Gépi tanulás, statisztikus adatfeldolgozás, jelfeldolgozáson alapuló alkalmazások
(pl. beszéd, kép, videó)
Humanitárius kockázatú területek (migrációs áramlás, aszály) felügyelete
Mesterséges intelligencia és döntéstámogatás
Drought is among the most destructive natural disasters with a drastic impact on the
economy, ecosystem, biodiversity, and agriculture. Climate change and global warming
are greatly contributing in increasing the severity, irregularity, and frequency of
droughts throughout the world. Accurate prediction of the droughts empowers the policies
made for mitigation, resilience and adaptation. Machine learning models have shown
outstanding performance with promising results in predicting the drought. This paper
aims at presenting a systematics state of the art review on the major machine learning
advancements using a novel taxonomy. Machine learning models have been classified
in five major groups based on the method used, i.e., neural networks-based, decision
tree-based, support vector-based, hybrids and ensembles. The survey shows that emerging
ensemble and hybrid models deliver higher performance where single methods are often
outperformed.
