Publication Date

Spring 2021

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)


Computer Science

First Advisor

Katerina Potika

Second Advisor

Navrati Saxena

Third Advisor

Ali Tohidi


wildfires, multi-criteria decision making, fuzzy set theory


Wildfires are uncontrolled fires that may lead to the destruction of biodiversity, soil fertility, and human resources. There is a need for timely detection and prediction of wildfires to minimize their disastrous effects. In this research, we propose a wildfire prediction model that relies on multi-criteria decision making (MCDM) to explicitly evaluates multiple conflicting criteria in decision making and weave the wildfire risks into the city’s resiliency plan. We incorporate fuzzy set theory to handle imprecision and uncertainties. In the process, we create a new data set that includes California cities’ weather, vegetation, topography, and population density records. The model ranks the cities of California based on their risk of wildfires.