干旱区生态环境知识资源中心(Arid): 基于小波变换与神经网络的石羊河流域夏季地温预测模型研究

Arid

	基于小波变换与神经网络的石羊河流域夏季地温预测模型研究
其他题名	Prediction model of summer land surface temperature in the Shiyang River basin based on the wavelet transform and neural network
	贾东于; 李开明; 聂晓英; 袁春霞; 李清峰; 高福元
来源期刊	冰川冻土
ISSN	1000-0240
出版年	2020
卷号	42 期号:2 页码:412-422
中文摘要	地温变化在气候反馈效应中起着重要作用,理解地温及其与影响因素之间的时空关系对预测全球温度变化至关重要。利用1998 - 2017年石羊河流域的逐日常规气象观测资料,采用小波分析结合BP(Back Propagation)神经网络构建了石羊河流域夏季地温预报模型,结果表明:日平均地温预测效果在不同站点均为最佳,其中预测值和观测值的相关系数均大于0.87,3 ℃以内的预测概率均大于84%。其中,民勤地区地温预测效果最好,预测值和观测值的相关系数达到0.91,3 ℃以内的预测概率达到86%。日最高地温的预测值与观测值的相关系数高于0.8,但误差平方和、标准差较大。永昌地区日最高地温的模拟效果最好,3 ℃以内的预测概率达到83%。日最低地温的预测与观测值的平均相关系数高于0.66,3 ℃以内的预报概率高于83%,但预测值略低。其中,武威地区日最低地温的预测效果最好,预测值与观测值的相关系数为0.72,3 ℃以内的预测概率达到94%。研究成果可为有效弥补干旱、半干旱区地温观测资料缺失和探讨其与局地气候的关系提供一些参考。
英文摘要	The change of ground temperature plays an important role in the climate feedback effect,and understanding the spatio-temporal relationship between ground temperature and its influencing factors is crucial to the prediction of global temperature change. The summer land surface temperature(LST)was constructed by wavelet analysis and BP neural network based on the daily observed data of meteorological stations in the Shiyang River basin from 1998 to 2017. The prediction results and the accuracy are tested. The results show that:(1)The prediction effect of daily average land surface temperature is the best at different stations,and the correlation coefficients between the predicted values and the observed values are both greater than 0.87,and the prediction probability within 3 ℃ is higher than 84%. Among them,Minqin has the best prediction results. The correlation coefficient between predicted and observed values reaches 0.91,and the prediction probability within 3 ℃ is 86%. (2)The prediction results of daily maximum LST in the Shiyang River basin can reflect its variation trend,and the correlation coefficient between the predicted value and the observed value is higher than 0.8. Among them,the simulation effect in Yongchang is the best,and the prediction probability within 3 ℃ is 83%. (3)For the daily minimum LST,the average correlation coefficient between the observed and simulated values is higher than 0.66,but it is little underestimated. The prediction probability of daily lowest LST in the Shiyang River basin at different stations within 3 ℃ is all higher than 83%. Among them,Wuwei has the best forecasting effect,the correlation coefficient between forecasting value and observation value is 0.72,and the forecasting probability within 3 ℃ reaches 94%. This study can provide some references for making up for the lack of ground temperature observation in arid and semi-arid areas and discussing its relationship with local climate.
中文关键词	地温 ; 石羊河流域 ; 小波变换 ; 神经网络 ; 预测
英文关键词	land surface temperature(LST) Shiyang River basin wavelet transform neural network prediction
类型	Article
语种	中文
收录类别	CSCD
WOS研究方向	Meteorology & Atmospheric Sciences
CSCD记录号	CSCD:6809882
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/336924
作者单位	贾东于, 兰州城市学院地理与环境工程学院, 兰州, 甘肃 730070, 中国.; 李开明, 兰州城市学院地理与环境工程学院, 兰州, 甘肃 730070, 中国.; 聂晓英, 兰州城市学院地理与环境工程学院, 兰州, 甘肃 730070, 中国.; 袁春霞, 兰州城市学院地理与环境工程学院, 兰州, 甘肃 730070, 中国.; 李清峰, 兰州城市学院地理与环境工程学院, 兰州, 甘肃 730070, 中国.; 高福元, 兰州城市学院地理与环境工程学院, 兰州, 甘肃 730070, 中国.
推荐引用方式 GB/T 7714	贾东于,李开明,聂晓英,等. 基于小波变换与神经网络的石羊河流域夏季地温预测模型研究[J],2020,42(2):412-422.
APA	贾东于,李开明,聂晓英,袁春霞,李清峰,&高福元.(2020).基于小波变换与神经网络的石羊河流域夏季地温预测模型研究.冰川冻土,42(2),412-422.
MLA	贾东于,et al."基于小波变换与神经网络的石羊河流域夏季地温预测模型研究".冰川冻土 42.2(2020):412-422.