地理科学 ›› 2016, Vol. 36 ›› Issue (5): 780-786.doi: 10.13249/j.cnki.sgs.2016.05.017

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基于LM-BP神经网络的西北地区太阳辐射时空变化研究

李净1(), 冯姣姣1, 王卫东2, 张福存3   

  1. 1.西北师范大学地理与环境科学学院,甘肃 兰州 730070
    2.甘肃省测绘工程院,甘肃 兰州 730000
    3.西宁市测绘院,青海 西宁 810000
  • 收稿日期:2015-04-08 修回日期:2015-10-28 出版日期:2016-07-20 发布日期:2016-07-20
  • 作者简介:

    作者简介:李净(1978-),女,甘肃会宁人,博士,副教授,主要研究方向为辐射平衡和定量遥感。E-mail:li_jinger@163.com

  • 基金资助:
    甘肃省科技计划项目(1308RJZA141) 资助。

Spatial and Temporal Changes in Solar Radiation of Northwest China Based LM-BP Neural Network

Jing Li1(), Jiaojiao Feng1, Weidong Wang2, Fucun Zhang3   

  1. 1. College of Geographical and Environmental Science, Northwest Normal University, Lanzhou 730070, Gansu,China
    2.Gansu Province Academy of Engineering of Surveying and Mapping, Lanzhou 730000, Gansu,China 3. Xining Institute of Surveying and Mapping, Xining 810000,Qinghai,China)
  • Received:2015-04-08 Revised:2015-10-28 Online:2016-07-20 Published:2016-07-20
  • Supported by:
    Gansu Province Science and Technology Program (1308RJZA141)

摘要:

定量模拟太阳辐射对认识西北地区气候变化至关重要,但西北地区辐射站点稀少,而气象站点较多,利用众多的气象站点观测值模拟太阳辐射是获得太阳辐射数据很好的方法之一。利用LM (Levenberg-Marquardt) 算法对普通的BP神经网络进行优化(优化后的BP神经网络简称LM-BP神经网络)模拟太阳辐射,通过与传统气候模型模拟的太阳辐射结果对比发现,LM-BP神经网络模型的模拟精度最高,模拟值与实测值的拟合程度明显优于H-S模型和A-P模型。由此利用西北地区159个气象站点的气象数据和LM-BP神经网络模型模拟了1990~2012年这些气象站点的太阳总辐射月总量,将LM-BP神经网络模拟的气象站点的太阳辐射和25个辐射观测站的实测太阳辐射数据相结合,通过空间插值得到了西北地区太阳总辐射的空间分布,并分析了其时空分布及变化特征。研究结果发现西北地区1990~2012年的年均总辐射月总量变化为262~643 βMJ/m2,呈现“中间高,两端低”的空间分布特征。LM-BP神经网络模型的模拟精度高,是一种很有发展前景的辐射模拟方法,可将其应用在无辐射观测地区的太阳辐射模拟中。

关键词: LM-BP神经网络, 太阳辐射, 时空变化, 西北地区

Abstract:

Solar radiation data can be used to simulate surface dynamic and thermal process. Solar radiation data is the important input parameter of the models in ecology, hydrology, crop, solar radiation transmission, global circulation and so on. Quantitative simulation of solar radiation is important for understanding climate change in Northwest China. However, the solar radiation stations are sparse in Northwest China, so using small amount of radiation site data interpolating or extrapolating is difficult to obtain the spatial distribution of solar radiation data. There are more many weather stations in Northwest China, so it is one of the best methods to simulate the solar radiation by using a large number of meteorological observations. In this article, the LM (Levenberg-Marquardt) algorithm is used to optimize the BP neural network (LM-BP neural network is abbreviation of BP neural network for the optimization). This article simulates solar radiation using LM-BP neural network, H-S and A-P climate models at Urumqi, Kashi, Hami, Xining and Guyuan radiation stations and uses MPE, MBE and RMSE indexes of accuracy assessment to test the three models. The results indicate that LM-BP neural network has the highest accuracy in model simulations, showing satisfactory performance compared with the simulation results of traditional two climate models, simulated and observed values of fitting degree model is superior to H-S and A-P climate models. So we selects the LM-BP neural network model to simulate solar radiation in Northwest China. Basing on the meteorological data from 159 weather stations in Northwest, we apply the BP neural network optimized LM (Levenberg - Marquardt) algorithm to simulate the total month solar radiation during 1990-2012 in these meteorological observation stations. Then the solar radiation value of the 159 weather stations and the measured radiation data of the 25 radiation observation station to obtain the spatial-temporal distribution of annual average solar radiation by interpolation, and analyzes. These results indicate that average annual total radiation in 1990-2012 in Northwestern ranges from 262 MJ/m2 to 643 MJ/m2, presenting the distribution pattern of high in the middle, low on both end. LM neural network is a promising method for solar radiation simulation, which can be used in the simulation of solar radiation in the area of no radiation observation.

Key words: LM-BP neural network, solar radiation, spatial and temporal changes, Northwest China

中图分类号: 

  • P422.1