A performance evaluation of random forest, artificial neural network, and support vector machine learning algorithms to predict spatio-temporal land use-land cover dynamics: a case from lusaka and colombo

A performance evaluation of random forest, artificial neural network, and support vector machine learning algorithms to predict spatio-temporal land use-land cover dynamics: a case from lusaka and colombo

Reliable information plays a pivotal role in sustainable urban planning. With advancements in computer technology, geoinformatics tools enable accurate identification of land use and land cover (LULC) in both spatial and temporal dimensions. Given the need for precise information to enhance decision...

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Bibliographic Details
Main Authors: Bwalya Mutale, Neel Chaminda Withanage, Prabuddh Kumar Mishra, Jingwei Shen, Kamal Abdelrahman, Mohammed S. Fnais
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-09-01
Series:Frontiers in Environmental Science
Subjects:
artificial neural network
Colombo
land use/land cover
Lusaka
random forest
support vector machine
Online Access:https://www.frontiersin.org/articles/10.3389/fenvs.2024.1431645/full

Internet

https://www.frontiersin.org/articles/10.3389/fenvs.2024.1431645/full

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