【摘要】 针对如何从海量的气象数据中挖掘出有用的知识,并提高气象预报的准确度,提出了在Hadoop平台上构建基于遗传神经网络算法的天气预报方法.该方法采用遗传算法与神经网络算法相结合,避免了传统算法容易陷入局部最优的问题,并以天津市13个台站1951–2006年的地面气候资料日值数据为基础,建立了遗传神经网络预测模型,最后以降雨量等级为决策属性进行了实验.结果表明,该方法对所有降水等级的预测准确率都要优于传统的神经网络算法,对于降水等级R0的预测精度最高,达到了87%,不仅可以有效的处理海量气象数据,同时具有较高的预测精准度和良好的扩展性,为天气预报提拱了一种全新的思路和方法.更多还原

【Abstract】 Aiming at how to dig out useful knowledge from the massive meteorological data and improve the accuracy of meteorological forecast, this paper proposed a weather forecast method based on the genetic neural network algorithm on Hadoop platform. The method combined genetic algorithm with neural network algorithm, which could avoid the problem of local optimization in traditional algorithm. Then, the genetic neural network forecasting model is established, and the daily data of the ground climate from 1951 to 2006 of 13 stations in Tianjin is used as experimental data. Finally, the experiment is performed taking the rainfall level as decision attribute, and the results show that the method proposed in this paper can get better prediction accuracy for all rainfall level than traditional neural network algorithm. It has the highest prediction precision for the rainfall level R0 and reaches 87%, which can not only effectively deal with mass meteorological data, but also has high prediction precision and good scalability, it proposes a new way of thinking and method for weather forecast.更多还原