IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i10p3730-d362633.html
   My bibliography  Save this article

Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

Author

Listed:
  • Sina Shaffiee Haghshenas

    (Department of Civil Engineering, University of Calabria, 87036 Rende, Italy)

  • Behrouz Pirouz

    (Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende, Italy)

  • Sami Shaffiee Haghshenas

    (Department of Civil Engineering, University of Calabria, 87036 Rende, Italy)

  • Behzad Pirouz

    (Department of Computer Engineering, Modelling, Electronics and Systems Engineering, University of Calabria, 87036 Rende, Italy)

  • Patrizia Piro

    (Department of Civil Engineering, University of Calabria, 87036 Rende, Italy)

  • Kyoung-Sae Na

    (Gil Medical Center, Gachon University, Incheon 21565, Korea)

  • Seo-Eun Cho

    (Gil Medical Center, Gachon University, Incheon 21565, Korea)

  • Zong Woo Geem

    (College of IT Convergence, Gachon University, Seongnam 13120, Korea)

Abstract

Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

Suggested Citation

  • Sina Shaffiee Haghshenas & Behrouz Pirouz & Sami Shaffiee Haghshenas & Behzad Pirouz & Patrizia Piro & Kyoung-Sae Na & Seo-Eun Cho & Zong Woo Geem, 2020. "Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications," IJERPH, MDPI, vol. 17(10), pages 1-21, May.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:10:p:3730-:d:362633
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/10/3730/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/17/10/3730/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Lin & He, Jing & Wu, Desheng & Zeng, Yu-Rong, 2012. "A novel differential evolution algorithm for joint replenishment problem under interdependence and its application," International Journal of Production Economics, Elsevier, vol. 135(1), pages 190-198.
    2. Behrouz Pirouz & Sina Shaffiee Haghshenas & Behzad Pirouz & Sami Shaffiee Haghshenas & Patrizia Piro, 2020. "Development of an Assessment Method for Investigating the Impact of Climate and Urban Parameters in Confirmed Cases of COVID-19: A New Challenge in Sustainable Development," IJERPH, MDPI, vol. 17(8), pages 1-17, April.
    3. Geem, Zong Woo, 2011. "Transport energy demand modeling of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 39(8), pages 4644-4650, August.
    4. Mario Maiolo & Behrouz Pirouz & Roberto Bruno & Stefania Anna Palermo & Natale Arcuri & Patrizia Piro, 2020. "The Role of the Extensive Green Roofs on Decreasing Building Energy Consumption in the Mediterranean Climate," Sustainability, MDPI, vol. 12(1), pages 1-13, January.
    5. Behrouz Pirouz & Natale Arcuri & Behzad Pirouz & Stefania Anna Palermo & Michele Turco & Mario Maiolo, 2020. "Development of an Assessment Method for Evaluation of Sustainable Factories," Sustainability, MDPI, vol. 12(5), pages 1-15, February.
    6. Reza Mikaeil & Sina Shaffiee Haghshenas & Zoheir Sedaghati, 2019. "Geotechnical risk evaluation of tunneling projects using optimization techniques (case study: the second part of Emamzade Hashem tunnel)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(3), pages 1099-1113, July.
    7. Hao Yu & Xu Sun & Wei Deng Solvang & Xu Zhao, 2020. "Reverse Logistics Network Design for Effective Management of Medical Waste in Epidemic Outbreaks: Insights from the Coronavirus Disease 2019 (COVID-19) Outbreak in Wuhan (China)," IJERPH, MDPI, vol. 17(5), pages 1-25, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Essam A. Rashed & Akimasa Hirata, 2021. "One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan," IJERPH, MDPI, vol. 18(11), pages 1-16, May.
    2. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    3. Diego Galvan & Luciane Effting & Hágata Cremasco & Carlos Adam Conte-Junior, 2020. "Can Socioeconomic, Health, and Safety Data Explain the Spread of COVID-19 Outbreak on Brazilian Federative Units?," IJERPH, MDPI, vol. 17(23), pages 1-16, November.
    4. Sachiko Kodera & Essam A. Rashed & Akimasa Hirata, 2020. "Correlation between COVID-19 Morbidity and Mortality Rates in Japan and Local Population Density, Temperature, and Absolute Humidity," IJERPH, MDPI, vol. 17(15), pages 1-14, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Behrouz Pirouz & Sina Shaffiee Haghshenas & Behzad Pirouz & Sami Shaffiee Haghshenas & Patrizia Piro, 2020. "Development of an Assessment Method for Investigating the Impact of Climate and Urban Parameters in Confirmed Cases of COVID-19: A New Challenge in Sustainable Development," IJERPH, MDPI, vol. 17(8), pages 1-17, April.
    2. Behrouz Pirouz & Sina Shaffiee Haghshenas & Sami Shaffiee Haghshenas & Patrizia Piro, 2020. "Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligenc," Sustainability, MDPI, vol. 12(6), pages 1-21, March.
    3. Piero Bevilacqua & Stefania Perrella & Daniela Cirone & Roberto Bruno & Natale Arcuri, 2021. "Efficiency Improvement of Photovoltaic Modules via Back Surface Cooling," Energies, MDPI, vol. 14(4), pages 1-18, February.
    4. Behrouz Pirouz & Stefania Anna Palermo & Mario Maiolo & Natale Arcuri & Patrizia Piro, 2020. "Decreasing Water Footprint of Electricity and Heat by Extensive Green Roofs: Case of Southern Italy," Sustainability, MDPI, vol. 12(23), pages 1-16, December.
    5. Min Su & Qiang Wang & Rongrong Li, 2021. "How to Dispose of Medical Waste Caused by COVID-19? A Case Study of China," IJERPH, MDPI, vol. 18(22), pages 1-18, November.
    6. Bin Li & Weihong Guo & Xiao Liu & Yuqing Zhang & Peter John Russell & Marc Aurel Schnabel, 2021. "Sustainable Passive Design for Building Performance of Healthy Built Environment in the Lingnan Area," Sustainability, MDPI, vol. 13(16), pages 1-22, August.
    7. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    8. Gabriele Cervino & Luca Fiorillo & Giovanni Surace & Valeria Paduano & Maria Teresa Fiorillo & Rosa De Stefano & Riccardo Laudicella & Sergio Baldari & Michele Gaeta & Marco Cicciù, 2020. "SARS-CoV-2 Persistence: Data Summary up to Q2 2020," Data, MDPI, vol. 5(3), pages 1-16, September.
    9. Giuseppe Agapito & Chiara Zucco & Mario Cannataro, 2020. "COVID-WAREHOUSE: A Data Warehouse of Italian COVID-19, Pollution, and Climate Data," IJERPH, MDPI, vol. 17(15), pages 1-22, August.
    10. Joaquín Hernandez-Fernandez & Juan Carrascal Sanchez & Juan Lopez Martinez, 2024. "Sustainable Catalysts from Industrial FeO Waste for Pyrolysis and Oxidation of Hospital Polypropylene in Cartagena," Sustainability, MDPI, vol. 16(14), pages 1-22, July.
    11. Wu, Qunli & Peng, Chenyang, 2017. "A hybrid BAG-SA optimal approach to estimate energy demand of China," Energy, Elsevier, vol. 120(C), pages 985-995.
    12. Eugenia Ama Andoh & Hao Yu, 2023. "A two-stage decision-support approach for improving sustainable last-mile cold chain logistics operations of COVID-19 vaccines," Annals of Operations Research, Springer, vol. 328(1), pages 75-105, September.
    13. Satrio Mukti Wibowo & Dedi Budiman Hakim & Baba Barus & Akhmad Fauzi, 2022. "Estimation of Energy Demand in Indonesia using Artificial Neural Network," International Journal of Energy Economics and Policy, Econjournals, vol. 12(6), pages 261-271, November.
    14. Giuseppe Guido & Sina Shaffiee Haghshenas & Sami Shaffiee Haghshenas & Alessandro Vitale & Vincenzo Gallelli & Vittorio Astarita, 2020. "Development of a Binary Classification Model to Assess Safety in Transportation Systems Using GMDH-Type Neural Network Algorithm," Sustainability, MDPI, vol. 12(17), pages 1-19, August.
    15. Alaa Khadra & Mårten Hugosson & Jan Akander & Jonn Are Myhren, 2020. "Development of a Weight Factor Method for Sustainability Decisions in Building Renovation. Case Study Using Renobuild," Sustainability, MDPI, vol. 12(17), pages 1-15, September.
    16. Silva, Mafalda C. & Horta, Isabel M. & Leal, Vítor & Oliveira, Vítor, 2017. "A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand," Applied Energy, Elsevier, vol. 202(C), pages 386-398.
    17. Natale Arcuri & Manuela De Ruggiero & Francesca Salvo & Raffaele Zinno, 2020. "Automated Valuation Methods through the Cost Approach in a BIM and GIS Integration Framework for Smart City Appraisals," Sustainability, MDPI, vol. 12(18), pages 1-16, September.
    18. Edson R. Marciotto & Marcos Vinicius Bueno de Morais, 2021. "Energetics of Urban Canopies: A Meteorological Perspective," J, MDPI, vol. 4(4), pages 1-19, October.
    19. Behrouz Pirouz & Natale Arcuri & Behzad Pirouz & Stefania Anna Palermo & Michele Turco & Mario Maiolo, 2020. "Development of an Assessment Method for Evaluation of Sustainable Factories," Sustainability, MDPI, vol. 12(5), pages 1-15, February.
    20. Mehmet Kayakuş, 2020. "The Estimation of Turkey's Energy Demand Through Artificial Neural Networks and Support Vector Regression Methods," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(2), pages 227-236, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:10:p:3730-:d:362633. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.