A Meteorological Predication Model Based on Storm and Online Sequential Extreme Learning Machine

In order to improve the accuracy of meteorological forecasting, deal with frequent local meteorological disasters in real time, and have higher efficiency of dealing with massive data, this paper proposes a meteorological forecasting model using the Storm-based online sequential extreme learning machine. The model firstly initializes multiple online extreme learning machine. When new batches of data arrive, the model continually studies the new data samples based on the training results, and introduces the stochastic gradient descent method and the error weight adjustment method to give the error feedback for new prediction results and then update the error weight parameters in real time, and finally to improve prediction accuracy. In addition, the Storm flow processing framework is adopted to improve the proposed model in the aspect of parallelism in order to enhance the ability of dealing with massive high-dimensional data. The experimental results show that compared with the Hadoop-based parallel extreme learning machine (PELM), the proposed model has higher prediction accuracy and more excellent parallelism.