Assessment of explainable tree-based ensemble algorithms for the enhancement of Copernicus digital elevation model in agricultural lands

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 (Ada...

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Bibliographic Details
Published in:International journal of image and data fusion 2024-10, Vol.15 (4), p.430-460
Main Authors: Okolie, Chukwuma, Adeleke, Adedayo, Mills, Jon, Smit, Julian, Maduako, Ikechukwu, Bagheri, Hossein, Komar, Tom, Wang, Shidong
Format: Article
Language:English
Subjects:
Algorithms
bagging
boosting
Computing time
Copernicus
Deep learning
Digital Elevation Models
Error reduction
Evolutionary algorithms
explainability
global digital elevation model
gradient boosting
Histograms
Knowledge bases (artificial intelligence)
LiDAR
Machine learning
partial dependence
Root-mean-square errors
tree-based ensembles
Workflow
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Summary: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.
ISSN:1947-9832
1947-9824
DOI:10.1080/19479832.2024.2329563