Simulation and Analysis of Spatialization of Mean Annual Air Temperature Based on BP Neural Network

  • 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2010-02-19

  Revised date: 2011-07-15

  Online published: 2011-08-23


Air temperature is one of the main influential factors of ecosystem. Ideally, air temperature is mainly affected by longitude, latitude, altitude and the distance from the ocean, so its spatial distribution should show a regular tendency. However, to some extent, the existence of various microcosmic topographical factors (such as slope, aspect, topographic relief,terrain shade land cover, etc.) disturbs its fundamental distribution tendency, even strongly in certain areas, and thus complicates the research and estimation on air temperature. Artificial Neural Network (ANN), which has adaptive capability, high-efficient computing power and powerful nonlinearity approach capability, can effectively improve prediction precision and have generalization capacity. BP neural network is one of the easily understood and most effective methods of ANN. By applying BP neural network and Matlab platform, the spatialization of mean annual air temperature was carried out in this paper, and the spatialization result was compared with those by previous researchers. The comparison confirmed the advantage of the spatialization method. The results from the comparison indicate that BP neural network has higher accuracy with a mean absolute error of 0.51℃ than other spatialization methods, including IDW, Kriging, Spline and Trend. Furthermore, the results of the spatialization are able to describe the distribution of low air temperature in mountain areas, such as the Altai, the Tianshan Mountains, the Kunlun Mountains and the Himalayas in more details. This study not only complements theories, technologies and methods of air temperature spatialization, but also provides an important data product for relevant researches. It also provides a reference to spatialization of other climate data, such as rainfall, evaporation, and so on.

Cite this article

ZHANG Sai, LIAO Shunbao . Simulation and Analysis of Spatialization of Mean Annual Air Temperature Based on BP Neural Network[J]. Journal of Geo-information Science, 2011 , 13(4) : 534 -538 . DOI: 10.3724/SP.J.1047.2011.00534


[1] 于贵瑞,何洪林,刘新安,等. 中国陆地生态信息空间化技术研究(I)——气象/气候信息的空间化途径[J]. 自然资源学报,2004,19(4):537-544.

[2] 廖顺宝,李泽辉.气温数据栅格化中的几个具体问题[J].气象科技2004,32(5):352-356.

[3] 杨凤海,孙彦坤,于太义,等. 近10年黑龙江省气温的时空变异分析[J].地球信息科学学报,2009,11(5): 585-595.

[4] 任传友,于贵瑞,刘新安,等.东北地区热量资源栅格化信息系统的建立与应用[J].资源科学,2003,25(1):66-71.

[5] 刘新安,于贵瑞,范辽生,等.中国陆地生态信息空间化技术研究(Ⅲ)——温度、降水等气候要素[J]. 自然资源学报,2004,19(6):131-138.

[6] 刘志红,Li Lingtao,McVicar T R,等.专用气候数据空间插值软件ANUSPLIN及其应用[J]. 气象,2008,34(2):92-100.

[7] Daly C, Gibson W P, Taylor G H, et al. A Knowledge-based Approach to the Statistical Mapping of Climate[J]. Climate Research, 2002, 22(6): 99-113.

[8] 莫林,张秋文.人工神经网络在降水量空间插值中的应用研究[J]. 计算机与数字工程,2007,35(9):9-12.

[9] 周开利,康耀红.神经网络模型及其MATLAB仿真应用程序设计[M].北京:清华大学出版社,2005.

[10] 高隽. 人工神经网络原理及仿真实例[M]. 北京:机械工业出版社, 2007.

[11] Hornik K, Stinchcombe M, White H. Universal Approximation of an Unknown Mapping and its Derivatives Using in Multiplayer Feedforward Networks[J]. Neural Networks, 1990(3): 55-560.

[12] Hornik K, Stinchcombe M, White H. Multiplayer Feedforward Networks are Universal Approximators[J]. Neural Networks, 1996(3): 359-366.

[13] 刘长安. 人工神经网络的研究方法及应用., 2004, 12.