Conversion of the Time Series of Measured Soil Moisture Data to a Daily Time Step – a Case Study Utilizing the Random Forests Algorithm
Modeling the water content in soil is important for the development of agricultural information systems. Various data are necessary for such modelling. In this paper the authors are proposing a methodology for a frequent situation, i.e., when the modeler is facing a problem due to the lack of available data. Soil water prediction, e.g., for irrigation planning, should be performed with a daily time step. Unfortunately, past measurements of soil moisture, which are necessary for the calibration of a model, are often not available at such a frequency. In the case study presented the soil moisture data were acquired every two weeks. The authors have tested a model utilizing the Random Forests (RF) algorithm, which was used for the conversion of the original data to data with a daily time step. The accuracy of the application of RF to this task is compared with a neural network-based model. The testing accomplished shows that the RF algorithm performs with a higher degree of accuracy and is more suitable for this task.