A Review on the Applications of Artificial Neural Networks (ANNS) in Large-Scale Evaluation of Urban Environment
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.
Keywords - Artificial Intelligence, Artificial Neural Networks (ANNs), Machine learning, urban environment, Settlement