Forecast Of Irrigation Water Use Based On Neural Network

The forecast methods for irrigation water use were studied and a case study was carried out in Dongfenqu Irrigation District, Hubei Province. The methods used for the forecast of irrigation water use were first reviewed and the limits were analyzed.Then the linearity stochastic model was introduced and an AR model was used to forecast the monthly irrigation water use. Firstly, the historical data of irrigation water use were pretreated and the time series was made as steady series. Then the forecasting was carried out with AR model. The result showed that the AR model is not conpetent for the forecast of irrigation water use although the time series showed reasonable correlation. Linearity of the model may be the probable matter.Therefore the non-linear model was considered and a BP artificial neural network model was developed. Levenberg-Marquardt (LM) algorithm was used for reducing the training time and combined with genetic algorithm to search the globel optimization points. Using this model, a case study was carried out for Dongfengqu Irrigaiton District and the results were compared with the observed data. The results showed that the model could forecast the irrigation water use reasonably.