摘要
利用张掖国家湿地公园冬季水域结冰厚度观测资料和张掖观象台的气温、地温气象资料,运用统计学方法和BP神经网络方法建立了张掖国家湿地公园水域结冰厚度预报方程。通过对不同的预报方法进行预报效果验证,该结冰厚度的预报模型能够对结冰厚度有比较理想的预报效果,流动水域结冰厚度预报历史拟合率分别为:80.6%(多元回归)、74.6%(逐步回归)、100%(BP神经网络);模型试报准确率分别为:72.7%(多元回归)、72.7%(逐步回归)、81.8%(BP神经网络)。静止水域结冰厚度预测历史拟合率分别为:76.9%(多元回归)、71.8%(逐步回归)、93.5%(BP神经网络);模型试报准确率分别为:76.0%(多元回归)、72.0%(逐步回归)、84.0%(BP神经网络)。结果表明:多元回归方法优于逐步回归方法,而BP神经网络又明显优于传统的统计学方法,数据显示该结冰厚度的预报模型能够对结冰厚度有较好的预报效果,预报模型能够对水域结冰厚度进行有效的短期预报,其性能指标符合实际要求,具有很好的实际应用价值。
Based on the observations of the ice thickness of the water area of the Zhangye National Wetland Park, and air temperature and ground temperature observed by the Zhangye Meteorological Observatory, the icing thickness prediction equation was established by using statistical and BP neural network methods. Through verifying the forecast effects of different forecasting methods, the prediction model could forecast the icing thickness better. The historical fitting rate of icing thickness forecast of flowing waters is 80.6% （ multi- variate regression）, 74.6% （ stepwise regression）, 100% （ BP neural network）, respectively, and the model forecast accuracy is 72. 7% （multivariate regression）, 72.7% （stepwise regression）, 81.8% （BP neural network）, respectively. The historical fitting rate of icing thickness forecast of static water is 76.9% （ multivariate regression）, 71.8% （ stepwise regression）, 93.5% （ BP neural net- work）, respectively, and the model forecast accuracy is 76.0% （ multivariate regression）, 72.0% （ stepwise regression）, 84.0% （ BP neural network） , respectively. Results show that the multiple regression method is better than that of stepwise regression method, and the BP neural network is superior to traditional statistical methods, and the prediction model of icing thickness had better forecast effect. It is also showed that these prediction models have significant short -term forecast skills and can be used practically.
出处
《干旱气象》
2013年第2期425-431,共7页
Journal of Arid Meteorology
基金
甘肃省气象局气象科研项目"张掖国家湿地公园水域结冰厚度预报服务系统研究"(2012-08)
甘肃省气象局第六批"十人计划"共同资助
关键词
水域
BP神经网络
统计预报
模型
结冰厚度
water area
BP neural network, statistical forecast
forecast model
ice thickness
