A machine learning analysis of drought and rural population change on the North American Great Plains since the 1970s.

ABSTRACT: Machine learning techniques have to date not been widely used in population-environment research, but represent a promising tool for identifying relationships between environmental variables and population outcomes. They may be particularly useful for instances where the nature of the relationship is not obvious or not easily detected using other methods, or where the relationship potentially varies across spatial scales within a given study unit. Machine learning techniques may also help the researcher identify the relative strength of influence of specific variables within a larger set of interacting ones, and so provide a useful methodological approach for exploratory research. In this study, we use machine learning techniques in the form of random forest and regression tree analyses to

SUBMITTER: McLeman R 

PROVIDER: S-EPMC9085368 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature