Modeling of Urban Heat Island using Artificial Neural Network and Remote Sensing in Tehran

Abstract

The Urban Heat Island phenomenon happens due to the differences in the thermal behavior between urban and rural areas which many factors such as vegetated, water, impervious and built-up areas could affect this phenomenon. Urban Heat Island consists of three types: Canopy heat island, Boundary heat island, and surface heat island. In this study, the surface type of urban heat island is analyzed. In this paper, 13 TM/ETM+ images have been obtained from 1990 to 2015(an image biennially). Urban Heat Islands effects are much more severe in summer; therefore, all images have been taken in summer. NDVI, IBI, albedo, and also land surface temperature were derived from images. Various neural network topologies have been used to identify the best model for predicting the urban heat island intensity. The LST of 2016 has been considered as validation data, thus the best result from fitting structures was obtained from Cascade which the Bayesian Regularization was its training algorithm (R-squared=0.62, RMSE=1.839 K).