1. Introduction
The world is urbanizing faster and 68% of the population is expected to live in cities by 2050 [
1]. The increased urbanization of the population, the urban heat island effect, and the effects of global warming have resulted in a rise in urban temperature [
2,
3,
4]. According to research findings, it is estimated that the temperature increase will reach a peak of 1.5 °C between 2030 and 2050 [
5]. Therefore, heatstroke is becoming a growing concern in cities. In Tokyo, ambulance evacuations due to heatstroke were six times greater than in previous years, and the number of patients suffering from heatstroke has been growing since 2010 [
6], a trend that extends to other cities in Japan, including Sendai, roughly 300 km from Tokyo [
7]. Therefore, urban warming is now becoming a serious problem, dramatically affecting outdoor thermal comfort and significantly increasing the risk of heatstroke for pedestrians [
8]. To mitigate urban warming and adapt to it, effectively evaluating the urban thermal environment is the focus of current research, which will decrease human exposure to heat and the possibility of heat-related illness [
9].
A growing body of literature [
9,
10,
11] has evaluated the impact of urban warming using standard effective temperature (SET*), which is used to assess adaptation to urban warming. As one of the key factors in calculating SET*, the MRT is the most important index that characterizes the effects of the thermal radiant environment on human thermal comfort [
12,
13]. When compared to standard meteorological indices such as air temperature, Thorsson et al. [
14] propose that MRT might be a more appropriate indicator for evaluating intra-urban variances in thermal comfort conditions, particularly in a complex urban setting. So, the MRT has been extensively used in urban human-biometeorological investigations across the globe to parameterize levels of thermal comfort and heat stress during harsh weather [
15,
16,
17].
Indeed, numerous research [
18,
19,
20,
21,
22] indicates that climatic and building form elements such as solar location, date, sunlight duration, mean air temperature, and cloud cover may have an effect on the MRT. Shahrukh Anis et al. [
23] found that sunshine duration could affect global solar radiation and further impact MRT and that the morphology of urban canyons will influence sunshine duration. Lindberg et al. [
24] revealed that the shadows of buildings have the potential to limit incoming shortwave radiation and hence MRT, which is critical for MRT distribution. In recent years, several studies [
25,
26,
27] have paid more attention to the sky view factor (SVF), which is an indirect representation of the built morphology; it has been demonstrated to have a significant impact on MRT and outdoor thermal environments. Dogan [
28] indicated that MRT is impacted by the surface temperature of the adjacent urban features, particularly for pedestrians, and that the ground temperature has a stronger effect on MRT distribution than others [
29]. Although the analysis of the parameters affecting MRT has focused mainly on weather or building parameters in recent years, if building-related parameters (SVF, building shadow, surface temperature, sunshine duration) are considered together with weather data in different urban morphologies, the order of importance of the various parameters to MRT in urban spaces can be quickly determined. Due to the difficulty of coping with the changing weather conditions, optimization solutions for building-related factors might be recommended for outdoor environment improvements.
Typically, sensitivity analysis approaches are employed to investigate the order of importance. However, there are two significant limits: (1) sensitivity analysis is used to evaluate the change of dependent variables when one of the independent variables is changed; in order to evaluate the order of importance of the parameters for MRT, a method for quickly and accurately calculating the MRT by the parameters is required. (2) There are numerous sensitivity analysis approaches, and it is vital to select one that is compatible with the quick calculation approach. Therefore, there is a need to develop a sensitivity analysis technique that is compatible with the quick calculation approach. Therefore, an appropriate prediction approach and sensitivity analysis approach for the prediction approach are critical for addressing these two concerns.
For prediction approaches, many studies have successively proposed different models that obtain weather data to make predictions [
30]. Roman et al. [
30] analyzed the application of different prediction methods, including artificial neural networks (ANN) [
31,
32,
33], Gaussian process [
34], polynomial regression [
35], support vector machine [
36], and linear regression [
37] prediction methods, in building performance simulations by performing a literature review. According to the authors, the most extensively used approaches in building performance assessment are as follows: (accounting for 33%) is ANN, followed by polynomial regression methods (constituting 22.6%). Moreover, ANN methods are advantageous in some other fields due to their nonlinear mapping ability and favorable prediction data-processing ability, and many related building performance simulation studies have been performed over the past decade.
At present, some other ANNs have been utilized to overcome these problems. Kumar et al. [
38] presented an energy analysis with the use of ANNs. Among those mentioned, a backpropagation neural network (BPNN), using a backpropagation algorithm, an enhanced version of the feedforward neural network (FFNN), was shown to be better at evaluating, estimating, and predicting a big dataset when compared to statistical approaches such as the least-squares method. Afterward, Wang et al. [
39] determined that the BP algorithm can be used with a linear ANN, which is a gradient descent algorithm that is widely utilized to train ANNs. Mohandes et al. [
40] indicated that neural networks are the most extensively employed kind of network for prediction, in particular, the MLNN [
41] and recurrent neural network (RNN). RNN has become one of the most popular neural networks in recent years, nonetheless, it is only useful for continuous-time prediction and data preparation [
42].
However, BPNNs are limited in their ability to solve specific situations. Due to the fact that a BPNN corrects network connection weights using the root mean square error (RMSE) and gradient descent technique, unavoidable issues such as slipping into local minima, sluggish convergence speed, and overfitting may exist [
43]. Kolhe et al. [
44] used a hybrid prediction model that included a genetic algorithm (GA) and a backpropagation neural network (BPNN) to predict wind energy. Although the BPNN worked well, combining it with a GA resulted in more efficient and accurate outcomes. Then, Ata et al. and Meukam et al. [
45,
46] revealed that: (1) In regions including windy environments, the most often-utilized ANN is the MLNN; (2) Backpropagation algorithms seem to be the most frequently used algorithms for MLPNN training in wind energy conversion systems; and (3) GAs are among the most frequently used optimization algorithms for optimizing BPNNs. According to Zhu et al. [
47], the backpropagation neural network model enhanced by the genetic algorithm (GA-BPNN) has greater accuracy than the BPNN method but a slower computation time. Consequently, the MLNN has higher accuracy than the one-hidden-layer FFNN. Therefore, the MLNN optimized by the GA and BP algorithm (MLNN-GABP) was selected to compare the prediction results of the MRT with the MLNN. The more accurate one could be used for sensitivity analysis.
In previous studies, numerous approaches have been presented to determine the relative relevance of ANN variables, such as the linkage weight method approach [
48], the Garson algorithm approach [
49], the partial derivative approach [
50,
51], and the sensitivity analysis approach [
52]. Nonetheless, there is no unanimity due to the disparate priority assigned to the individual variables because the approach determines the relative relevance of the individual variable’s method of sensitivity analysis [
53]. As a result, a proper sensitivity analysis technique is required. Chan et al. [
54] predicted outdoor thermal comfort using an MLNN with two hidden layers. For the summertime prediction, 12,500 training datasets were utilized and the R-value was 0.898, indicating its high accuracy. This study employs an MLNN that is capable of handling large amounts of data and achieving high accuracy and combines a biological and a weather-related metric to predict the predicted mean vote (PMV) and SET*, achieving both high prediction accuracy and ranking. According to this study, the sensitivity analysis approach was proven to be suitable for ANN. Therefore, the sensitivity analysis approach was chosen as the conference for analyzing the important variables for MRT in this study.
In this work, ANNs were used to predict the MRT distribution, and data were obtained every day in the summer from 2014 to 2019 in Sendai at noon true solar time (when the sun is at its greatest height and solar radiation is at its strongest). The main objective of this work is to develop MLNN-GABP models to predict the distribution of MRT at 1.5 m (pedestrian height) of the surrounding buildings. It was important to gather training and validation datasets for the ANN. The meteorological datasets from 2014 to 2018 were used as the inputs, whereas the MRT results calculated using the same weather dataset’s settings were used as the target outputs. The validation approach used the 2019 meteorological datasets as the inputs for the trained neural network and the MRT results generated under the same weather dataset’s settings as the validation outputs. The MRT prediction model was developed using a set of simplified-shape buildings in Sendai, Japan (38°16′06″ N, 140°52′10″ E).
The findings given in this paper are expected to aid in the development of a viable strategy for predicting MRT distributions without requiring extensive calculations. Urban planners and designers must provide pertinent recommendations and design measures based on optimal outdoor thermal environments.
6. Conclusions
Using meteorological data and artificial neural networks (ANNs), we developed a unique technique of MRT prediction for a reduced block construction model. In addition, we compared the results of the ANN predictions with the results of the heat balance analysis simulations conducted during the years 2014–2018, as well as for the year 2019, which were not included in the training datasets but were acquired from other sources. The following are the findings and ramifications of this study.
We selected the weather parameters (solar altitude, solar direction, air temperature at noon true solar time, minimum air temperature, and global solar radiation) and the related parameters in each mesh (the sky view factor of each grid, sunshine duration before noon true solar time, the shadow ration in each grid at noon true solar time, and ground surface temperature) as the input parameters for the training datasets, which proved suitable for the MRT distribution prediction under a particular building form.
The article optimized the MLNN using a BP algorithm and GA; the optimization results indicated that the GA and BP algorithm have some effect on the results. The neural network without optimization produces more deviant prediction values, whereas the optimized neural network produces more stable prediction values when dealing with a large matrix.
An MLNN-GABP model was created to derive the MRT values from the weather variables during a specified period. After five years of training, it is possible to forecast the MRT distribution for the following year. The prediction results are extremely accurate, and a comparison of the MRT distribution plots reveals that they are consistent. As a result, the influence of this input parameter on the output can be analyzed using sensitivity analysis, and a relationship between the grid-related indicators and the MRT distribution can be determined.
Still, the proposed method has limitations. First, the MRT was only predicted using meteorology data for Sendai (38°16′06″ N, 140°52′10″ E), considering simplified building shapes, and the method only proposes an algorithm; further verifications are expected for realistic urban blocks in future works. It has not been validated in this study whether this method is applicable if other parameters are considered, such as trees, water, etc. Meanwhile, it is also important to consider wind patterns and non-realistic building arrangements. Therefore, further analysis will need to be conducted in subsequent research to address this issue. Second, the developed prediction method was only confirmed to predict the simulated results; it should be validated if this method can predict the MRT in real buildings. Finally, although this prediction has very high accuracy, it requires a large number of training datasets. Therefore, it would be very difficult to obtain five-year training data if this method were scheduled to be used in a real building environment and measured in the future.