Abstract
In order to improve prediction accuracy of urban heat island intensity, we chose 9 main influencing factors from 1981 to 2006 and predicted urban heat island intensity in Chuxiong city in 2006 with backpropagation neural network. The predicted value was 2.1815°C. Compared with its measured value, residual error was 0.1042, relative error was 4.5588model and GM(1,1) model. The result shows that backpropagation neural network is effective to predict urban heat island intensity.
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Xi, W., He, P. (2010). Prediction of Urban Heat Island Intensity in Chuxiong City with Backpropagation Neural Network. In: Zeng, Z., Wang, J. (eds) Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12990-2_4
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DOI: https://doi.org/10.1007/978-3-642-12990-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12989-6
Online ISBN: 978-3-642-12990-2
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