Multi-Algorithm Hybrid Optimization of Back Propagation (BP) Neural Networks for Reference Crop Evapotranspiration Prediction Models

Abstract

The reference crop evapotranspiration (ET0) statistic is useful for estimating agricultural system water requirements and managing irrigation. In dry areas, the accurate calculation of ET0 is crucial for optimal agricultural water resource utilization. By investigating the relationship between meteorological information and ET0 in Shihezi City, four prediction models were developed: a BP neural network prediction model, a BP neural network prediction model improved by genetic algorithm (GA-BP), a BP neural network prediction model improved by particle swarm algorithm (PSO-BP), as well as an improved hybrid BP neural network prediction model (GA-PSO-BP). The Pearson correlation analysis found that the key parameters influencing ET0 were temperature (Tmax, Tave, Tmin), hours of sunshine (N), relative humidity (RH), wind speed (U), as well as average pressure (AP). Based on the analysis results, different combinations of meteorological input factors were established for modeling, and the results showed that when the input factors were temperature (Tmax, Tave, Tmin), hours of sunshine (N), as well as relative humidity (RH), the overall effect of the ET0 prediction model was better than the other input combinations, and the GA-PSO-BP prediction model was the best, which could provide some guidance for the deployment and use of water resources. This may assist in the allocation and utilization of agricultural water resources in Shihezi.

Keywords

• reference crop evapotranspiration
• BP neural network
• Pearson correlation analysis
• hybrid optimization