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Assessment of Global Forest Coverage through Machine Learning Algorithms.
- Source :
- EAI Endorsed Transactions on Scalable Information Systems; 2024, Vol. 11 Issue 4, p1-8, 8p
- Publication Year :
- 2024
-
Abstract
- This exploration of paper presents an investigation of the Forest Region Inclusion Dataset that gives data on the backwoods inclusion of different nations overall from 1990 to 2020. The dataset contains country-wise information on population, population density, population development rate, total population rate, and forest region inclusion. We examined this dataset to decide the patterns in woodland region inclusion across various nations and mainlands, as well as the connection among populace and backwoods region inclusion. Our discoveries show that while certain nations have essentially expanded their forest region inclusion, others have encountered a decline. Besides, we found that population density and development rate are adversely related with forest area coverage. Authors have implemented four machine learning algorithms that are Linear Regression, Decision Tree, Random Forest and Support Vector Machine on the dataset. [ABSTRACT FROM AUTHOR]
- Subjects :
- MACHINE learning
DECISION trees
SUPPORT vector machines
RANDOM forest algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 20329407
- Volume :
- 11
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- EAI Endorsed Transactions on Scalable Information Systems
- Publication Type :
- Academic Journal
- Accession number :
- 176424315
- Full Text :
- https://doi.org/10.4108/eetsis.5122