Testing machine learning algorithms on a binary classification phenological model

Aim Phenological models have become a vital tool for predicting future phenological responses to global climate change. Recently, machine learning (ML) has been used successfully to develop phenological models based on ground observations. However, fitting an observation series that has been observe...

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Bibliographic Details
Published in:	Global ecology and biogeography Vol. 32; no. 1; pp. 178 - 190
Main Authors:	Dai, Wujun, Jin, Huiying, Zhou, Lin, Liu, Tong, Zhang, Yuhong, Zhou, Zhiqiang, Fu, Yongshuo H., Jin, Guangze
Format:	Journal Article
Language:	English
Published:	Oxford Wiley Subscription Services, Inc 01.01.2023
Subjects:	Algorithms autumn binary classification biogeography Classification Climate change ecophysiological model ecophysiology Flowering ground observations Learning algorithms Machine learning Model accuracy model validation phenological model Phenology plant phenology prediction Predictions Root-mean-square errors sample size Spring Spring (season)
ISSN:	1466-822X, 1466-8238
Online Access:	Get full text
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