Multi-criteria selection of an Air Quality Model configuration based on quantitative and linguistic evaluations

Over the past years, the health impact of airborne particulate matter PM 10 has become a very topical subject. Thereby, a lot of research effort in the environmental sciences goes towards the modeling and the prediction of ambient PM 10 concentrations. In this paper, we are interested in the statistical classification of the daily mean PM 10 concentration in Tunisia according to the authority regulation. We consider two monitoring stations: a big industrial station and a traffic station. The main goal of this work is to determine the pertinent predictors of PM 10 concentration within a nonlinear multiclass framework. To do this, we used two popular statistical learning methods; the support vector machines (SVM) and the random forests (RF). The statistical results obtained on the real datasets, show that RF outperform SVM for the purpose of variable selection even with a reduced number of observations compared to the number of explicative variables. It was also demonstrated that the PM 10 concentration measured yesterday is the most relevant predictor of its present-day value. Moreover, we found that the more delayed values of PM 10 concentration may be crucial to get an accurate prediction.

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“…Thus, accurate meteorological fields are of utmost importance. They will lead to reliable forecasts of air pollution events (Almanza et al 2014).…”

Section: Data Gathering and Descriptionmentioning

confidence: 99%

Analysis and prediction of $$\mathrm{PM}_{10}$$ PM 10 concentration levels in Tunisia using statistical learning approaches

2016

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“…Similarly, a novel conjunctive MCDM approach that combines AHP and TOPSIS order was employed to develop an innovation support system that considers the interdependence of higher education institutes to comprehensively evaluate their innovation performance [39]. Another study presented the application of multi-criteria evaluation in the selection of an optimal allocation for an air quality model [40], which was developed to support managers in exploring the strengths and weaknesses of each alternative. This approach identifies a preferred result based on the integration of Bayesian networks and fuzzy logic to rank and evaluate suppliers [41].…”

Section: Related Workmentioning

confidence: 99%

Integration Multi-Model to Evaluate the Impact of Surface Water Quality on City Sustainability: A Case from Maanshan City in China

2019

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Water pollution is a worldwide problem that needs to be solved urgently and has a significant impact on the efficiency of sustainable cities. The evaluation of water pollution is a Multiple Criteria Decision-Making (MCDM) problem and using a MCDM model can help control water pollution and protect human health. However, different evaluation methods may obtain different results. How to effectively coordinate them to obtain a consensus result is the main aim of this work. The purpose of this article is to develop an ensemble learning evaluation method based on the concept of water quality to help policy-makers better evaluate surface water quality. A valid application is conducted to illustrate the use of the model for the surface water quality evaluation problem, thus demonstrating the effectiveness and feasibility of the proposed model.

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“…TOPSIS was used to evaluate sustainable development in livable city [21], and the results suggested that the social, economic, and environmental factors have important influences on liveable cities. Reference [22] presented a multicriteria evaluation for the selection of the optimal configuration of an air quality model. Additionally, some scholars developed support methods to explore the strengths and weaknesses of various alternatives and identified the preferred result based on the integration of Bayesian networks and fuzzy logic to rank and evaluate suppliers [23].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Sustainable Liveable City via Multi-MCDM and Hopfield Neural Network

Chen

2020

Mathematical Problems in Engineering

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Research on liveable cities has received increased attention in recent years because of the complexity and diversity of liveability standards. Evaluating the liveable environment is a multiple criteria decision-making (MCDM) problem, and the results can be used to control environmental pollution and protect human health. However, different evaluation methods can lead to different results; hence, determining how to effectively to obtain the consistent results is a main consideration of this study. The objective of this work is to design an optimal method based on the difference ratio concept. A hopfield neural network is selected to validate the experimental results. Referring to the liveable city rankings established by the Economist Intelligence Unit, thirteen large cities in China are used to illustrate the application of the model, evaluate the liveable urban environment, and demonstrate the effectiveness and feasibility of the proposed model. The results show that Hangzhou is the most liveable city and Beijing has the worst liveable urban environment. Therefore, a common policy should be strengthening environmental governance, with a special focus on the development of low-carbon cities, for which both the local and global environmental impacts could be mitigated.

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Multi-criteria selection of an Air Quality Model configuration based on quantitative and linguistic evaluations

Cited by 5 publications

References 38 publications

Analysis and prediction of $$\mathrm{PM}_{10}$$ PM 10 concentration levels in Tunisia using statistical learning approaches

Analysis and prediction of $$\mathrm{PM}_{10}$$ PM 10 concentration levels in Tunisia using statistical learning approaches

Integration Multi-Model to Evaluate the Impact of Surface Water Quality on City Sustainability: A Case from Maanshan City in China

Evaluating Sustainable Liveable City via Multi-MCDM and Hopfield Neural Network

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