security |
en_US |
dc.description.impactarea |
Geospatial Modelling & Analysis |
en_US |
dc.description.impactarea |
Command Control and Integrated Systems |
en_US |
dc.description.impactarea |
Industrial AI |
en_US |
dc.identifier.apacitation |
Wessels, G. J., Botha, N., Koen, H. S., & Van Eden, B. (2022). Towards better flood risk management using a Bayesian network approach. http://hdl.handle.net/10204/12579 |
en_ZA |
dc.identifier.chicagocitation |
Wessels, Gert J, Natasha Botha, Hildegarde S Koen, and Beatrice Van Eden. "Towards better flood risk management using a Bayesian network approach." <i>23rd Annual International RAPDASA Conference joined by RobMech, PRASA and CoSAAMI, Somerset-West, Cape Town, 9-11 November 2022</i> (2022): http://hdl.handle.net/10204/12579 |
en_ZA |
dc.identifier.vancouvercitation |
Wessels GJ, Botha N, Koen HS, Van Eden B, Towards better flood risk management using a Bayesian network approach; 2022. http://hdl.handle.net/10204/12579 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Wessels, Gert J
AU - Botha, Natasha
AU - Koen, Hildegarde S
AU - Van Eden, Beatrice
AB - 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.
DA - 2022-11
DB - ResearchSpace
DP - CSIR
J1 - 23rd Annual International RAPDASA Conference joined by RobMech, PRASA and CoSAAMI, Somerset-West, Cape Town, 9-11 November 2022
KW - Flood prediction model
KW - Tshwane catchment area
KW - Flooding
KW - Industrial AI
LK - https://researchspace.csir.co.za
PY - 2022
T1 - Towards better flood risk management using a Bayesian network approach
TI - Towards better flood risk management using a Bayesian network approach
UR - http://hdl.handle.net/10204/12579
ER -
|
en_ZA |
dc.identifier.worklist |
26371 |
en_US |