Conversion of the Time Series of Measured Soil Moisture Data to a Daily Time Step – a Case Study Utilizing the Random Forests Algorithm

Milan Cisty, Lubomir Celar
2016 Journal of Sustainable Development of Energy, Water and Environment Systems  
Cite as: Cisty, M., Celar, L., Conversion of the Time Series of Measured Soil Moisture Data to a Daily Time Step -A Case Study Utilizing the Random Forests Algorithm, J. sustain. dev. energy water environ. syst., 4(2), pp 183-192, 2016, DOI: http://dx. ABSTRACT 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.,
more » ... 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 networkbased model. The testing accomplished shows that the RF algorithm performs with a higher degree of accuracy and is more suitable for this task.
doi:10.13044/j.sdewes.2016.04.0015 fatcat:avnwt4g5b5ebtmkt77d4qedfoa