Year: 2016
Type: article
Abstract: Robert C. Green II, AdvisorForest fires are a dangerous and devastating phenomenon.Being able to accurately predict the burned area of a forest fire could potentially limit biological damage as well as better prepare for ensuing economical and ecological damage.A data set from the Montesinho Natural Park in Portugal provides a difficult regression task regarding the prediction of forest fire burn area due to the limited amount of data entries and the imbalanced nature of the data set.This thesis focuses on improving these results through the use of a Backpropagation trained Artificial Neural Network which is systematically evaluated over a variety of configurations, activation functions, and input methodologies, resulting in approximately 30% improvements to regression error rates.A Particle Swarm Optimization (PSO) trained Artificial Neural Network is also evaluated in a variety of configurations providing approximately 75% improvement of regression error rates.Going further, the data is also clustered on both inputs and outputs using k-Means and Spectral algorithms in order to pursue the task of classification where near perfect classification is achieved when clustering on inputs is considered and an accuracy of roughly 60% is achieved when clustering on output values.
Author: Jeremy J Storer
Institution: Bowling Green State University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: green
OpenAlex ID: W2345609030