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Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

Author

Listed:
  • Ayaz Hussain

    (Department of Computer Science, University of Management and Technology Sialkot, Sialkot 51310, Pakistan)

  • Umar Draz

    (Department of Computer Science, University of Sahiwal, Sahiwal 57000, Pakistan
    CS Department COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan)

  • Tariq Ali

    (Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia)

  • Saman Tariq

    (Department of Computer Science, University of Management and Technology Sialkot, Sialkot 51310, Pakistan)

  • Muhammad Irfan

    (Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia)

  • Adam Glowacz

    (Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Kraków, Poland)

  • Jose Alfonso Antonino Daviu

    (Department Electrical Engineering, Universitat Politecnica de Valencia, Instituto Tecnologico de la Energía Camino de Vera s/n, 46022 Valencia, Spain)

  • Sana Yasin

    (Department of Computer Science and Information Technology, Superior University, Gold Campus, Lahore 54000, Pakistan
    Computer Science Department, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan)

  • Saifur Rahman

    (Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia)

Abstract

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

Suggested Citation

  • Ayaz Hussain & Umar Draz & Tariq Ali & Saman Tariq & Muhammad Irfan & Adam Glowacz & Jose Alfonso Antonino Daviu & Sana Yasin & Saifur Rahman, 2020. "Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach," Energies, MDPI, vol. 13(15), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3930-:d:393104
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    References listed on IDEAS

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    1. World Bank, 2012. "The World Bank Annual Report 2012," World Bank Publications - Books, The World Bank Group, number 11846.
    2. Kraay,Aart C., 2018. "Methodology for a World Bank Human Capital Index," Policy Research Working Paper Series 8593, The World Bank.
    3. World Bank, 2012. "The World Bank Annual Report 2012," World Bank Publications - Books, The World Bank Group, number 11845.
    4. World Bank, 2012. "The World Bank Annual Report 2012," World Bank Publications - Books, The World Bank Group, number 11844.
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    Cited by:

    1. Abdul Rehman & Muhammad Ahmed Qureshi & Tariq Ali & Muhammad Irfan & Saima Abdullah & Sana Yasin & Umar Draz & Adam Glowacz & Grzegorz Nowakowski & Abdullah Alghamdi & Abdulaziz A. Alsulami & Mariusz , 2021. "Smart Fire Detection and Deterrent System for Human Savior by Using Internet of Things (IoT)," Energies, MDPI, vol. 14(17), pages 1-30, September.
    2. Abdallah Namoun & Ali Tufail & Muhammad Yasar Khan & Ahmed Alrehaili & Toqeer Ali Syed & Oussama BenRhouma, 2022. "Solid Waste Generation and Disposal Using Machine Learning Approaches: A Survey of Solutions and Challenges," Sustainability, MDPI, vol. 14(20), pages 1-32, October.
    3. Mesfer Al Duhayyim & Heba G. Mohamed & Mohammed Aljebreen & Mohamed K. Nour & Abdullah Mohamed & Amgad Atta Abdelmageed & Ishfaq Yaseen & Gouse Pasha Mohammed, 2022. "Artificial Ecosystem-Based Optimization with an Improved Deep Learning Model for IoT-Assisted Sustainable Waste Management," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    4. Jae Hong Park & Phil Goo Kang & Eunseok Kim & Tae Woo Kim & Gahee Kim & Heejeong Seok & Jinwon Seo, 2021. "Introduction of IoT-Based Surrogate Parameters in the Ex-Post Countermeasure of Industrial Sectors in Integrated Permit Policy," Sustainability, MDPI, vol. 13(23), pages 1-22, December.
    5. Nurul Hamizah Mohamed & Samir Khan & Sandeep Jagtap, 2023. "Modernizing Medical Waste Management: Unleashing the Power of the Internet of Things (IoT)," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    6. Vladimir Simic & Ali Ebadi Torkayesh & Abtin Ijadi Maghsoodi, 2023. "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," Annals of Operations Research, Springer, vol. 328(1), pages 1105-1150, September.
    7. Sehrish Munawar Cheema & Abdul Hannan & Ivan Miguel Pires, 2022. "Smart Waste Management and Classification Systems Using Cutting Edge Approach," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
    8. Shaik Vaseem Akram & Rajesh Singh & Anita Gehlot & Mamoon Rashid & Ahmed Saeed AlGhamdi & Sultan S. Alshamrani & Deepak Prashar, 2021. "Role of Wireless Aided Technologies in the Solid Waste Management: A Comprehensive Review," Sustainability, MDPI, vol. 13(23), pages 1-31, November.
    9. Sabbir Ahmed & Sameera Mubarak & Jia Tina Du & Santoso Wibowo, 2022. "Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning," IJERPH, MDPI, vol. 19(24), pages 1-15, December.

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