A Principal Component Analysis-Boosted Dynamic Gaussian Mixture Clustering Model for Ignition Factors of Brazil’s Rainforests

Abstract

Analysis of Brazil’s rainforest fires caused by various factors has become a hot topic nowadays,. Mining of rainforest fire data through learning unlabeled training samples can reveal inherent properties and patterns, providing a clue for fire prevention. Among commonly used mining approaches, clustering algorithms based on density estimation can relatively effectively capture the potential ignition features through probability calculation, while the Gaussian mixture model (GMM) based on Expectation-Maximum (EM) can effectively quantify fire distribution curves and decompose a fire object into different shape clustering problems based on the actual distribution characteristics of fires data, and thus cluster fires more accurately. However, when the discrimination of probability density is not apparent, the clustering effect is susceptible to both the number of parameters used in clustering and the shape of the clustering problem. Therefore, in the present paper, based on a new strategy of selecting and updating the parameters in the GMM, a new hybrid clustering model called Principal Component Analysis-boosted Dynamic Gaussian Mixture Clustering model (PCA-DGM) is developed. Specifically, Principal Component Analysis (PCA) reduces the dimension of fire samples and strengthens key ignition features. Furthermore, a new dynamic distance loss function is developed by dynamically selecting density parameters or distance parameters, whose computing value is utilized as one important parameter of the clustering shape decision of the GMM. Using the PCA-DGM, which can effectively solve clustering problems with various shapes, the causes of forest fires in Brazil are analyzed at both the temporal and geographical levels, and the experimental results demonstrate that the proposed PCA-DGM in this paper has a better clustering effect than the other traditional clustering algorithms.