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SUMMARY:Machine Learning Approaches to Assessing Future Flood &amp\; Storm
  Risk - Robert Rouse | University of Cambridge
DTSTART:20210622T100000Z
DTEND:20210622T113000Z
UID:TALK159073@talks.cam.ac.uk
CONTACT:87364
DESCRIPTION:Seasonal precipitation extremes may potentially be exacerbated
  by anthropogenic climate change\, increasing the risks of drought and flo
 oding and their associated impact.  Given data paucity in certain geograph
 ies\, such as those perhaps more susceptible to the effects of climate cha
 nge\, the development of empirical models that can deliver high performanc
 e with minimum calibration and extant\, obtainable inputs could be highly 
 beneficial.\n\nIn this talk\, I will begin with a simple hydrological mode
 l alongside a\, likewise\, simple artificial neural network and how these 
 serve as the rationale for the development of machine learning approaches 
 to the problem\; this includes: identification of a suitable feature set a
 nd proxy variables\, handling extreme values\, a comparison of machine lea
 rning methodologies and their suitability to the problem\, the development
  of a generalisable machine learning model\, and that of a novel architect
 ure(s).  I may also discuss my approach to storm prediction\, how my lack 
 of progress has failed me thus far\, and how I intend to resolve this and 
 fit it into this project as a whole.
LOCATION:https://zoom.us/j/6708259482?pwd=Qk03U3hxZWNJZUZpT2pVZnFtU2RRUT09
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