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

Informações sobre recurso

Date of publication: 
Maio 2019
Resource Language: 
ISBN / Resource ID: 
OSF_preprint:4617E-378-69E

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.

Autores e editores

Author(s), editor(s), contributor(s): 

S M Labib

Provedor de dados

Center for Open Science

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