Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning model.

ABSTRACT: Various modelling techniques are available to understand the temporal and spatial variations of the phenology of species. Scientists often rely on correlative models, which establish a statistical relationship between a response variable (such as species abundance or presence-absence) and a set of predominantly abiotic covariates. The choice of the modeling approach, i.e., the algorithm, is itself a significant source of variability, as different algorithms applied to the same dataset can yield disparate outcomes. This inter-model variability has led to the adoption of ensemble modelling techniques, among which stacked generalisation, which has recently demonstrated its capacity to produce robust results. Stacked ensemble modelling incorporates predictions from multiple base learners or mo

SUBMITTER: Da Re D 

PROVIDER: S-EPMC11782657 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature