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

Ayaz Hussain, Zaghum Umar, Tariq Ali, Saman Tariq, Muhammad Irfan, Adam Glowacz, Jose Alfonso Antonino Daviu, Sana Yasin and Saifur Rahman
Additional contact information
Ayaz Hussain: Department of Computer Science, University of Management and Technology Sialkot, Sialkot 51310, 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
Saifur Rahman: Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia

Energies, 2020, vol. 13, issue 15, 1-22

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.

Keywords: Internet of Things; air monitoring; forecasting; air pollutant; smart bin; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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