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A neural network approach for modeling the Heat Island phenomenon in urban areas during the summer period
Author(s) -
Santamouris M.,
Mihalakakou G.,
Papanikolaou N.,
Asimakopoulos D. N.
Publication year - 1999
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/1998gl900316
Subject(s) - urban heat island , artificial neural network , meteorology , environmental science , air temperature , feedforward neural network , backpropagation , atmospheric temperature , urban area , intensity (physics) , remote sensing , computer science , geology , geography , machine learning , physics , quantum mechanics , economy , economics
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.