Estimating Soil Temperature at Various Depths in Bangladesh: A Comparative Analysis of Advanced Machine Learning Tree-Based Models

Lipon Chandra Das*, Tasnim Anisha, Anisul Islam

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Soil temperature at various depths is vital for hydrology, ecology, agriculture, and engineering, influenced by weather conditions and physical factors. This research utilizes machine learning models with historical data and temporal windowing to predict soil temperature at different depths, effectively addressing nonlinear complexities in environmental studies and meteorology. Utilizing evaluation criteria such as the Root Mean Square Error, Nash-Sutcliffe coefficient, and Pearson Correlation coefficient, it is advisable to employ all four data-based models (Decision Trees, Random Forest, Gradient Boosting Trees, and a hybrid DT-GBT) for accurate soil temperature estimation. The findings reveal that the RF model performed better than others in accurately estimating soil temperature at depths of 10 cm, 20 cm, and 30 cm. Notably, the RF model, followed by the hybrid DT-GBT, GBT, and DT methods, consistently demonstrated accurate predictions across various depths. Overall, the RF model exhibited slightly better performance with significantly faster computation speed compared to other models, making it highly recommended for accurate soil temperature estimation across different depths. These models provide cost-effective alternatives to on-site measurements, delivering valuable benefits to advantages to agriculture. Their accuracy and reliability enhance decision-making processes, thereby improving overall agricultural practices.

Original languageEnglish
Title of host publicationProceedings of the 2nd International Conference on Nonlinear Dynamics and Applications (ICNDA 2024)
Subtitle of host publicationDynamical Models, Communications and Networks
EditorsAsit Saha, Santo Banerjee
Place of PublicationCham, Switzerland
PublisherSpringer
Pages543-557
Number of pages15
Volume3
ISBN (Electronic)9783031691461
ISBN (Print)9783031691454, 9783031691485
DOIs
Publication statusPublished - 10 Dec 2024
Externally publishedYes
Event2nd International Conference on Nonlinear Dynamics and Applications, ICNDA 2024 - Majitar, India
Duration: 21 Feb 202423 Feb 2024
https://icnda.in/

Publication series

NameSpringer Proceedings in Physics
Volume314 SPP
ISSN (Print)0930-8989
ISSN (Electronic)1867-4941

Conference

Conference2nd International Conference on Nonlinear Dynamics and Applications, ICNDA 2024
Country/TerritoryIndia
CityMajitar
Period21/02/2423/02/24
Internet address

Keywords

  • Bangladesh
  • meteorological parameters
  • soil temperature
  • tree-based models

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