Random forest of Classification and Regression Tree (CART) in the estimation of SWC based on meteorological inputs and hydrodynamics behind

Tsung Hsi Wu, Pei Yuan Chen, Chien Chih Chen, Meng Ju Chung, Zheng Kai Ye, Ming Hsu Li

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

This study employs the random forest algorithm of Classification and Regression Trees (CART) to estimate soil water content (SWC) at shallow depths in a grassland terrain site. Leveraging meteorological parameters, the random forest model demonstrates efficient and effective SWC estimation in a 12-folds time series cross-validation. The results reveal distinct strategies employed by the CART model for different SWC depths, addressing seasonal variations and SWC sensitivity to precipitation. Additionally, the study highlights limitations of CART in extrapolating beyond training data, leading to misfit in certain scenarios. These findings contribute valuable insights for improved SWC estimation and agricultural practices.

原文???core.languages.en_GB???
主出版物標題2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
發行者Institute of Electrical and Electronics Engineers Inc.
頁面2251-2255
頁數5
ISBN(電子)9798350300673
DOIs
出版狀態已出版 - 2023
事件2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 - Taipei, Taiwan
持續時間: 31 10月 20233 11月 2023

出版系列

名字2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
國家/地區Taiwan
城市Taipei
期間31/10/233/11/23

指紋

深入研究「Random forest of Classification and Regression Tree (CART) in the estimation of SWC based on meteorological inputs and hydrodynamics behind」主題。共同形成了獨特的指紋。

引用此