Prediction of C&D, Grit, Asphalt and Treated Biomedical Wastes During COVID-19 Using Grey Model

Sanaalsadat Eslami; Golam Kabir; Kelvin Tsun Wai Ng

doi:10.1007/978-981-19-1061-6_40

Prediction of C&D, Grit, Asphalt and Treated Biomedical Wastes During COVID-19 Using Grey Model

Sanaalsadat Eslami^*
, Golam Kabir
, Kelvin Tsun Wai Ng

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Waste management has been recognized as a real issue in the current situation due to the COVID-19 impact on people’s lifestyles. Therefore, serious actions need to be taken to control and manage this impact on the environment. One of these important environmental programs is the investigation and research of generated wastes during the pandemic. Due to the COVID-19 pandemic, the types and amounts of waste generation have changed, therefore a way forward to reduce this impact is to investigate the data that coming from landfill to devise an appropriate approach. The goal of this study is to predict the amount of construction and demolition (C&D), Grit, Asphalt waste, and Treated Biomedical waste (TBW) before, during, and after pandemic using grey systems theory. The grey model is a relatively new forecasting method that has been employed for prediction in a small amount of data and is also used for uncertain systems. In this study, the data coming from Regina landfill is used to predict the amount of wastes generated during the pandemic using the grey model. The results will be compared with the existing regression-based waste model. Different measures like mean absolute percent error (MAPE) and root mean square error (RMSE) will be used to compare and evaluate the performance of these models. Finally, the best forecasting model will be chosen to predict the amount of waste generation for the future generation.

Original language	English
Title of host publication	Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 - CSCE21 Environmental Track
Editors	Scott Walbridge, Mazdak Nik-Bakht, Kelvin Tsun Ng, Manas Shome, M. Shahria Alam, Ashraf El Damatty, Gordon Lovegrove
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	385-394
Number of pages	10
ISBN (Print)	9789811910609
DOIs	https://doi.org/10.1007/978-981-19-1061-6_40
State	Published - 2023
Externally published	Yes
Event	Annual Conference of the Canadian Society of Civil Engineering , CSCE 2021 - Virtual, Online Duration: 26 May 2021 → 29 May 2021

Publication series

Name	Lecture Notes in Civil Engineering
Volume	249
ISSN (Print)	2366-2557
ISSN (Electronic)	2366-2565

Conference

Conference	Annual Conference of the Canadian Society of Civil Engineering , CSCE 2021
City	Virtual, Online
Period	26/05/21 → 29/05/21

Bibliographical note

Publisher Copyright:
© 2023, Canadian Society for Civil Engineering.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 12 Responsible Consumption and Production

Keywords

COVID-19
Grey model
Pandemic
Waste management
Waste prediction

ASJC Scopus subject areas

Civil and Structural Engineering

Access to Document

10.1007/978-981-19-1061-6_40

Cite this

Eslami, S., Kabir, G., & Ng, K. T. W. (2023). Prediction of C&D, Grit, Asphalt and Treated Biomedical Wastes During COVID-19 Using Grey Model. In S. Walbridge, M. Nik-Bakht, K. T. Ng, M. Shome, M. S. Alam, A. El Damatty, & G. Lovegrove (Eds.), Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 - CSCE21 Environmental Track (pp. 385-394). (Lecture Notes in Civil Engineering; Vol. 249). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-1061-6_40

Eslami, Sanaalsadat ; Kabir, Golam ; Ng, Kelvin Tsun Wai. / Prediction of C&D, Grit, Asphalt and Treated Biomedical Wastes During COVID-19 Using Grey Model. Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 - CSCE21 Environmental Track. editor / Scott Walbridge ; Mazdak Nik-Bakht ; Kelvin Tsun Ng ; Manas Shome ; M. Shahria Alam ; Ashraf El Damatty ; Gordon Lovegrove. Springer Science and Business Media Deutschland GmbH, 2023. pp. 385-394 (Lecture Notes in Civil Engineering).

@inproceedings{b1d89d0ed8a048f383e3910b8e5d81b1,

title = "Prediction of C\&D, Grit, Asphalt and Treated Biomedical Wastes During COVID-19 Using Grey Model",

abstract = "Waste management has been recognized as a real issue in the current situation due to the COVID-19 impact on people{\textquoteright}s lifestyles. Therefore, serious actions need to be taken to control and manage this impact on the environment. One of these important environmental programs is the investigation and research of generated wastes during the pandemic. Due to the COVID-19 pandemic, the types and amounts of waste generation have changed, therefore a way forward to reduce this impact is to investigate the data that coming from landfill to devise an appropriate approach. The goal of this study is to predict the amount of construction and demolition (C\&D), Grit, Asphalt waste, and Treated Biomedical waste (TBW) before, during, and after pandemic using grey systems theory. The grey model is a relatively new forecasting method that has been employed for prediction in a small amount of data and is also used for uncertain systems. In this study, the data coming from Regina landfill is used to predict the amount of wastes generated during the pandemic using the grey model. The results will be compared with the existing regression-based waste model. Different measures like mean absolute percent error (MAPE) and root mean square error (RMSE) will be used to compare and evaluate the performance of these models. Finally, the best forecasting model will be chosen to predict the amount of waste generation for the future generation.",

keywords = "COVID-19, Grey model, Pandemic, Waste management, Waste prediction",

author = "Sanaalsadat Eslami and Golam Kabir and Ng, \{Kelvin Tsun Wai\}",

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year = "2023",

doi = "10.1007/978-981-19-1061-6\_40",

language = "English",

isbn = "9789811910609",

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publisher = "Springer Science and Business Media Deutschland GmbH",

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editor = "Scott Walbridge and Mazdak Nik-Bakht and Ng, \{Kelvin Tsun\} and Manas Shome and Alam, \{M. Shahria\} and \{El Damatty\}, Ashraf and Gordon Lovegrove",

booktitle = "Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 - CSCE21 Environmental Track",

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Eslami, S, Kabir, G & Ng, KTW 2023, Prediction of C&D, Grit, Asphalt and Treated Biomedical Wastes During COVID-19 Using Grey Model. in S Walbridge, M Nik-Bakht, KT Ng, M Shome, MS Alam, A El Damatty & G Lovegrove (eds), Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 - CSCE21 Environmental Track. Lecture Notes in Civil Engineering, vol. 249, Springer Science and Business Media Deutschland GmbH, pp. 385-394, Annual Conference of the Canadian Society of Civil Engineering , CSCE 2021, Virtual, Online, 26/05/21. https://doi.org/10.1007/978-981-19-1061-6_40

Prediction of C&D, Grit, Asphalt and Treated Biomedical Wastes During COVID-19 Using Grey Model. / Eslami, Sanaalsadat; Kabir, Golam; Ng, Kelvin Tsun Wai.
Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 - CSCE21 Environmental Track. ed. / Scott Walbridge; Mazdak Nik-Bakht; Kelvin Tsun Ng; Manas Shome; M. Shahria Alam; Ashraf El Damatty; Gordon Lovegrove. Springer Science and Business Media Deutschland GmbH, 2023. p. 385-394 (Lecture Notes in Civil Engineering; Vol. 249).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Prediction of C&D, Grit, Asphalt and Treated Biomedical Wastes During COVID-19 Using Grey Model

AU - Eslami, Sanaalsadat

AU - Kabir, Golam

AU - Ng, Kelvin Tsun Wai

PY - 2023

Y1 - 2023

N2 - Waste management has been recognized as a real issue in the current situation due to the COVID-19 impact on people’s lifestyles. Therefore, serious actions need to be taken to control and manage this impact on the environment. One of these important environmental programs is the investigation and research of generated wastes during the pandemic. Due to the COVID-19 pandemic, the types and amounts of waste generation have changed, therefore a way forward to reduce this impact is to investigate the data that coming from landfill to devise an appropriate approach. The goal of this study is to predict the amount of construction and demolition (C&D), Grit, Asphalt waste, and Treated Biomedical waste (TBW) before, during, and after pandemic using grey systems theory. The grey model is a relatively new forecasting method that has been employed for prediction in a small amount of data and is also used for uncertain systems. In this study, the data coming from Regina landfill is used to predict the amount of wastes generated during the pandemic using the grey model. The results will be compared with the existing regression-based waste model. Different measures like mean absolute percent error (MAPE) and root mean square error (RMSE) will be used to compare and evaluate the performance of these models. Finally, the best forecasting model will be chosen to predict the amount of waste generation for the future generation.

AB - Waste management has been recognized as a real issue in the current situation due to the COVID-19 impact on people’s lifestyles. Therefore, serious actions need to be taken to control and manage this impact on the environment. One of these important environmental programs is the investigation and research of generated wastes during the pandemic. Due to the COVID-19 pandemic, the types and amounts of waste generation have changed, therefore a way forward to reduce this impact is to investigate the data that coming from landfill to devise an appropriate approach. The goal of this study is to predict the amount of construction and demolition (C&D), Grit, Asphalt waste, and Treated Biomedical waste (TBW) before, during, and after pandemic using grey systems theory. The grey model is a relatively new forecasting method that has been employed for prediction in a small amount of data and is also used for uncertain systems. In this study, the data coming from Regina landfill is used to predict the amount of wastes generated during the pandemic using the grey model. The results will be compared with the existing regression-based waste model. Different measures like mean absolute percent error (MAPE) and root mean square error (RMSE) will be used to compare and evaluate the performance of these models. Finally, the best forecasting model will be chosen to predict the amount of waste generation for the future generation.

KW - COVID-19

KW - Grey model

KW - Pandemic

KW - Waste management

KW - Waste prediction

UR - https://www.scopus.com/pages/publications/85138762088

U2 - 10.1007/978-981-19-1061-6_40

DO - 10.1007/978-981-19-1061-6_40

M3 - Conference contribution

AN - SCOPUS:85138762088

SN - 9789811910609

T3 - Lecture Notes in Civil Engineering

SP - 385

EP - 394

BT - Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 - CSCE21 Environmental Track

A2 - Walbridge, Scott

A2 - Nik-Bakht, Mazdak

A2 - Ng, Kelvin Tsun

A2 - Shome, Manas

A2 - Alam, M. Shahria

A2 - El Damatty, Ashraf

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ER -

Eslami S, Kabir G, Ng KTW. Prediction of C&D, Grit, Asphalt and Treated Biomedical Wastes During COVID-19 Using Grey Model. In Walbridge S, Nik-Bakht M, Ng KT, Shome M, Alam MS, El Damatty A, Lovegrove G, editors, Proceedings of the Canadian Society of Civil Engineering Annual Conference 2021 - CSCE21 Environmental Track. Springer Science and Business Media Deutschland GmbH. 2023. p. 385-394. (Lecture Notes in Civil Engineering). doi: 10.1007/978-981-19-1061-6_40

Prediction of C&D, Grit, Asphalt and Treated Biomedical Wastes During COVID-19 Using Grey Model

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this