Simulation of green roofs and their potential mitigating effects on the urban heat island using an artificial neural network: A case study in Austin, Texas

The growing number of densely populated cities has resulted in a decrease in vegetation coverage, which in turn, has resulted in a temperature increase in urban areas. This phenomenon is known as urban heat island (UHI). Several strategies have been proposed to mitigate the effect of urban heat islands in recent years, including usage of green roof coverage. However, there is a need for a rigorous analysis of the relationship between UHI and different urban characteristics using advanced models for urban planners to make policy-decisions to mitigate the UHI effect. In this study, the cooling effect of the green roof strategy in the city of Austin, considering 2D/3D urban characteristic parameters was investigated. To begin with, land surface temperature (LST) was estimated from Landsat 8 TIRS data in July 2016. Also, 3D urban morphology parameters derived from light detection and ranging (LiDAR) data. To simulate the green roof strategy, Sentinel 2A satellite images were used to calculate the normalized difference vegetation index and the normalized difference built-up index because of higher spatial resolution than Landsat 8 OLI. Then a multilayer feed-forward neural network was applied as a nonlinear model to find a relationship between LST and various urban characteristic parameters simultaneously. Furthermore, the importance of the variables in LST modeling was evaluated using sensitivity analysis. After that, some downtown residential and office buildings, which had the potential to become a green roof, were selected to implement the green roof strategy. Finally, by analyzing the relationship between LST reduction due to green roof simulation and urban indicators, the best buildings for green roof implementation were determined. Results showed that the accuracy of the LST modeling was reached to R-2 = 0.786 and RMSE = 0.956 degrees C. In addition, by greening 3.2% of total building roofs, the average of LST decreased by 1.96 degrees C. Moreover, the results indicated that the building green roofs with (i) heights of 15-25 m, (ii) the highest values of sky view factor and solar radiation, and (iii) the lowest distance to the water body, had the greatest cooling effects on LST. Consequently, these findings indicated that the green roof has a significant effect on tem-perature reduction, especially by selecting the buildings with the above mentioned characteristics. (c) 2020 COSPAR. Published by Elsevier Ltd. All rights reserved.