Farmland reference crop evapotranspiration prediction method considering meteorological factor uncertainty

Abstract

The invention discloses a farmland reference crop evapotranspiration prediction method considering meteorological factor uncertainty. The farmland reference crop evapotranspiration prediction method comprises the steps of S1, obtaining a set number of groups of weather forecast data of a prediction area in 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 corrected weather forecast data into a trained RBF neural network, and predicting to obtain the evapotranspiration of the farmland reference crops. According to the scheme, after the Bayesian probability forecasting system is adopted to correct the weather forecast data, the uncertainty of the weather forecast data can be eliminated, so that the reference crop evapotranspiration predicted by the RBF neural network by adopting the data is more accurate, and therefore, supplementation can be performed more accurately according to the water demand of crops during irrigation, and normal production of crops can be guaranteed while water is saved.

Description

technical field [0001] The invention relates to farmland irrigation scheduling, in particular to a farmland reference crop evapotranspiration prediction method considering the uncertainty of meteorological factors. Background technique [0002] Reference transpiration (ET 0 ) is an important data of water resources and irrigation management, and a key factor in water balance and irrigation scheduling. Configuration and so on play an important role. Therefore, in order to better manage crop irrigation water consumption and improve crop water use efficiency, there is an urgent need for crop ET 0 Make accurate predictions. [0003] The Penman-Monteith (PM) model recommended by FAO-56 in 1998 is to calculate ET 0 The standard calculation method has a rigorous theoretical basis and high calculation accuracy. Over the past 60 years, global ET 0 A significant decline has occurred, and the change is closely related to natural factors such as climate change. In recent years, m...

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