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

doi:10.1109/APSIPAASC58517.2023.10317380

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2251-2255
Number of pages	5
ISBN (Electronic)	9798350300673
DOIs	https://doi.org/10.1109/APSIPAASC58517.2023.10317380
State	Published - 2023
Event	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 - Taipei, Taiwan Duration: 31 Oct 2023 → 3 Nov 2023

Publication series

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

Conference

Conference	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
Country/Territory	Taiwan
City	Taipei
Period	31/10/23 → 3/11/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/APSIPAASC58517.2023.10317380

人工智慧架構的地球環境災害防減抗策略TWAI(湍)(1/2)
Chen, C.-C. (PI)
1/11/21 → 31/10/22
Project: Research

Cite this

Wu, T. H., Chen, P. Y., Chen, C. C., Chung, M. J., Ye, Z. K., & Li, M. H. (2023). Random forest of Classification and Regression Tree (CART) in the estimation of SWC based on meteorological inputs and hydrodynamics behind. In 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 (pp. 2251-2255). (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPAASC58517.2023.10317380

Wu, Tsung Hsi ; Chen, Pei Yuan ; Chen, Chien Chih et al. / Random forest of Classification and Regression Tree (CART) in the estimation of SWC based on meteorological inputs and hydrodynamics behind. 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 2251-2255 (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023).

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

abstract = "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.",

author = "Wu, {Tsung Hsi} and Chen, {Pei Yuan} and Chen, {Chien Chih} and Chung, {Meng Ju} and Ye, {Zheng Kai} and Li, {Ming Hsu}",

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series = "2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023",

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Wu, TH, Chen, PY , Chen, CC, Chung, MJ, Ye, ZK & Li, MH 2023, Random forest of Classification and Regression Tree (CART) in the estimation of SWC based on meteorological inputs and hydrodynamics behind. in 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023, Institute of Electrical and Electronics Engineers Inc., pp. 2251-2255, 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023, Taipei, Taiwan, 31/10/23. https://doi.org/10.1109/APSIPAASC58517.2023.10317380

Random forest of Classification and Regression Tree (CART) in the estimation of SWC based on meteorological inputs and hydrodynamics behind. / Wu, Tsung Hsi; Chen, Pei Yuan ; Chen, Chien Chih et al.
2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 2251-2255 (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Random forest of Classification and Regression Tree (CART) in the estimation of SWC based on meteorological inputs and hydrodynamics behind

AU - Wu, Tsung Hsi

AU - Chen, Pei Yuan

AU - Chen, Chien Chih

AU - Chung, Meng Ju

AU - Ye, Zheng Kai

AU - Li, Ming Hsu

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

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M3 - 會議論文篇章

AN - SCOPUS:85180013131

T3 - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

SP - 2251

EP - 2255

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

Y2 - 31 October 2023 through 3 November 2023

ER -

Wu TH, Chen PY , Chen CC, Chung MJ, Ye ZK, Li MH. Random forest of Classification and Regression Tree (CART) in the estimation of SWC based on meteorological inputs and hydrodynamics behind. In 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 2251-2255. (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023). doi: 10.1109/APSIPAASC58517.2023.10317380

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

Abstract

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人工智慧架構的地球環境災害防減抗策略TWAI(湍)(1/2)

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