University of Twente Student Theses


A Log Gaussian Cox process for predicting chimney fires at Fire Department Twente

School, M.L. (2018) A Log Gaussian Cox process for predicting chimney fires at Fire Department Twente.

[img] PDF
Abstract:The Twente fire department is developing an interest in the use of Business Intelligence for their operations with the data they have available from all emergency calls made in the past twelve years (2004 until 2016). Last year, an applied mathematics student started a collaboration with the fire department by modelling fire-related emergency calls in the region of Twente. He investigated whether an inhomogeneous Poisson process could describe these emergency calls. The answer was unfortunately not satisfying, because too many incidents of different types were considered and the relatively simple inhomogeneous Poisson process did not cover the data well. In this research we focus on one of the largest types of chimney fires, and expand the model to also encounter spatially dependent noise. The inhomogeneous Poisson process is therefore extended with a random field which results in a Log Gaussian Cox process. The research includes finding the spatial and temporal influence covariates of chimney fires and modelling the Log Gaussian Cox process in two steps, first modelling the inhomogeneous Poisson process and then adding the random field corresponding to the spatially dependent noise. The number of residents in an area and the mean daily temperature have the highest in uence on the occurrence of chimney fires, with an extension for the month October where people start using their chimneys. The resulting Log Gaussian Cox process is dependent on the above three variables based on residents, temperature and the month October and together with the spatially dependent noise it delivers satisfying results for predicting chimney fires in Twente. Finally, a dashboard is constructed to put the prediction into practice and to make Business Intelligence visible in the organisation.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:31 mathematics
Programme:Applied Mathematics MSc (60348)
Link to this item:
Export this item as:BibTeX
HTML Citation
Reference Manager


Repository Staff Only: item control page