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Lookup NU author(s): Chukwuma Okolie, Professor Jon MillsORCiD, Tom Komar, Dr Shidong WangORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.There has been a rapid evolution of tree-based ensemble algorithms which have outperformed deep learning in several studies, thus emerging as a competitive solution for many applications. In this study, ten tree-based ensemble algorithms (random forest, bagging meta-estimator, adaptive boosting (AdaBoost), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), light gradient boosting (LightGBM), histogram-based GBM, categorical boosting (CatBoost), natural gradient boosting (NGBoost), and the regularised greedy forest (RGF)) were comparatively evaluated for the enhancement of Copernicus digital elevation model (DEM) in an agricultural landscape. The enhancement methodology combines elevation and terrain parameters alignment, with feature-level fusion into a DEM enhancement workflow. The training dataset is comprised of eight DEM-derived predictor variables, and the target variable (elevation error). In terms of root mean square error (RMSE) reduction, the best enhancements were achieved by GBM, random forest and the regularised greedy forest at the first, second and third implementation sites respectively. The computational time for training LightGBM was nearly five-hundred times faster than NGBoost, and the speed of LightGBM was closely matched by the histogram-based GBM. Our results provide a knowledge base for other researchers to focus their optimisation strategies on the most promising algorithms.
Author(s): Okolie C, Adeleke A, Mills J, Smit J, Maduako I, Bagheri H, Komar T, Wang S
Publication type: Article
Publication status: Published
Journal: International Journal of Image and Data Fusion
Year: 2024
Pages: epub ahead of print
Online publication date: 12/04/2024
Acceptance date: 19/02/2024
Date deposited: 20/05/2024
ISSN (print): 1947-9832
ISSN (electronic): 1947-9824
Publisher: Taylor and Francis Ltd.
URL: https://doi.org/10.1080/19479832.2024.2329563
DOI: 10.1080/19479832.2024.2329563
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