A neural network approach for modeling the Heat Island phenomenon in urban areas during the summer period
A neural network approach for modeling the Heat Island phenomenon in urban areas during the summer period
复制标题
用于模拟夏季城市地区热岛现象的神经网络方法
DOI：
10.1029/1998gl900316
复制
发表时间：
1999
影响因子：
5.2
通讯作者：
D. Asimakopoulos
中科院分区：
文献类型：
--
作者：
M. Santamouris;G. Mihalakakou;N. Papanikolaou;D. Asimakopoulos
在雅典主要地区的20个站点记录了夏季期间的环境温度数据，调查了城市中环境温度的分布和城市热岛强度。设计了一种基于预测或记录的小时值的神经网络方法，用于对每个站点的气温进行建模、预测和估计。设计和训练了多种基于BP算法的前馈、多层、神经网络结构，用于站点的温度预报和估计。使用广泛的测量集合对结果进行了检验，发现它们与实际值很好地吻合。此外，每个被估计的站点被用作估计下一个站点的温度的输入。将模拟结果与实测数据进行了比较，结果表明，神经网络方法能够较好地模拟大城市地区夏季多个地点的城市温度场。
The distribution of ambient air temperature in a city and the urban heat island intensity are investigated during the summer period in the major Athens region where ambient air temperature data are recorded at twenty stations. A neural network approach, based on predicted or recorded hourly values, is designed for modeling, predicting and estimating the air temperature at each station. Various feedforward, multiple layered, neural network architectures based on backpropagation algorithm are designed and trained for the stations' temperature prediction and estimation. The results were tested using extensive sets of measurements and it was found that they correspond well with the actual values. Furthermore, each one of the estimated stations is used as one input for the estimation of the next station's temperatures. The results were compared with the measured data and the neural network method was found able to simulate with sufficient accuracy the urban temperature field at several locations in a large urban region during the summer.