Machine Learning for Predicting Field Soil Moisture Using Soil, Crop, and Nearby Weather Station Data in the Red River Valley of the North

Abstract

Precise soil moisture prediction is important for water management and logistics of on-farm operations. However, soil moisture is affected by various soil, crop, and meteorological factors, and it is difficult to establish ideal mathematical models for moisture prediction. We investigated various machine learning techniques for predicting soil moisture in the Red River Valley of the North (RRVN). Specifically, the evaluated machine learning techniques included classification and regression trees (CART), random forest regression (RFR), boosted regression trees (BRT), multiple linear regression (MLR), support vector regression (SVR), and artificial neural networks (ANN). The objective of this study was to determine the effectiveness of these machine learning techniques and evaluate the importance of predictor variables. The RFR and BRT algorithms performed the best, with mean absolute errors (MAE) of 3 m−3 and root mean square errors (RMSE) of 0.045 and 0.048 m3 m−3, respectively. Similarly, RFR, SVR, and BRT showed high correlations (r2 of 0.72, 0.65 and 0.67 respectively) between predicted and measured soil moisture. The CART, RFR, and BRT models showed that soil moisture at nearby weather stations had the highest relative influence on moisture prediction, followed by 4-day cumulative rainfall and PET, subsequently followed by bulk density and Ksat.

Keywords

• random forest regression
• accumulated local effects
• variable importance