Issue |
BIO Web Conf.
Volume 130, 2024
International Scientific Conference on Biotechnology and Food Technology (BFT-2024)
|
|
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Article Number | 02003 | |
Number of page(s) | 8 | |
Section | Soil Biotechnology | |
DOI | https://doi.org/10.1051/bioconf/202413002003 | |
Published online | 09 October 2024 |
Application of machine learning methods to predict soil moisture based on meteorological and atmospheric data
1 Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia
2 Bauman Moscow State Technical University, 105005 Moscow, Russia
3 Siberian Federal University, 660041 Krasnoyarsk, Russia
* Corresponding author: sofaglu2000@mail.ru
The purpose of this study was to develop and evaluate models for predicting soil moisture based on data from meteorological conditions and particle concentrations in the air. Two machine learning methods were used in the work: random forest and linear regression. The results of the study showed that the random forest model achieved 94% accuracy, while the linear regression model showed 92% accuracy. Air temperature, air humidity and the concentration of particles in the air turned out to be important factors affecting soil moisture. Both models offered good predictive capabilities, with an emphasis on the ability of a random forest to adapt to complex nonlinear dependencies, and linear regression to interpret the results. The developed models can be useful for optimizing agricultural processes, managing land resources and environmental monitoring.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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