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UID:20261004T213840EDT-2720bZbRuU@132.216.98.100
DTSTAMP:20261005T013840Z
DESCRIPTION:PhD Oral Defense of John Quilty\, Bioresource Engineering.\n\nD
 ata-driven forecasting (i.e.\, regression\, machine learning\, artificial 
 intelligence\, etc.) has become a popular and very useful alternative to p
 hysically-based and conceptual forecasting approaches in the water resourc
 es domain since such methods solely rely on statistical relationships betw
 een explanatory variables and the target process\, require no explicit phy
 sical knowledge of the processes under study\, are rapid to develop\, have
  low-costs\, and are easy to implement in real-time.  However\, similar to
  physically-based and conceptual forecasting approaches\, the nonlinear\, 
 multiscale\, and uncertain nature of water resources provide challenges in
  the development of accurate and reliable data-driven forecasts.\n\nEveryo
 ne in the McGill community is welcome to attend a PhD oral defense. Please
  join us in celebrating the accomplishments of our PhD candidates.\n\n \n
DTSTART:20180823T170000Z
DTEND:20180823T170000Z
LOCATION:MS2-022\, Macdonald-Stewart Building\, CA\, QC\, St Anne de Bellev
 ue\, H9X 3V9\, 21111 Lakeshore Road
SUMMARY:PhD Oral Defense: An ensemble wavelet-based stochastic data-driven 
 framework for addressing nonlinearity\, multiscale change\, and uncertaint
 y in water resources forecasting
URL:https://www.mcgill.ca/macdonald/channels/event/phd-oral-defense-ensembl
 e-wavelet-based-stochastic-data-driven-framework-addressing-nonlinearity-2
 88353
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