Investigation of the Likelihood of Green Infrastructure (GI) Enhancement along Linear Waterways or on Derelict Sites (DS) Using Machine Learning.

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S M Labib


Studies evaluating potential of Green Infrastructure (GI) development using traditional Boolean logic-based multi-criteria analysis methods are not capable of predicting future GI development under dynamic urban scape. This study evaluated robust soft-computing-based methods of artificial intelligence (Artificial Neural Network, Adaptive Neuro-Fuzzy Interface-System) and used statistical modelling (logistic regression) to predict GI or grey transformation likelihoods for vacant sites along waterway corridors (WWC) and derelict sites (DS) based on ecological, environmental, and social criteria. The study found that the ANN and ANFIS models had better predictive capacity and more accuracy (72% accurate) than logistic models (65% accurate). Site sizes, population coverage, and air pollution were identified as the main influencing factors regarding GI/GY transformation. Finally, for Manchester, the likelihood of GI transformation was higher for WWCs (80%) than for DS (60%), and DS were more likely to transform into GY based on current trends.



Artificial Intelligence and Robotics, Computer Sciences, Environmental Monitoring, Environmental Sciences, Geographic Information Sciences, Geography, Physical Sciences and Mathematics, Social and Behavioral Sciences, Spatial Science


machine learning, Artificial Neural Network, Green Infrastructure, Green Space, Urban Land Use


Published: 2019-05-08 07:13


GNU Lesser General Public License (LGPL) 2.1

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