… explicit dataset from five provinces of the Central Highlands, Vietnam. The results revealed
… The ensemble DFPA model classified 34.7, 44.1, and 21.2% of the Central Highlands into …

… Using machine learning methods and the best-available data from our 7,000-kilometer
mega-transect survey and open access data, we built the first species distribution model (SDM) to …

… machine learning to predict soil nutrient depletion for Rwanda. The “caretEnsemble” package
is a machine learning … The Congo watershed division, Birunga and Central plateau AEZs …

… ground truth and RS data in a non-parametric machine learning algorithm approach. Random
forest, a non-parametric machine learning algorithms were used to predict Teff yield at the …

… This study explored the use of remote sensing imagery and machine learning (ML) … Six
machine learning (ML) algorithms integrated with Landsat 8 imagery were evaluated for …

… models are similar, with the GPR machine learning model achieving the best outcomes. In
… Finally, by constructing a machine learning interpretable model to analyze the specific role …

… We train machine learning models to predict the likelihood of losses and explore the most
influential variables. On independent samples, the models identify substantial drought loss …

… feature selection approach along with tree machine learning approaches including random
forest (… Secondly, compared with the performances of the robust tree-based machine learning …

… to predict soil erosion susceptibility in the Central Highlands of Sri Lanka under different …
equation, statistical, machine learning and hybrid machine learning techniques to predict soil …

… This study investigates the applicability of various geospatial data, machine learning techniques
(MLTs) and spatial statistical tools to demarcate the forest fire susceptible regions of the …