基于回归BP神经网络误差分级迭代法的逐时气温预测方法研究
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摘要
气温预测是天气预测中的一项主要内容,由于气温的影响因素多而复杂,要想达到精细化预测目的,仍是十分复杂的科学难题.当前学术界的一般方法是假设数学模型对温度物理过程进行研究,建立了BP神经网络模型、温度与相对湿度之间的回归模型,最终在回归模型的基础上通过改进的BP神经网络建模,即利用BP神经网络误差分级迭代法建模,通过历史温度进行逐时气温预测,全样本误差达到0.617℃.
Forecasting air temperature is one of the main contents for the weather forecasting.Due to the complexity characteristics of the influence factors of temperature, it is still a complicated scientific difficult problem to meet the requirements of refined prediction. The general method of current academic circles is to study physical process of temperature based on mathematical model. In this paper, the BP neural network model and regression model between the temperature and relative humidity are established. Finally, the paper establishes improved BP neural network model on the basis of the regression model, which is named the error classification iterative method. The model is used to forecast time-time air temperature by using the historical temperature, which result of the sampling error is 0.617℃.
Forecasting air temperature is one of the main contents for the weather forecasting.Due to the complexity characteristics of the influence factors of temperature, it is still a complicated scientific difficult problem to meet the requirements of refined prediction. The general method of current academic circles is to study physical process of temperature based on mathematical model. In this paper, the BP neural network model and regression model between the temperature and relative humidity are established. Finally, the paper establishes improved BP neural network model on the basis of the regression model, which is named the error classification iterative method. The model is used to forecast time-time air temperature by using the historical temperature, which result of the sampling error is 0.617℃.
引文
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[6]杨瑞梅.提高天气预报和气候预测的方法[J].现代农业科技,2012(24):253-253.
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[8]郯俊岭,江志红,马婷婷.基于贝叶斯模型的中国未来气温变化预估及不确定性分析[J].气象学报,2016,74(4):583-597.
[9]王芳,涂春丽,勾永尧.基于Elman神经网络的气温预测研究[J].安徽农业科学,2011, 39(33):20859-20860.
[10]薛宇峰,杨超梅.基于卡尔曼滤波的神经网络月平均气温预测模型[J].四川气象,2005, 25(4):4-6.
[11]张成,屈卫东.基于RBF神经网络的临近空间气温预测模型[J].控制工程,2008, 15(增刊):106-108+112.
[12]张赛,廖顺宝.多年平均气温空间化BP神经网络模型的模拟分析[J].地球信息科学学报,2011, 13(4):534-538.
[13]胡伍生神经网络理论及其工程应用[M]北京:测绘出版社,2006.
[14]胡伍生,迪达尔,王昭斌.神经网络BP改进算法及其性能分析[J].现代测绘,2016, 39(6):1-4.
[2]潘留杰,薛春芳,王建鹏,等.一个简单的格点温度预报订正方法[J].气象,2017,43(12):1584-1593.
[3]朱庆杰,陈静,卢时林.贝叶斯预测模型在气温变化预测中的应用[J].河北联合大学学报(自然科学版),2002, 24(4):91-98.
[4]丛凌博,蔡吉花.ARMA模型在哈尔滨气温预测中的应用[J].数学的实践与认识,2012, 42(16):190-195.
[5]曹晓宇.气温实时预测方法探究[J].科技视界,2012(24):134-135.
[6]杨瑞梅.提高天气预报和气候预测的方法[J].现代农业科技,2012(24):253-253.
[7]赵义军,张小轩,商梦娇.多元回归分析在温度预测中的应用及R实现[J].佳木斯大学学报(自然科学版),2016, 34(6):1020-1025.
[8]郯俊岭,江志红,马婷婷.基于贝叶斯模型的中国未来气温变化预估及不确定性分析[J].气象学报,2016,74(4):583-597.
[9]王芳,涂春丽,勾永尧.基于Elman神经网络的气温预测研究[J].安徽农业科学,2011, 39(33):20859-20860.
[10]薛宇峰,杨超梅.基于卡尔曼滤波的神经网络月平均气温预测模型[J].四川气象,2005, 25(4):4-6.
[11]张成,屈卫东.基于RBF神经网络的临近空间气温预测模型[J].控制工程,2008, 15(增刊):106-108+112.
[12]张赛,廖顺宝.多年平均气温空间化BP神经网络模型的模拟分析[J].地球信息科学学报,2011, 13(4):534-538.
[13]胡伍生神经网络理论及其工程应用[M]北京:测绘出版社,2006.
[14]胡伍生,迪达尔,王昭斌.神经网络BP改进算法及其性能分析[J].现代测绘,2016, 39(6):1-4.