A Principal Component Analysis-Boosted Dynamic Gaussian Mixture Clustering Model for Ignition Factors of Brazil’s Rainforests
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 approa...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9584896/ |
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| author | Maofa Wang Guangda Gao Hongliang Huang Ali Asghar Heidari Qian Zhang Huiling Chen Weiyu Tang |
| author_facet | Maofa Wang Guangda Gao Hongliang Huang Ali Asghar Heidari Qian Zhang Huiling Chen Weiyu Tang |
| author_sort | Maofa Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T23:07:18Z |
| format | Article |
| id | doaj.art-f01f2152fceb4b5ca30c1e69c5e1844e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T23:07:18Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f01f2152fceb4b5ca30c1e69c5e1844e2022-12-21T19:23:50ZengIEEEIEEE Access2169-35362021-01-01914574814576210.1109/ACCESS.2021.31221129584896A Principal Component Analysis-Boosted Dynamic Gaussian Mixture Clustering Model for Ignition Factors of Brazil’s RainforestsMaofa Wang0https://orcid.org/0000-0001-6517-4042Guangda Gao1Hongliang Huang2https://orcid.org/0000-0002-5819-8633Ali Asghar Heidari3https://orcid.org/0000-0001-6938-9948Qian Zhang4Huiling Chen5https://orcid.org/0000-0002-7714-9693Weiyu Tang6https://orcid.org/0000-0003-0145-7064Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, ChinaSchool of Information Engineering, China University of Geosciences (Beijing), Beijing, ChinaSchool of Public Foundation and Applied Statistics, Zhuhai College of Jilin University, Zhuhai, ChinaSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranSchool of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, ChinaSchool of Computer, Zhuhai College of Jilin University, Zhuhai, ChinaAnalysis 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.https://ieeexplore.ieee.org/document/9584896/Forest fireignition factorPCA-DGMprincipal component analysisGaussian mixture |
| spellingShingle | Maofa Wang Guangda Gao Hongliang Huang Ali Asghar Heidari Qian Zhang Huiling Chen Weiyu Tang A Principal Component Analysis-Boosted Dynamic Gaussian Mixture Clustering Model for Ignition Factors of Brazil’s Rainforests IEEE Access Forest fire ignition factor PCA-DGM principal component analysis Gaussian mixture |
| title | A Principal Component Analysis-Boosted Dynamic Gaussian Mixture Clustering Model for Ignition Factors of Brazil’s Rainforests |
| title_full | A Principal Component Analysis-Boosted Dynamic Gaussian Mixture Clustering Model for Ignition Factors of Brazil’s Rainforests |
| title_fullStr | A Principal Component Analysis-Boosted Dynamic Gaussian Mixture Clustering Model for Ignition Factors of Brazil’s Rainforests |
| title_full_unstemmed | A Principal Component Analysis-Boosted Dynamic Gaussian Mixture Clustering Model for Ignition Factors of Brazil’s Rainforests |
| title_short | A Principal Component Analysis-Boosted Dynamic Gaussian Mixture Clustering Model for Ignition Factors of Brazil’s Rainforests |
| title_sort | principal component analysis boosted dynamic gaussian mixture clustering model for ignition factors of brazil x2019 s rainforests |
| topic | Forest fire ignition factor PCA-DGM principal component analysis Gaussian mixture |
| url | https://ieeexplore.ieee.org/document/9584896/ |
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