IDEAS home Printed from https://ideas.repec.org/a/zib/zbnecr/v5y2022i2p78-84.html
   My bibliography  Save this article

Spatial Modeling Of Air Pollutant Concentrations Using Gwr And Anfis Models In Tehran City

Author

Listed:
  • Vahid Isazade

    (Department of GIS and remote sensing, Faculty of geography, University of Tehran, Tehran, Iran.)

  • Abdul Baser Qasimi

    (Department of geography, Faculty of Education, Samangan university, Samangan, Afghanistan.)

  • Keyvan Seraj

    (Department of Civil Engineering, Kharazmi University, Tehran, Iran)

  • Esmail Isazade

    (Department of Urban planning, Kharazmi University, Tehran, Iran)

Abstract

Today, air quality is a major subject in city regions that have affected human health, the environment, and the city ecosystem. Therefore, government officials, environmental organizations, health organizations, and city managers often need to model the concentration of air contaminants. This study aimed to compare geographically weighted regression (GWR) modeling and neural network (ANFIS) using Segno and Mamdani rules to spatially predict the concentration density of fNO2, CO, and SO2 pollutant indices. And PM 2.5 for the year 2021 in Tehran. The results of the statistical analysis of Sugeno and Mamdani rules revealed that the (RMSE) in evaluating the ANFIS model with the Mamdani method was 0.895 ppm, and with the Sugno method it was 1.004 ppm, whereas the RMSE in terms of Spatial weighted regression model was obtained on digital model with a height of (12.5 m) and a value of 692.0 ppm. The evaluation results showed that Mamdani and Sugno laws do not have the same and desirable accuracy. For Mamdani law, the RMSE level of PM 2.5 pollutant was (0.71 ppm) and according to Sugno law, this level was obtained for CO pollutant (0.81 ppm). While evaluating the geographically weighted regression model for the four air pollution indices the digital altitude model of (12.5 m) had similar results, which statistically for the digital altitude model of (12.5 m) obtained the RMSE for PM 2.5 (0.82 ppm). The findings of this study demonstrated that the weighted geographic regression model and the ANFI neural network have acceptable functionalities for spatial prediction of air pollutants.

Suggested Citation

  • Vahid Isazade & Abdul Baser Qasimi & Keyvan Seraj & Esmail Isazade, 2022. "Spatial Modeling Of Air Pollutant Concentrations Using Gwr And Anfis Models In Tehran City," Environmental Contaminants Reviews (ECR), Zibeline International Publishing, vol. 5(2), pages 78-84, October.
    • Handle: RePEc:zib:zbnecr:v:5:y:2022:i:2:p:78-84
    DOI: 10.26480/ecr.02.2022.78.84
    as

    Download full text from publisher

    File URL: https://contaminantsreviews.com/paper/2ecr2022/2ecr2022-78-84.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.26480/ecr.02.2022.78.84?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Amirkhani, S. & Nasirivatan, Sh. & Kasaeian, A.B. & Hajinezhad, A., 2015. "ANN and ANFIS models to predict the performance of solar chimney power plants," Renewable Energy, Elsevier, vol. 83(C), pages 597-607.
    2. Laura Goulier & Bastian Paas & Laura Ehrnsperger & Otto Klemm, 2020. "Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables," IJERPH, MDPI, vol. 17(6), pages 1-22, March.
    Full references (including those not matched with items on IDEAS)

    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. Amirifard, Masoumeh & Kasaeian, Alibakhsh & Amidpour, Majid, 2018. "Integration of a solar pond with a latent heat storage system," Renewable Energy, Elsevier, vol. 125(C), pages 682-693.
    2. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    3. Nirmalendu Biswas & Dipak Kumar Mandal & Sharmistha Bose & Nirmal K. Manna & Ali Cemal Benim, 2023. "Experimental Treatment of Solar Chimney Power Plant—A Comprehensive Review," Energies, MDPI, vol. 16(17), pages 1-41, August.
    4. Zendehboudi, Sohrab & Rezaei, Nima & Lohi, Ali, 2018. "Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review," Applied Energy, Elsevier, vol. 228(C), pages 2539-2566.
    5. Ayadi, Ahmed & Bouabidi, Abdallah & Driss, Zied & Abid, Mohamed Salah, 2018. "Experimental and numerical analysis of the collector roof height effect on the solar chimney performance," Renewable Energy, Elsevier, vol. 115(C), pages 649-662.
    6. Selimefendigil, Fatih & Öztop, Hakan F., 2021. "Thermoelectric generation in bifurcating channels and efficient modeling by using hybrid CFD and artificial neural networks," Renewable Energy, Elsevier, vol. 172(C), pages 582-598.
    7. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    8. Dong, Qingli & Sun, Yuhuan & Li, Peizhi, 2017. "A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China," Renewable Energy, Elsevier, vol. 102(PA), pages 241-257.
    9. Abdelsalam, Emad & Darwish, Omar & Karajeh, Ola & Almomani, Fares & Darweesh, Dirar & Kiswani, Sanad & Omar, Abdullah & Alkisrawi, Malek, 2022. "A classifier to detect best mode for Solar Chimney Power Plant system," Renewable Energy, Elsevier, vol. 197(C), pages 244-256.
    10. Yan An & Zhihong Zou & Ranran Li, 2016. "Descriptive Characteristics of Surface Water Quality in Hong Kong by a Self-Organising Map," IJERPH, MDPI, vol. 13(1), pages 1-13, January.
    11. Nidhal Ben Khedher & Fatih Selimefendigil & Lioua Kolsi & Walid Aich & Lotfi Ben Said & Ismail Boukholda, 2022. "Performance Optimization of a Thermoelectric Device by Using a Shear Thinning Nanofluid and Rotating Cylinder in a Cavity with Ventilation Ports," Mathematics, MDPI, vol. 10(7), pages 1-20, March.

    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:zib:zbnecr:v:5:y:2022:i:2:p:78-84. 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: Zibeline International Publishing (email available below). General contact details of provider: https://contaminantsreviews.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.