Comparative Analysis of Machine Learning and Deep Learning Approaches for Hourly Soil Temperature Estimation At Multiple Depths from Meteorological Data in A Semi-Arid Region of Burkina Faso
Year: 2025
Type: article
Abstract: Accurate estimation of soil temperature is essential for understanding land–atmosphere interactions and managing agricultural systems, par-particularly in semi-arid regions. This study evaluates and compares the performance of multiple modeling approaches: machine learning (Random Forest, XGBoost, LightGBM, SVR, GPR, Decision Tree), and deep learning (MLP-ANN, LSTM, ConvLSTM, Hybrid CNN–LSTM) to predict hourly soil temperature at depths of 10, 20, 30, and 40 cm in a semi-arid land from Burkina Faso. Using high-frequency meteorological data collected, the models were assessed based on their accuracy, temporal generalization, and variable importance. Results show that among ML models, Random Forest demonstrates strong performance up to 30 cm. Deep learning models, particularly ConvLSTM and Hybrid CNN–LSTM, outperform all others across depths, capturing the spatio-temporal variability of subsurface temperature with high fidelity. SHAP analysis reveals that air temperature (Ta) and solar radiation (RAD) dominate at the surface, while dew point tem-temperature (DPT) and vapor pressure deficit (VPD) gain importance in deeper layers. Variables such as rainfall, wind direction, and relative humidity show minimal contribution and can be excluded without sacrificing performance.
DOI: 10.14419/wcverd98
Institution: Université Joseph Ki-Zerbo
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4415104649
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