Abstract

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.

Highlights

  • The structure and function of the natural hydrological system rests with the soil moisture in the hydrological cycle [1,2,3,4]

  • The soil moisture data were collected during the period from 28 February 2012 to 8 November 2016, in Beijing, China

  • An Radially Uniform (RU) design algorithm was applied to sample representative data points to develop the Gaussian Process Regression (GPR) model in predicting the soil moisture

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Summary

Introduction

The structure and function of the natural hydrological system rests with the soil moisture in the hydrological cycle [1,2,3,4]. Drought is one of the main natural disasters for agriculture all over the world. Soil moisture is an important variable for drought assessment and forecasting [6,7,8], as well as flood and landslide simulation and prediction [9,10,11,12]. Soil moisture forecasting is of great importance to flood management. Soil moisture in the functional landscape describes initial conditions of the watershed well and may provide valuable information for flood forecasting and early warning systems [14]

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