Simulation of Water Demand for Farmland Subarea Irrigation Based on Bayesian Neural Network

Aiming at problem that common solution process in simulation process of farmland partitioned irrigation water demand was prone to fall into local minimization, excessive fitting, and excessive dependence on historical water use data, resulting in significant errors in final simulation results, a simulation analysis method of farmland partitioned irrigation water demand based on Bayesian neural network was studied． Water demand in previous week, proportion of monthly water demand in the year, upper limit of daily temperature and daily rainfall were taken as indicators, and average value of indicator data was obtained through cluster analysis, and sample data of historical water use for farmland subarea irrigation were clustered． Bayesian neural network model was built． Average value of index data was input into model, and indicator data were trained according to BP neural network principle and Bayesian rule, at last, simulation results of irrigation water demand of farmland were output． Experimental results showed that correlation between data in data clustering results was higher than 95%, data fitting effect was good, and simulation of water demand had higher accuracy and stability．