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.
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
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