基于混合模型的中长期降水量预测

Forecasting of Medium and Long Term Precipitation Based on Hybrid Model

针对中长期降水量预测精度较低的问题,提出了由改进集合经验模态分解方法、最小二乘法、核极限学习机和改进的果蝇优化算法构成的混合模型来对区域年度降水量序列进行预测。首先,通过改进集合经验模态分解方法将非平稳降水量时间序列分解为多个分解项。然后,根据不同分解项的特性分别采用最小二乘法和核极限学习机对其进行预测。由于核极限学习机均存在一定的参数敏感特性,因此提出使用改进的果蝇优化算法来对核极限学习机的相关参数搜索寻优,以提高其预测精度。最后,将各分解项的预测结果叠加,从而形成最终预测结果。以广东省7个地市1951-2015年的年度降水量为例,对所提方法进行了验证,结果表明:相比于自回归移动平均模型和核极限学习机模型,混合模型预测具有更高的预测精度。

Accurate estimation of precipitation is an important issue in water resources engineering,management and planning.In order to improve the accuracy of medium and long term precipitation forecasting,a hybrid forecasting model based on modified ensemble empirical mode decomposition,least squares method,kernel extreme learning machine and modified fruit fly optimization algorithm was presented.By using modified ensemble empirical mode decomposition,non-stationary precipitation time series is decomposed into many terms.Then the decomposed terms are predicted by the least square method or the kernel extreme learning machine according to its characteristics.Because the kernel extreme learning machine has some characteristics of parameter sensibility,the modified fruit fly optimization algorithm is used to search the optimal parameters to improve the forecasting accuracy.Finally,forecast results of each decomposed term are added together to obtain the final forecasting results.The method was tested by using annual precipitation data from seven cities in China’s Guangdong province between 1951 and 2015.Results show that compared with the auto-regressive moving average model and kernel extreme learning machine model,the mixed model has higher prediction accuracy.