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.
","abstract_html":"<p>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. </p>","abstract_has_math":false,"creators":["Walters, Matthew"],"institution":null,"degree_name":"Master of Science in Industrial Engineering (MSIE)","degree_level":"Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Liu, Xiao","Cothren, Jackson D."],"advisors":["Rainwater, Chase E."],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05-01T07:00:00Z","date_published":"2022-05-01T07:00:00Z","updated_at":"2026-07-24T00:58:44Z","subjects":["data science","machine learning","natural disaster","neural network","wildfire","Industrial Engineering","Industrial Technology","Operational Research","Risk Analysis"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uark.edu/etd/4521","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Liu, Xiao","Cothren, Jackson D."]},{"key":"dc:contributor.advisor","label":"Advisor","values":["Rainwater, Chase E."]},{"key":"dc:creator","label":"Author","values":["Walters, Matthew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-02-06T08:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Industrial Engineering (MSIE)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["data science","machine learning","natural disaster","neural network","wildfire","Industrial Engineering","Industrial Technology","Operational Research","Risk Analysis"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uark.edu/etd/4521"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Predicting the Likelihood and Scale of Wildfires in California using Meteorological and Vegetation Data"]}]}],"canonical_facts":{"dc:contributor":["Liu, Xiao","Cothren, Jackson D."],"dc:contributor.advisor":["Rainwater, Chase E."],"dc:creator":["Walters, Matthew"],"dc:date":["2022"],"dc:date.available":["2024-02-06T08:00:00Z"],"dc:description.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.
"],"dc:identifier":["https://scholarworks.uark.edu/etd/4521"],"dc:subject":["data science","machine learning","natural disaster","neural network","wildfire","Industrial Engineering","Industrial Technology","Operational Research","Risk Analysis"],"dc:title":["Predicting the Likelihood and Scale of Wildfires in California using Meteorological and Vegetation Data"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Industrial Engineering (MSIE)"]},"updated_at":"2026-07-24T00:58:44Z"}