基于PSO与LS-SVM的作物需水量预测

为了提高作物需水量预测精度，提出基于粒子群优化算法(PSO)优化最小二乘支持向量机(LS-SVM)的预测模型。该模型以空气湿度、温度、太阳辐射以及风速为输入，利用多项式核函数和径向基核函数的非负线性组合构造核函数，将粒子群优化算法(PSO)与交叉验证方法用于确定模型参数。实验结果表明与神经网络和随机森林相比，PSO优化的LS-SVM可获得更好的预测精度和泛化能力，可用于节水灌溉，具有较高的应用价值。

Predicting Crop Water Requirements Based on Particle Swarm Optimization #br#and Least Square Support Vector Machine

To improve the accuracy of crop water requirement prediction, a model based on Least Square Support Vector Machine (LS-SVM) optimized by Particle Swarm Optimization (PSO) is put forward. Relative humidity, air temperature,  solar radiation and wind speed are considered as input variables. A nonnegative linear combination of polynomial kernel function and radial basis kernel function is used as the kernel function of LS-SVM. PSO and cross validation are applied to optimize the parameters of LS-SVM. Experimental results indicate that LS-SVM optimized by PSO outperforms neural network and random forest. It can be used for water-saving irrigation, and has good application value.