An exploratory data analysis of the temperature fluctuations in a spreading fire

Abstract

A series of fire experiments were carried out in a wind tunnel at the United States Forest Service's Fire Science Laboratory in Missoula, Montana. The experiments involved tines cut out of pieces of cardboard. The pieces were laid out in comb‐like strips parallel to each other along a testbed. They were ignited at the windward end of the testbed. The progress of the fire was monitored by thermocouples, recording temperature, set out equidistantly up the middle of the testbed. Goals of the experiment included improved understanding of wildfire spread and the development of practical tools for wild land fire managers to employ. There was to be a search for regular pulsing in the series and any other interesting phenomena. This paper presents the results of a variety of exploratory data analyses meant to elicit information concerning the series before commencing probability modeling. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

Proposing the Use of Hazard Analysis for Machine Learning Data Sets

“…• Exploratory Data Analysis (Brillinger, 2011) • Boxing clever (Rob Ashmore, 2018) • Datasheets for datasets (Timnit Gebru, 2021) These machine learning data set verification results should present these statistical examinations of the data sets. The results should explain where the data set does not statistically fulfill the requirements associated with the operational design domain.…”

The poly‐log Weibull model applied to space‐time interpolation of temperature

“…Studies to detect those trends could be realized through the analysis of historical time series (Brillinger & Finney, 2014;Davidson, Stephenson, & Turasie, 2016;Guttorp & Xu, 2011) of atmospheric variables from a network of weather stations, using some adjusted predictive statistical model for the spatial interpolation (Krähenmann, Bissolli, Rapp, & Ahrens, 2011). However, every model is prone to have erroneous temperature values due to the vast distances involved and the sparsity of stations within the state.…”