Abstract:
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 featurelevel
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.
