Farmland Evapotranspiration Prediction Using Bayesian Correction
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Solution Overview
Problem
Conventional methods for predicting reference crop evapotranspiration (ET0) are inaccurate due to the uncertainty of meteorological factors, leading to inefficient water management in farmland irrigation.
Innovation Solution
A method that involves acquiring weather forecast data, correcting it using a Bayesian probability forecast system, and inputting the corrected data into a trained radial basis function (RBF) neural network to predict farmland reference crop evapotranspiration, enhancing prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional water consumption prediction models are used to predict ET0, then the prediction process is simple, but the prediction accuracy is low due to the complex non-linear relationship between ET0 and meteorological factors
Solution Approach 1:
The patent introduces a Bayesian probability forecast system as an intermediary component between the input meteorological data and the RBF neural network. This intermediary corrects the weather forecast data by considering the uncertainty of meteorological factors, thereby improving the overall prediction accuracy of ET0 without requiring the neural network itself to be overly complex
Solution Approach 2:
The patent changes the parameters fed into the prediction model by correcting the weather forecast data using Bayesian probability. Instead of using raw forecast data directly, the system transforms the data by incorporating uncertainty analysis, which modifies the input parameters to better reflect real-world conditions and improve prediction accuracy
2Reliability
If conventional prediction models ignore meteorological factor uncertainty, then the calculation is straightforward, but the prediction results are easily distorted and inaccurate
Solution Approach 1:
The patent performs preliminary correction of the weather forecast data using Bayesian probability before feeding it into the RBF neural network. By addressing the uncertainty of meteorological factors in advance, the system ensures that the data entering the prediction model is already adjusted for reliability, preventing distortion in the final ET0 prediction results
Solution Approach 2:
The Bayesian probability forecast system provides feedback by comparing the uncertainty characteristics of meteorological factors and adjusting the forecast data accordingly. This feedback mechanism continuously refines the input data quality, ensuring that the prediction model receives corrected data that accounts for meteorological uncertainty
Data Source
AI summary
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.