Work

Application of machine learning methods to predict soil moisture based on meteorological and atmospheric data

Year: 2024

Type: article

Abstract: 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.

Source: Springer Link (Chiba Institute of Technology)

DOI: 10.1051/bioconf/202413002003/pdf

Authors: Vadim Tynchenko, Oksana Kukartseva, Ksenia Degtyareva, Yesenia Khrapunova, Pavel Anisimov

Institutions: Bauman Moscow State Technical University, Siberian State Aerospace University, Siberian Federal University

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Life on land

Open Access status: green

OpenAlex ID: W6940562278

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