Study On Real-Time Crop Evapotranspiration Forecast In Hunpu Irrigation Area

The crop evapotranspiration forecast is core content of the real-time irrigation forecast. Real-time irrigation forecast is the necessary conditions of establishment and execution irrigation area dynamic irrigation water plan. It is also the core content of realizing irrigation area irrigation water-saving. Only real-time irrigation forecast reliable, accurate, dynamic water plan can be practical and play guiding water, to obtain water-saving, high-yield and high benefit effect. The irrigation forecast development tendency is the development real-time irrigation forecast, therefore the study of crop evapotranspiration has the important theory significance and the practical value.The reference crop evapotranspiration forecast is the most basic in crop evapotranspiration forecast. Therefore, according to Shenyang City Hunpu irrigation practical information this paper focuses on daily reference crop evapotranspiration forecast model, and discuss on the crop evapotranspiration and field soil moisture forecast.The paper introduces the back propagation(BP) network and particle swarm optimization(PSO). The BP network contains the essence part of the neural network, its simple structure, high plasticity, clear mathematical significance, distinct learning algorithm steps; but it has its own shortcomings, such as slow convergence, easy to fall into a local minimum, redundancy, learning and memory of instability and so on. Particle Swarm Optimization (PSO) is a smart intelligence algorithm, it has many advantages, such as strong discovery ability, strong memory function, higher efficiency, fast convergence, simple operation, easy to integrate with other methods, easy to program realization and so on. A model of PSO-BP is established for predicting ET₀.This model integrates PSO global optimization function and BP network mapping capability advantages, and overcomes the shortcomings such as network slow learning,easy to fall into the local minimum and network poor stability. This model has adaptability and good predictive ability.The reference crop evapotranspiration of Hunpu irrigation area is predicted by PSO-BP. The daily weather information from 1995 to 2004 is as basis information of this study. Through the various meteorological data analysis, the daily average temperature, sunshine hours and the sequence number are selected as the neural network input vector; daily reference crop evapotranspiration is as network output vector. The number of hidden layer is one.The number of hidden layer neuron is four that is determined by model experiments. Matlab7.0 is used for programming and training. To 1997 data as test samples as an example, the reference crop evapotranspiration is forecasted by BP network and PSO-BP. BP network and PSO-BP use the same sample data and training function. Through the analysis of the forecast data and original data, it shows that the maximum relative error of the reference crop evapotranspiration forecasted by PSO-BP is 25.15% and the error is smaller than BP network's. Forecast data of 1997 is 137. PSO-BP forecasting data relative absolute error less than 10% is 101,less than 15% is 22, less than 20% is 8, less than 25% is 5 and less than 30% is 1.The forecast qualified rate is high. The network forecast result curve and the primary data curve basically superposes; it proves the PSO-BP forecast ability is strong. Selecting some data from BP networks and PSO-BP forecast results and comparing these data,the comparison result shows that PSO-BP forecast results is closer to actual value. In the training process, PSO-BP iteration 400 precision can be achieved, BP network requires 20,000 training to achieve the required precision.The crop evapotranspiration determination method and computation method is summarized. According to Hunpu irrigation area actual situation, this paper chooses single crop coefficient approach to calculate Hunpu irrigation area crop evapotranspiration. K_c adopts the CAS Shenyang Experimental Station results. Irrigation area crop evapotranspiration is forecasted by K_c and PSO-BP forecast reference crop evapotranspiration.