Short-Term Soil Moisture Forecasting via Gaussian Process Regression with Sample Selection

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Original languageEnglish
Article number3085
Number of pages17
Journal / PublicationWater (Switzerland)
Issue number11
Online published3 Nov 2020
Publication statusPublished - Nov 2020
Externally publishedYes



Soil moisture is a critical limiting factor for crop growth. Accurate soil moisture prediction helps to schedule irrigation and improve the crop production. A soil moisture prediction method based on Gaussian Process Regression (GPR) is proposed in this paper. In order to reduce the computation time of the GPR model, the Radially Uniform (RU) design algorithm was incorporated into the sample selection during the training procedure. Thus, representative training samples are identified and less training time is required. To validate the proposed prediction model, the soil moisture data collected in Beijing, China, was fully utilized. The experimental results demonstrate that the forecasting performance of the GPR model with the RU design algorithm is generally better than that of the generic GPR model in terms of less forecasting errors for both deterministic and probabilistic forecasting, while less computing time is needed for the model training.

Research Area(s)

  • Gaussian Process Regression, Radially Uniform design, Soil moisture forecasting

Bibliographic Note

Publisher Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

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