Experimental Exploratory of Temporal Sampling Forest in Forest Fire Regression and Classification


Yee, Jian Chew and Shih, Yin Ooi and Ying, Han Pang (2020) Experimental Exploratory of Temporal Sampling Forest in Forest Fire Regression and Classification. In: 2020 8th International Conference on Information and Communication Technology (ICoICT), 24.06.2020, Virtual Conference, Yogyakarta, Indonesia.

[img] Text
20.pdf - Published Version
Restricted to Repository staff only

Download (1MB)


Temporal Sampling Forest (TS-F) has been devoted to tackle the sequential data classification problem. It extends the robustness of random forest (RF) in handling the sequential data classification. However, it has not been used in the area of forest fire detection. Forest fire can be seen as a temporal phenomenon where it does not form in one day, but subsequently occurred due to the sequential changes of climates, human factors, and other affecting factors. Therefore, this paper is aim to tackle the data of forest fire from two perspectives, which are regression analysis and classification problem by using TS-F. The root mean square error (RMSE) obtained for regression analysis in the 1 st dataset is 61.35 while the classification accuracy of the 2 nd dataset is 84.18 %.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Temporal sampling forest, random forest, temporal data classification, temporal regression analysis, forest fire
Subjects: Q Science > QA Mathematics > QA150-272.5 Algebra
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 06 Oct 2021 02:08
Last Modified: 06 Oct 2021 02:08
URII: http://shdl.mmu.edu.my/id/eprint/8477


Downloads per month over past year

View ItemEdit (login required)