A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study

Guilherme Weigert Cassales, Serajis Salekin, Nick Jin Sean Lim, Dean F. Meason, Albert Bifet, Bernhard Pfahringer, Eibe Frank

Abstract: Highlights•Three state-of-the-art Deep learning (DL) algorithms, along with a baseline DL model, were compared for forecasting tree stem radius using point dendrometer data.•Variations in temporal resolution for data acquisition significantly influenced the forecasting performance of DL models.•A notable trade-off in forecasting accuracy was observed when using reduced temporal resolutions.