The Influence of Block Morphology on Urban Thermal Environment Analysis Based on a Feed-Forward Neural Network Model

Abstract

Morphological indicators, which are important for urban planning, can be adjusted to effectively mitigate the heat island effect and promote a more comfortable urban environment. Most studies obtain the relationship between morphological indicators and land surface temperature (LST) from the urban scale, and it is difficult to apply the results to urban management and construction projects. Traditional research methods have ignored the complex and interactive relationship between morphological indicators and LST. In this work, the feed-forward neural network (FNN) model is utilized to model the nonlinear relationship between morphological indicators and LST at the block scale. After validation and comparison, the FNN model achieved MAE of 0.885 and RMSE of 1.184, indicating that the influence of morphological indicators on LST could be precisely mapped. In addition, using cooling LST as the optimization target, the specific indicator scheme is suggested based on the FNN model, where the percentage of green space is 17.1%, the percentage of impervious surface is 82.9%, the percentage of water is 0, the bare soil percentage is 0, the floor area ratio is 0.814, the building cover percentage is 32.2%, and the average building height is 7.2 m.