基于PSO与LS-SVM的作物需水量预测

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

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LSSVM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。

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.