Data Mining Approach to Predicting Soil Moisture Based on Meteorological Factors and Flow Rates-Reference-Cited by-同舟云学术

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Data Mining Approach to Predicting Soil Moisture Based on Meteorological Factors and Flow Rates

Published:2024-02-28 Issue:1 Volume:42 Page:1-14
ISSN:1226-8763
Container-title:Horticultural Science and Technology
language:en
Short-container-title:HST

Author:

Choi Su Hoon¹,Lee Sang-Hyun²,Yang Ung³,Kim Min Soo⁴

Affiliation:

1. Department of Mathematics and Statistics, Chonnam National University, Gwangju 61186, Korea

2. Interdisciplinary Program in IT-Bio Convergence System, Chonnam National University, Gwangju 61186, Korea

3. Asian Pear Research Institute, Chonnam National University, Gwangju 61186, Korea

4. Department of Statistics, Chonnam National University, Gwangju 61186, Korea

Publisher

Korean Society of Horticultural Science

Link

https://www.hst-j.org/articles/pdf/doi/10.7235/HORT.20240001.pdf

Reference50 articles.

1. Intraseasonal dynamics of soil moisture variability within a small agricultural maize cropped field

2. Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling

3. Climate, Leaf Litter Chemistry and Leaf Litter Decomposition in Terrestrial Ecosystems: A Triangular Relationship

4. Bagging predictors

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