Prediction of urban heat island intensity in chuxiong city with backpropagation neural network

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?. Compared with its measured value, residual error was 0.1042, relative error was 4.5588%. It was superior to results of SCGM model and GM(1,1) model. The result shows that backpropagation neural network is effective to predict urban heat island intensity. © 2010 Springer-Verlag Berlin Heidelberg.