University of Twente Student Theses
Predicting flooding due to extreme precipitation in an urban environment using machine learning algorithms
Kilsdonk, R.A.H. (2021) Predicting flooding due to extreme precipitation in an urban environment using machine learning algorithms.
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| Abstract: | Pluvial flooding in an urban environment can occur quite sudden. Therefore, flood early warning systems with a short run time are desired. A method to reduce computational load is surrogate modelling. Response surface surrogate models (Machine learning (ML) algorithms) are a second level abstraction from reality. These algorithms do not emulate any internal component of the original simulation, but try to and relations between the input variables and output. They are, once trained, extremely fast in predicting the output from a given input. Therefore, the use of such ML algorithms as a flood early warning system is studied. |
| Item Type: | Essay (Master) |
| Faculty: | ET: Engineering Technology |
| Subject: | 56 civil engineering |
| Programme: | Civil Engineering and Management MSc (60026) |
| Link to this item: | https://purl.utwente.nl/essays/86086 |
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