Forecasting Model of Irrigation Water Requirement Based on Least Squares Support Vector Machine

The irrigation water requirement forecasting is the basis for making scheduling program of water resource and allocating on water in irrigation area rationally and efficiently. The factors influencing the irrigation water are complex and nonlinear, and support vector machine (SVM) has many advantages on nonlinear small samples, therefore, this paper introduces SVM into forecasting irrigation water requirement and proposes a forecasting model of irrigation water requirement based on least squares support vector machine (LS-SVM). Then the forecasting model is applied to estimate the irrigation water requirement of T irrigation area in Tarim River Basin, and is compared with BP artificial neural network (BPANN). The result indicates that the forecasting model based on LS-SVM has an excellent generalization ability and small error. LS-SVM provides an effective method to forecast irrigation water requirement.