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

Labib, S.M.

doi:10.1016/j.envsoft.2019.05.006

Cited by 20 publications

(13 citation statements)

References 87 publications

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“…However, a questionnaire survey is needed in such studies to calculate driving forces weights which are usually very subjective since AHP depends on subjective expert opinions. There is a limitation that when using AHP the results might not be repeatable, especially if the experts are changed each time ( Cannemi et al., 2014 ; Labib, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…The city has 24 districts and it covers about 324 km 2 . Irbid is witnessing an increase in the population and urban growth over the past nine years due to the regional instability and the flow of Syrian refugees (Al-Kofahi et al, 2018, 2019. Due to the lack of restrictions and the absence of preserved land, many periphery villages are absorbed within Irbid city agglomeration.…”

Section: Study Areamentioning

confidence: 99%

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Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model

Gharaibeh

Shaamala

Obeidat

et al. 2020

Heliyon

173

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Urban growth and land-use change are a few of many puzzling factors affecting our future cities. Creating a precise simulation for future land change is a challenging process that requires temporal and spatial modeling. Many recent studies developed and trained models to predict urban expansion patterns using Artificial Intelligence (AI). This study aims to enhance the simulation capability of Cellular Automata Markov Chain (CA-MC) model in predicting changes in land-use. This study integrates the Artificial Neural Network (ANN) into CA-MC to incorporate several driving forces that highly impact land-use change. The research utilizes different socio-economic, spatial, and environmental variables (slope, distance to road, distance to urban centers, distance to commercial, density, elevation, and land fertility) to generate potential transition maps using ANN Data-driven model. The generated maps are fed to CA-MC as additional inputs. We calibrated the original CA-MC and our models for 2015 cross-comparing simulated maps and actual maps obtained for Irbid city, Jordan in 2015. Validation of our model was assessed and compared to the CA-MC model using Kappa indices including the agreement in terms of quantity and location. The results elucidated that our model with an accuracy of 90.04% substantially outperforms CA-MC (86.29%) model. The improvement we obtained from integrating ANN with CA-MC suggested that the influence imposed by the driving force was necessary to be taken into account for more accurate prediction. In addition to the improved model prediction, the predicted maps of Irbid for the years 2021 and 2027 will guide local authorities in the development of management strategies that balance urban expansion and protect agricultural regions. This will play a vital role in sustaining Jordan's food security.

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Section: Discussionmentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model

Gharaibeh

Shaamala

Obeidat

et al. 2020

Heliyon

173

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“…For example, a drop-off in influence might be seen due to the relative importance of the immediate proximity of peoples' homes for exposure to environmental hazards, as indicators of the areas experienced most frequently, whether for passive or active uses, and/or as a marker of what gives a place a sense of belonging (Xie et al, 2018). This sort of spatial relationship might lead itself to representation through the use of kernels, weighted functions or fuzzy logic (Malczewski, 2006;Boroushaki and Malczewski, 2008;Labib, 2019). Although there are examples of the use of distance decays (Egorov et al, 2017;Requia et al, 2016) there is yet little evidence of the use of such approaches in the literature.…”

Section: Spatial Scale and Geographic Exposure Areamentioning

confidence: 99%

Spatial dimensions of the influence of urban green-blue spaces on human health: A systematic review

Labib

Lindley

Huck

2020

Environmental Research

Self Cite

324

163

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“…Charoenkit and Piyathamrongchai developed a framework for the comparative assessment of multifunctionality among different urban ecosystem structures [30]. A previous study evaluated a machine learning approach using artificial neural network (ANN), adaptive network-based fuzzy inference system (ANFIS) algorithms, and statistical modeling to predict the probability of green or grey transformation for vacant places, poor condition sites, and waterway corridors [31]. Previously, the Storm Water Management Model (SWMM)-calibrated green infrastructure models were used to assess runoff reductions via different location strategies within the watershed, both spatially known and unknown [32].…”

Section: Introductionmentioning

confidence: 99%

Decision-Support System for LID Footprint Planning and Urban Runoff Mitigation in the Lower Rio Grande Valley of South Texas

Guerrero¹,

Alam

Mahmoud

et al. 2020

Sustainability

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To address regional flooding in the United States, federal and state agencies are adopting strict drainage policies in any large-scale commercial development within the watershed boundary. The conventional approach of implementing a wet detention pond (WP) reduces the land cover and causes operation and maintenance challenges eventually. The present study developed a decision-support system (DSS) in the Lower Rio Grande Valley region of South Texas for optimal selection of Best Management Practices (BMPs) by substituting a portion of the WP footprint with three regionally promising low-impact development practices, namely, porous concrete pavement (PCP), bioretention (BR), and bioswale (BS). Source Load Assessment and Management Model for Windows (WinSLAMM) was used as the foundation for the DSS database and algorithm development. This tool suggested that the implementation of bioswale alone can considerably reduce the footprint and construction cost. Less than 0.95 ha of installation of BR and BS can mitigate 79–91% of runoff from a maximum of 5 ha of commercial development. A combination of BR, BS, and WP was found to reduce runoff significantly (~100%), which suggests that the successful adoption of DSS might support better planning of the urban stormwater management in the Lower Rio Grande Valley (LRGV).

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

Cited by 20 publications

References 87 publications

Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model

Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model

Spatial dimensions of the influence of urban green-blue spaces on human health: A systematic review

Decision-Support System for LID Footprint Planning and Urban Runoff Mitigation in the Lower Rio Grande Valley of South Texas

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