Date of Graduation
5-2022
Document Type
Thesis
Degree Name
Master of Science in Industrial Engineering (MSIE)
Degree Level
Graduate
Department
Industrial Engineering
Advisor/Mentor
Chase Rainwater
Committee Member
Xiao Liu
Second Committee Member
Jack Cothren
Keywords
data science, machine learning, natural disaster, neural network, wildfire
Abstract
Wildfires have devastating ecological, environmental, economical, and public health impacts through the deterioration of water and air quality, CO2 emissions, property damage, and lung illnesses. The early detection and prevention of wildfires allow for the minimization of these risks. The use of Artificial Intelligence (AI) in wildfire detection and prediction has been highly researched as a tool to assist firefighters in stopping wildfires in its early stages. The three common wildfire prediction categories include image and video detection, behavior prediction, and susceptibility prediction. Data such as climate, weather, vegetation, satellite images, and historical wildfire data is most commonly used. Many approaches such as Support Vector Machines (SVM), Basic Neural Networks (BNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Convolutional Neural Networks (CNN) have been highly used in wildfire prediction. The goal of this research is to discover the best combination of data and prediction methodology that most accurately predicts a locations likelihood and scale of a wildfire occurring in any given month to assist in the resource allocation and planning of fighting wildfires.
Citation
Walters, M. (2022). Predicting the Likelihood and Scale of Wildfires in California using Meteorological and Vegetation Data. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/4521
Included in
Industrial Engineering Commons, Industrial Technology Commons, Operational Research Commons, Risk Analysis Commons