每年專案
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??? |
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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 2023 → 3 11月 2023 |
出版系列
名字 | 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 |
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???event.eventtypes.event.conference??? | 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 |
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國家/地區 | Taiwan |
城市 | Taipei |
期間 | 31/10/23 → 3/11/23 |
指紋
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