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Emulators for dynamic vegetation models - Supervised learning in large data sets

Olsson, Olof (2017) FMS820 20172
Mathematical Statistics
Abstract
The observed and expected changes in the environment due to human
actions implies risks that future food production will be insufficient. Pre-
dicting the impact these changes have on the agricultural system could
be beneficial by allowing for proactive mitigating efforts. The prediction
of these impacts often involve large computer programs that simulate the
behavior of the environment. By implementing a statistical representation
of the simulator, called an emulator, our hope is that these predictions
could be obtain at a lower computational cost. This master thesis has
implemented and evaluated a Gaussian process emulator for a vegeta-
tion model that is used for predicting the annual production of spring
wheat based on... (More)
The observed and expected changes in the environment due to human
actions implies risks that future food production will be insufficient. Pre-
dicting the impact these changes have on the agricultural system could
be beneficial by allowing for proactive mitigating efforts. The prediction
of these impacts often involve large computer programs that simulate the
behavior of the environment. By implementing a statistical representation
of the simulator, called an emulator, our hope is that these predictions
could be obtain at a lower computational cost. This master thesis has
implemented and evaluated a Gaussian process emulator for a vegeta-
tion model that is used for predicting the annual production of spring
wheat based on climate data at different locations around the world. The
problem of accurately modeling the simulator using a Gaussian process
approach was split into two parts. The first part was to model the average
yield at each location given average climate input at that location. The
second part was to model the yield at a specific year for a location given
the average yield at that location and the climate input anomalies dur-
ing that year. The results was far from satisfactory and a more complex
approach is probably needed before the emulator can be of any practical
use. Based on our findings, possible extensions that might improve results
are discussed. (Less)
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author
Olsson, Olof
supervisor
organization
course
FMS820 20172
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Gaussian process emulator, DGVM, emulation
language
English
id
8928740
date added to LUP
2017-11-22 09:06:51
date last changed
2017-11-22 09:06:51
@misc{8928740,
  abstract     = {The observed and expected changes in the environment due to human
actions implies risks that future food production will be insufficient. Pre-
dicting the impact these changes have on the agricultural system could
be beneficial by allowing for proactive mitigating efforts. The prediction
of these impacts often involve large computer programs that simulate the
behavior of the environment. By implementing a statistical representation
of the simulator, called an emulator, our hope is that these predictions
could be obtain at a lower computational cost. This master thesis has
implemented and evaluated a Gaussian process emulator for a vegeta-
tion model that is used for predicting the annual production of spring
wheat based on climate data at different locations around the world. The
problem of accurately modeling the simulator using a Gaussian process
approach was split into two parts. The first part was to model the average
yield at each location given average climate input at that location. The
second part was to model the yield at a specific year for a location given
the average yield at that location and the climate input anomalies dur-
ing that year. The results was far from satisfactory and a more complex
approach is probably needed before the emulator can be of any practical
use. Based on our findings, possible extensions that might improve results
are discussed.},
  author       = {Olsson, Olof},
  keyword      = {Gaussian process emulator,DGVM,emulation},
  language     = {eng},
  note         = {Student Paper},
  title        = {Emulators for dynamic vegetation models - Supervised learning in large data sets},
  year         = {2017},
}