Abstract: 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.

Key words: Hybrid model, Kernel extreme learning machine, Least squares method, Modified ensemble empirical mode decomposition, Modified fruit fly optimization algorithm, Prediction