Investigation of the likelihood of green infrastructure (GI) enhancement along linear waterways or on derelict sites (DS) using machine learning. (August 2019)

• Record Type: Journal Article
• Authors: Labib, S.M.
• Is Part Of: Environmental modelling & software. Volume 118(2019)
• Page Start: 146
• Page End: 165
• Publication Date: 2019-08
• Subjects: Green infrastructure -- Urban land use -- Green space -- Machine learning -- Artificial neural network (ANN); Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.05.006
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Studies evaluating the potential for green infrastructure (GI) development using traditional Boolean logic-based multi-criteria analysis methods are not capable of predicting future GI development in dynamic urbanscapes. This study evaluated both artificial neural network (ANN) and adaptive, network-based fuzzy inference system (ANFIS) algorithms in conjunction with statistical modelling to predict green or grey transformation likelihoods for derelict sites (DS) and vacant sites along waterway corridors (WWC) in Manchester based on ecological, environmental, and social criteria. The soft-computing algorithms had better predictive capacity at 72% accuracy versus the 65% of logistic models. Site sizes, population coverage, and air pollution were identified as the main influencers in the potential for site transformation. In Manchester, the likelihood of GI transformation was higher for WWC than derelict sites at 80% versus 60% likelihood, respectively. Furthermore, DS were more likely to transform into grey development based on current trends and urban planning practice. Graphical abstract: Image 1 Highlights: GIS & Machine learning were applied to modelling future green infrastructure trends. Machine learning modelling was more robust and accurate than logistic regression. Green infrastructure development was more prevalent along waterways. Derelict sites were more likely to redevelop as grey infrastructure.