A machine learning approach to fill gaps in dendrometer data.

ABSTRACT:

Key message

The machine learning algorithm extreme gradient boosting can be employed to address the issue of long data gaps in individual trees, without the need for additional tree-growth data or climatic variables.

Abstract

The susceptibility of dendrometer devices to technical failures often makes time-series analyses challenging. Resulting data gaps decrease sample size and complicate time-series comparison and integration. Existing methods either focus on bridging smaller gaps, are dependent on data from other trees or rely on climate parameters. In this study, we test eight machine learning (ML) algorithms to fill gaps in dendrometer data of individual trees in urban and non-urban environments. Among these algorithms, extreme gradient boosting (XGB) demonstrates the be

SUBMITTER: Kuhl E 

PROVIDER: S-EPMC11584462 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature