Estimating construction waste generation in the Greater Bay Area, China using machine learning

Tang

et al. 2022

Transactions in GIS

As the most prevalent physical activity and transportation mode for older people, walking is considered to have multiple health and well‐being benefits. Previous studies used separate models to assess the built‐environment determinants of a battery of walking behavior measures, such as walking frequency and duration. In a departure from them, this study develops a system of equations, which is estimated by seemingly unrelated regression, to determine the built‐environment factors that significantly influence two correlated walking behavior measures (including walking frequency and duration) of older adults in Xiamen (a medium‐sized Chinese city) based on data from the Travel Survey of Xiamen Residents 2015 and built‐environment geo‐data. The results show the following: (1) the walking frequency and duration of older adults are affected by the built environment and socio‐demographic characteristics; (2) land‐use mix, intersection density, and bus route density positively influence older adults’ walking frequency and duration; (3) distance to the commercial center adversely impacts the walking frequency and duration; and (4) the built environment has similar effects on the two measures. This study offers a worthwhile reference for policy intervention to promote older adults' walking activities, thereby contributing to active and healthy aging.

Section: Discussionmentioning

confidence: 99%

Using a system of equations to assess the determinants of the walking behavior of older adults

Tang

et al. 2022

Transactions in GIS

“…In this regard, multi-criteria decision-making (MCDM) (Maghsoodi et al, 2018;Simic et al, 2021;Torkayesh, Malmir, et al, 2021) and machine learning algorithms (Bagheri et al, 2019;Chhay et al, 2018;Lu et al, 2021;Maghsoodi et al, 2020) are reliable tools that can unchain managers to tackle real-life multi-dimensional decision-making problems with a large number of decision criteria. Thus, this study develops a novel decision support system based on a multi-stage model that hybridizes the random forest recursive feature elimination (RF-RFE) algorithm, the indifference threshold-based attribute ratio analysis (ITARA), and the measurement of alternatives and ranking according to compromise solution (MARCOS) methods into a unique framework under the Fermatean fuzzy environment.…”

Section: Introductionmentioning

confidence: 99%

Locating a disinfection facility for hazardous healthcare waste in the COVID-19 era: a novel approach based on Fermatean fuzzy ITARA-MARCOS and random forest recursive feature elimination algorithm

2022

Hazardous healthcare waste (HCW) management system is one of the most critical urban systems affected by the COVID-19 pandemic due to the increase in waste generation rate in hospitals and medical centers dealing with infected patients as well as the degree of hazardousness of generated waste due to exposure to the virus. In this regard, waste network flow would face severe problems without taking care of hazardous waste through disinfection facilities. For this purpose, this study aims to develop an advanced decision support system based on a multi-stage model that was combined with the random forest recursive feature elimination (RF-RFE) algorithm, the indifference threshold-based attribute ratio analysis (ITARA), and measurement of alternatives and ranking according to compromise solution (MARCOS) methods into a unique framework under the Fermatean fuzzy environment. In the first stage, the innovative Fermatean fuzzy RF-RFE algorithm extracts core criteria from a finite set of initial criteria. In the second stage, the novel Fermatean fuzzy ITARA determines the semi-objective importance of the core criteria. In the third stage, the new Fermatean fuzzy MARCOS method ranks alternatives. A real-life case study in Istanbul, Turkey, illustrates the applicability of the introduced methodology. Our empirical findings indicate that "Pendik" is the best among five candidate locations for sitting a new disinfection facility for hazardous HCW in Istanbul. The sensitivity and comparative analyses confirmed that our approach is highly robust and reliable. This approach could be used to tackle other B Vladimir Simic

“…Over the past few years, as urbanization is accelerating, the amount of construction wastes has also been on the rise. According to statistics, construction wastes account for 40% of urban wastes, which poses prominent threats to the ecological environment [ 1 ]. Against such a background, more attention has been paid to recycling, the best approach for construction waste disposal.…”

Section: Introductionmentioning

confidence: 99%

Research on the Governance Relationship among Stakeholders of Construction Waste Recycling Based on ANP-SNA

Si-ling

Qin

Huang

2022

IJERPH

A method based on Analytic Network Process and Social Network Analysis (ANP-SNA) was proposed in this paper to determine and better clarify the governance relationship among various stakeholders involved. Firstly, fourteen stakeholders of construction waste recycling were identified using the snowball sampling method, and the governance relationships of these stakeholders were summarized into four aspects with eight indicators. Secondly, the weights of the stakeholder governance relationship indicators were determined based on Analytic Network Process (ANP). Thirdly, the Social Network Analysis (SNA) method was used to model the governance relationship network of the stakeholders, and the governance relationships among different stakeholders in the network were described by quantitative analysis of network cohesion, network centrality, structural holes, and other indicators. Finally, key points for optimizing the governance relationships among stakeholders of construction waste recycling were proposed in this paper, so as to provide a new solution for the collaborative governance of stakeholders.