One major limitation which currently exist with the study in urban environment is the often inconsistent and unreliable measures of urban development features across different field of studies which make large scale study difficult. The recent advances in new technologies have produced a wealth of data to help research activities by facilitating improved measurements and conducting large scale analysis. Additionally, according to the growing trend of world population the need for underground transportation is increasing. This research aimed to present the overall concept of Artificial Neural Networks (ANNs) and their role in statistical analysis part of two different but correlated field of study of Architecture and Urban planning engineering and Geotechnical Civil and transportation engineering. The advantage of machine learning in this project has focus on measuring the 1) role of ANNs on evaluation of street level urban qualities, and 2) urban underground transportation system infrastructures, in order to big data analysis in advance. The study explores the potential of big data management and big data analytics to measuring urban development features by applying different types of machine learning according to the aim of every research. Finally, the research specifically deals with optimization of time and human resource for evaluated of two correlated projects as well.
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|Titolo:||A REVIEW ON THE APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS (ANNs) IN LARGE-SCALE EVALUATION OF URBAN ENVIRONMENT|
RAMEZANSHIRAZI, MOHSEN (Corresponding author)
|Data di pubblicazione:||2017|
|Appartiene alla tipologia:||04d Abstract in atti di convegno|