基于PSO-LSSVM的干旱区中长期降水预测模型研究

降水量的准确预测对于干旱地区的水资源综合利用、抗旱减灾有重要意义。引入基于粒子群算法进行参数寻优的最小二乘支持向量机模型(PSO-LSSVM),构建考虑7a周期的年降水样本及考虑季节性特征的月降水样本,建立干旱区年、月尺度下的中长期降水预测模型,并应用新疆阿勒泰地区1960—2013年实测降水序列,验证模型的适用性。结果表明基于粒子群算法与最小二乘支持向量机的中长期降水预测模型预测精度高,泛化能力强,能有效地预测新疆阿勒泰地区年、月降水量。该模型为干旱区中长期降水预测提供了一种可靠的研究思路与方法。

Model of Medium-long-term Precipitation Forecasting in Arid Areas Based on PSO and LS-SVM Methods

Precipitation forecasting in arid region is of great significance for water resources utilization and drought disaster reduction.A precipitation forecasting model in yearly and monthly scales based on particle swarm algorithm (PSO) and least squares support vector machine (LSSVM) model was established using the annual precipitation sample of a seven-year cycle and the monthly precipitation sample of seasonal characteristics .The applicability of the model was verified through the measured precipitation sequence from 1960 to 2013 in Altay region.Results show that the model based on PSO and LSSVM could effectively forecast the annual and monthly precipitation in Altay re -gion, hence is of high precision and strong generalization ability .It offers a reliable research idea and method for medium and long-term precipitation forecast in arid areas .