Farmland reference crop evapotranspiration prediction method considering uncertainty of meteorological factors

Abstract

A farmland reference crop evapotranspiration prediction method based on uncertainty of meteorological factors, includes: S1. acquiring a set number of groups of weather forecast data of a prediction region within a preset time period; S2. inputting each group of weather forecast data into a Bayesian probability forecast system to obtain corrected weather forecast data; and S3. inputting each group of the corrected weather forecast data into a trained RBF neural network, and predicting to obtain a farmland reference crop evapotranspiration. In the present invention, the Bayesian probability forecast system is configured to correct the weather forecast data and eliminate uncertainty of weather forecast data to obtain the accurate reference crop evapotranspiration forecasted by the RBF neural network using these data.

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