An intelligent multi-output regression model for soil moisture prediction
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Soil moisture prediction plays a vital role in developing plants, soil properties, and sustenance of agricultural systems. Considering this motivation, in this study, an intelligent Multi-output regression method was implemented on daily values of meteorological and soil data obtained from Kemalpaşa-Örnekköy station in Izmir, Turkey, at three soil depths (15, 30, and 45 cm) between the years 2017 and 2019. In this study, nine different machine learning algorithms (Linear Regression (LR), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (Lasso), Random Forest (RF), Extra Tree Regression (ETR), Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Histogram-Based Gradient Boosting (HGB)) were compared each other in terms of MAE, RMSE, and R2 metrics. The experiments indicate that the implemented Multi-output regression models show good soil moisture prediction performance. Also, the ETR algorithm provided the best prediction performance with an 0.81 R2 value among the other models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.