In-Depth Examination of Machine Learning Models for the Prediction of Ground Temperature at Various Depths

• Published in: Atmosphere Vol. 14; no. 1; p. 68
• Main Authors: Yang, Jong-Won, Dashdondov, Khongorzul
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Accuracy; Agricultural production; Agriculture; Artificial intelligence; Atmosphere; Carbon dioxide; Climate change; Complexity; Corn; Correlation coefficient; Correlation coefficients; Datasets; Depth; Energy; Feature extraction; Food; Gene expression; ground temperature prediction; Heat; Humidity; Independent variables; Learning algorithms; Machine learning; Mathematical models; Measurement methods; Methods; Missing data; ML prediction; Modelling; Multilayers; Neural networks; Predictions; Radiation; Regression analysis; Regression models; Soil temperature; Sorghum; Spearman rank correlation coefficient; Statistical analysis; Statistical models; Support vector machines; Temperature; Time series; Weather stations; XGBoost model; Africa; South Asia
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos14010068

Ground temperature (GT) or soil temperature (ST) is simply the measurement of the warmness of the soil. Even though GT plays a meaningful role in agricultural production, the direct method of measuring the GT is time-consuming, expensive, and requires human effort. The foremost objective of this study is to build machine learning (ML) models for hourly GT prediction at different depths (5, 10, 20, and 30 cm) with the optimum hyperparameter tuning with less complexity. The present study utilizes a statistical model (multiple linear regression (MLR)) and four different ML models (support vector regression (SVR), random forest regression (RFR), multi-layered perceptron (MLP), and XGBoost (XGB)) for predicting GT. Overall, 13 independent variables and 5 GTs with different depths as response variables were collected from a meteorological station at an interval of 1 h between 1 January 2017 and 1 July 2021. In addition, two different input datasets named M1 (selected number of parameters) and M2 (collected dataset with all variables) were used to assess the model. The current study employed the Spearman rank correlation coefficient approach to extract the best features and used it as the M1 dataset; in addition, the present study adopted regression imputation for solving the missing data issues. From the results, the XGB model outperformed the other standard ML-based models in any depth GT prediction (MAE = 1.063; RMSE = 1.679; R2 = 0.978 for GT; MAE = 0.887; RMSE = 1.263; R2 = 0.979 for GT_5; MAE = 0.741; RMSE = 1.025; R2 = 0.985 for GT_10; MAE = 0.416; RMSE = 0.551; R2 = 0.995 for GT_20; MAE = 0.280; RMSE = 0.367; R2 = 0.997 for GT_20). Therefore, the present study developed a simpler, less-complex, faster, and more versatile model to predict the GT at different depths for a short-term prediction with a minimum number of predictor attributes.