A comparative analysis of Statistical and Computational Intelligence methodologies for the prediction of traffic-induced fine particulate matter and NO2

Kokkinos, Konstantinos; Karayannis, Vayos; Nathanail, Eftihia; Μουστάκας, Κωνσταντίνος

doi:10.1016/j.jclepro.2021.129500

Cited by 15 publications

(6 citation statements)

References 40 publications

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“…Those results suggest that images may capture similar features as traditional GIS predictors while also allowing for the inclusion of higher-resolution (i.e., street-level) features that traditional GIS variables lack . Another notable trend in recent air quality modeling studies is the adoption of advanced machine learning approaches to improve model performance, including random forest, − gradient boosting, , artificial neural network, − and hybrid algorithms. , …”

Section: Introductionmentioning

confidence: 96%

National Land Use Regression Model for NO₂ Using Street View Imagery and Satellite Observations

Dixit

Marshall

et al. 2022

Environ. Sci. Technol.

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Land use regression (LUR) models are widely applied to estimate intra-urban air pollution concentrations. National-scale LURs typically employ predictors from multiple curated geodatabases at neighborhood scales. In this study, we instead developed national NO 2 models relying on innovative street-level predictors extracted from Google Street View [GSV] imagery. Using machine learning (random forest), we developed two types of models: (1) GSV-only models, which use only GSV features, and (2) GSV + OMI models, which also include satellite observations of NO 2 . Our results suggest that street view imagery alone may provide sufficient information to explain NO 2 variation. Satellite observations can improve model performance, but the contribution decreases as more images are available. Random 10fold cross-validation R 2 of our best models were 0.88 (GSV-only) and 0.91 (GSV + OMI)�a performance that is comparable to traditional LUR approaches. Importantly, our models show that street-level features might have the potential to better capture intraurban variation of NO 2 pollution than traditional LUR. Collectively, our findings indicate that street view image-based modeling has great potential for building large-scale air quality models under a unified framework. Toward that goal, we describe a cost-effective image sampling strategy for future studies based on a systematic evaluation of image availability and model performance.

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Section: Introductionmentioning

confidence: 96%

National Land Use Regression Model for NO₂ Using Street View Imagery and Satellite Observations

Dixit

Marshall

et al. 2022

Environ. Sci. Technol.

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“…Beneficial and effective biomedical waste treatment technologies generate byproducts that can be further utilized as useful inputs in other industries, reinforcing the circular economy concept [48][49][50].…”

Section: Energy and Other Resource Utilizationmentioning

confidence: 97%

Sustainable Medical Waste Management Using an Intuitionistic Fuzzy-Based Decision Support System

Kokkinos,

Lakioti,

Moustakas

et al. 2023

Sustainability

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The growing urban population and increased use of healthcare services have brought significant attention to the safe and sustainable management of medical waste. Selecting the proper technology in medical waste management (MWM) represents one of the most critical challenges for decision-makers to ensure public health. In order to evaluate and choose the best MWM methodology, the current research provides a novel multi-criteria decision-making (MCDM) strategy for a variety of social stakeholders, to compute criteria weights, decision-making weights, and alternative ranking algorithms. The suggested structure addresses uncertain assessments of alternatives by extending weighting and ranking methods to acquire the decision-making weight and rank the MWM alternatives based on uncertain conditions. It also uses ‘intuitionistic fuzzy’ linguistic variables to indicate criteria weights. To assess all the factors pertaining to the sustainability of MWM actions, this study suggests the creation of a decision support system (DSS). Our DSS is built upon a novel strategy that utilizes a collection of MCDM models that are grounded on contemporary intuitionistic fuzzy logic methodologies. Alternative scenarios have been assessed for the instance of Greece, after specialists in the healthcare management field imposed 17 criteria and sub-criteria. The IF-MCDM methodologies used were the Intuitionistic Fuzzy DEMATEL, TOPSIS, and CORPAS. The alternative scenarios ranged from the prioritizing of safety laws and regulations to public acceptance and awareness, with the handling of hazardous risks and transportation playing a crucial part in the process. All ensemble methods produced the same ranking of the alternatives, demonstrating that safety and risk avoidance is the most significant scenario for sustainable urban development and public health.

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“…According to [22,26,27], the number of specialists may vary depending on the specific renewable energy transition occurring at the time. However, according to studies on how fuzzy cognitive mapping (FCM) learning is structured, a minimum of seven experts are needed [28][29][30][31].…”

Section: Development Of a Dss For The Transition To Co 2 -Minimized U...mentioning

confidence: 99%

A Fuzzy Cognitive Map and PESTEL-Based Approach to Mitigate CO2 Urban Mobility: The Case of Larissa, Greece

Kokkinos

Nathanail

2023

Sustainability

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The CO2 reduction promise must be widely adopted if governments are to decrease future emissions and alter the trajectory of urban mobility. However, from a long-term perspective, the strategic vision of CO2 mitigation is driven by inherent uncertainty and unanticipated volatility. As these issues emerge, they have a considerable impact on the future trends produced by a number of exogenous and endogenous factors, including Political, Economic, Social, Technological, Environmental, and Legal aspects (PESTEL). This study’s goal is to identify, categorize, and analyze major PESTEL factors that have an impact on the dynamics of urban mobility in a rapidly changing environment. For the example scenario of the city of Larissa, Greece, a Fuzzy Cognitive Map (FCM) approach was employed to examine the dynamic interactions and behaviors of the connected criteria from the previous PESTEL categories. An integrative strategy that evaluates the interaction of linguistic evaluations in the FCM is used to include all stakeholders in the creation of a Decision Support System (DSS). The methodology eliminates the uncertainty brought on by a dearth of quantitative data. The scenarios in the study strands highlight how urbanization’s effects on sustainable urban transportation and the emergence of urban PESTEL actors impact on CO2 reduction decision-making. We focus on the use case of Larissa, Greece (the city of the CIVITAS program), which began putting its sustainable urban development plan into practice in 2015. The proposed decision-making tool uses analytics and optimization algorithms to point responsible authorities and decision-makers in the direction of Larissa’s sustainable urban mobility and eventually the decarbonization of the urban and suburban regions.

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A comparative analysis of Statistical and Computational Intelligence methodologies for the prediction of traffic-induced fine particulate matter and NO2

Cited by 15 publications

References 40 publications

National Land Use Regression Model for NO₂ Using Street View Imagery and Satellite Observations

National Land Use Regression Model for NO₂ Using Street View Imagery and Satellite Observations

Sustainable Medical Waste Management Using an Intuitionistic Fuzzy-Based Decision Support System

A Fuzzy Cognitive Map and PESTEL-Based Approach to Mitigate CO2 Urban Mobility: The Case of Larissa, Greece

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A comparative analysis of Statistical and Computational Intelligence methodologies for the prediction of traffic-induced fine particulate matter and NO2

Cited by 15 publications

References 40 publications

National Land Use Regression Model for NO2 Using Street View Imagery and Satellite Observations

National Land Use Regression Model for NO2 Using Street View Imagery and Satellite Observations

Sustainable Medical Waste Management Using an Intuitionistic Fuzzy-Based Decision Support System

A Fuzzy Cognitive Map and PESTEL-Based Approach to Mitigate CO2 Urban Mobility: The Case of Larissa, Greece

Contact Info

Product

Resources

About

National Land Use Regression Model for NO₂ Using Street View Imagery and Satellite Observations

National Land Use Regression Model for NO₂ Using Street View Imagery and Satellite Observations