Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Cooke, William H., III

Committee Member

Bhushan, Shanti

Committee Member

Meng, Qingmin

Committee Member

Rodgers, John C., III

Date of Degree

12-14-2013

Document Type

Dissertation - Open Access

Major

Geosciences

Degree Name

Doctor of Philosophy

College

College of Arts and Sciences

Department

Department of Geosciences

Abstract

A novel approach for modeling anthropogenically-initiated wildfire ignition was developed that significantly advances the theoretical knowledge of human-wildfire interactions. Gravity interaction models that are commonly used for economic analyses associated with business competition were combined with fluid dynamics models that mimic human movement patterns to predict the probability of anthropogenically-initiated wildfire. Herein, a combined gravity interaction and fluid dynamics models is developed and validated for wildfire potential prediction against historic and current wildfire data. The study identified population centers and transportation corridors, in particular: proximity to railroads and roads; traffic volume; and density of the corridors as the most influential factors for wildfire ignition. The population centers are identified as global influencing factors, and are modeled as the gravity term. The transportation corridors are identified as local influencing factors, and are modeled using fluid flow analogy as diffusion and convection terms. An analytic convection diffusion model (CDM) model is derived and the model coefficients calibrated using historic wildfire data. The model is implemented in GIS, and applied for the prediction of wildfire potential prediction in southeastern Mississippi. The model shows a correlation of R2=0.87 against winter historic data, whereas the Gravity model with a fuel component shows only R2=0.75 correlations. The improved predictions using the proposed CDM model is due to its capability to predict both the global and the local measure of incendiary activity patterns within a single dynamic equation. The CDM model can be used as a standalone model that can predict the wildfire potential in a region. It can also be combined with the fuel layer and meteorological conditions to obtain spatio-temporal variation of wildfire risks, which would provide a decision support system for wildfire mitigation and land use planning and development. The CDM model will help fire managers better plan wildfire mitigation (fuel reduction) strategies and effectively stage equipment and personnel geographically in areas of drought that are coincident with high ignition probability. Land use and transportation managers will gain better understanding of the changes in wildfire risk pattern due to urban fringe development.

URI

https://hdl.handle.net/11668/19022

Download

Share

COinS