AI4ER-CEDSG group meeting: Machine Learning Approaches to Assessing Future Flood Risk

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• Robert Rouse
• Tuesday 03 December 2019, 11:00-12:00
• Bullard Lab, Seminar Room.

If you have a question about this talk, please contact Rachel Furner.

In the face of impending climate change, the need to understand the impact of extreme weather events is critical; whilst large climate models provide broad detail, such as large scale patterns in surface temperature, they are unsuitable for understanding regional and localised impact and for predicting the potential impact of extreme events. An investigation into machine learning approaches will be undertaken to determine whether or not such approaches are capable of generating credible predictions for extreme scenarios, specifically precipitative events and subsequent catchment run-off/peak flow. There are three aspects to this project: creating more usable output of precipitation data from GCMs, through bias correction and downscaling; an investigation into whether machine learning approaches can deliver more credible results when compared with industry hydrologists (partner: Mott MacDonald); and the analysis of machine learning performance around extreme hydrological events.

This talk is part of the AI4ER Seminar Series series.

This talk is included in these lists:

• AI4ER Seminar Series
• Bullard Lab, Seminar Room
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge talks
• Chris Davis' list
• Interested Talks
• Trust & Technology Initiative - interesting events
• bld31
• ndk22's list
• ob366-ai4er
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