Improving the accuracy of air relative humidity prediction using hybrid machine learning based on empirical mode decomposition: a comparative study.

This paper proposes a hybrid air relative humidity prediction based on preprocessing signal decomposition. New modelling strategy was introduced based on the use of the empirical mode decomposition, variational mode decomposition, and the empirical wavelet transform, combined with standalone machine learning to increase their numerical performances. First, standalone models, i.

Allopatric diversification and evolutionary melting pot in a North African Palearctic relict: The biogeographic history of Salamandra algira.

North Africa is a climatically and topographically complex region with unique biotic assemblages resulting from the combination of multiple biogeographic realms. Here, we assess the role of climate in promoting intra-specific diversification in a Palearctic relict, the North African fire salamander, Salamandra algira, using a combination of phylogenetic and population genetic analyses, paleoclimatic modelling and niche overlap tests. We used mitochondrial DNA (Cyt-b), 9838 ddRADseq loci, and 14 microsatellite loci to characterize patterns of genetic diversity and population structure.