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UID:178@siliconbrighton.com
DTSTART;TZID=Europe/London:20210617T150000
DTEND;TZID=Europe/London:20210617T160000
DTSTAMP:20221215T105219Z
URL:https://siliconbrighton.com/events/sussex-data-science-june-meetup/
SUMMARY:Sussex Data Science - June Meetup
DESCRIPTION:I am pleased to invite you to our fifteenth Sussex Data Science
  Meetup. As is the way of things at the moment\, this meeting has moved on
 line and will be conducted remotely via zoom. We are fortunate enough to h
 ave Edward Efui Salakpi\, a doctoral researcher from the University of Sus
 sex who will be talking about:\n\nForecasting the Grassland Vegetation Con
 dition with Bayesian Auto-Regression Distributed Lags Models\n\nAmongst al
 l the natural disasters reported yearly\, drought events are the most prev
 alent. Their devastating effects are mostly felt in the grass and shrublan
 ds of arid and semi-arid lands (ASALs) within the East African region. The
  people living in these ASAL regions are mostly pastoralists who depend so
 lely on the grass and shrubs as fodder for animals.\n\nUnfortunately\, the
  recurring drought conditions in these areas adversely affect livestock fa
 rming resulting in limited cash flow and loss of livelihoods. Addressing t
 his challenge requires Early Warning Systems (EWS) that do not only monito
 r but accurately forecast drought events.\nCurrent research on drought for
 ecast with univariate indicators has worked well for short-range (4-6 week
 s) forecasts. However\, drought events are known to be influenced by other
  environmental and biophysical factors that occur before and during these 
 drought events. The aim of our current research to build on the existing d
 rought models and extend the forecast range by including exogenous factors
  like precipitation\, soil moisture and land surface temperature derived f
 rom Earth Observation datasets.\n\nThe talk will be based on work done by 
 researchers working on drought forecast under the University of Sussex Ast
 roCast project. It will cover the use of an Auto-regressive distributed la
 g (ARDL) model implemented within a Bayesian Framework for modelling and f
 orecasting vegetation based drought condition.\n\nWe will close the meetin
 g with open discussions and announcements from the membership.
CATEGORIES:Sussex Data Science
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