Predicting wetland soil properties using machine learning, geophysics, and soil measurement data
Dejene L. Driba,
Efemena D. Emmanuel,
Kennedy O. Doro
Abstract:Purpose
Machine learning models can improve the prediction of spatial variation of wetland soil properties, such as soil moisture content (SMC) and soil organic matter (SOM). Their performance, however, relies on the quantity of data used to train the model, limiting their use with insufficient data. In this study, we assessed the use of synthetic data constrained by limited field data for training an eXtreme Gradient Boosting (XGBoost) algorithm used to predict the distribution of soil propertie… Show more
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