Spatial Extreme Value Analysis using Point Processes
Typ
Examensarbete för masterexamen
Program
Data science and AI (MPDSC), MSc
Publicerad
2021
Författare
Engström, Anne
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Extreme value theory is a field in statistics dealing with the occurrence of rare events,
also known as extreme events. It is commonly found in financial applications, but
also when modeling rare environmental events. This Master’s thesis proposes a new
methodology for modeling spatio-temporal extreme values, by generalising a method
for extremes of a time series to both space and time. The method is applied to data
of extreme wildfire activity in the United States, with the goal to predict the risk of
wildfires exceeding some large threshold. The model created for predicting extreme
wildfires in the spatio-temporal dimension is created from a self-exciting model,
with previous events triggering new events. In the spatio-temporal dimension, the
self-excitation is dependant on both the time and the distance to previous events,
making point processes suitable for modeling these.
The resulting model manages to capture both the influence of previous wildfires,
as well as the impact of a regions vegetation when assessing the risk of large wildfires
in the area. While the model cannot predict the exact locations of all wildfires
at a selected time point, it accurately shows the regions with an elevated risk of
extensive wildfire activity. It also captures the trend of increasing wildfire activity
in the studied area.
Beskrivning
Ämne/nyckelord
spatio-temporal extreme value analysis, point processes, peaks over threshold, risk