Predicting Cost of Municipal Waste Management using IoT Data and Machine Learning

Gregory Davrazos; Theodor Panagiotakopoulos; Sotiris Kotsiantis; Achilles Kameas

doi:10.1109/IISA59645.2023.10345856

Predicting Cost of Municipal Waste Management using IoT Data and Machine Learning

Gregory Davrazos
, Theodor Panagiotakopoulos
, Sotiris Kotsiantis
, Achilles Kameas

School of Business

University of Patras
Hellenic Open University

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

Abstract

Internet of Things (IoT) technologies have been proliferated as a solution able to deliver on the promise of efficient waste management. Their application has been explored in various stages of the waste management process leading to significant improvements compared to conventional methods. Machine learning techniques for IoT sensor data analysis make the implementation of smart waste management systems feasible and a offer a valuable tool to municipal authorities in the challenging process of waste management. This paper applies a set of machine learning regression models and a soft voting meta ensemble for predicting the cost of waste management using an open online dataset. Our study reveals that the CatBoost Regressor performs better than the rest of the models tested with the meta ensemble slightly improving its performance.

Original language	English
Title of host publication	14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350318067
DOIs	https://doi.org/10.1109/IISA59645.2023.10345856
Publication status	Published - 2023
Event	14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023 - Volos, Greece Duration: 10 Jul 2023 → 12 Jul 2023

Publication series

Name	14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

Conference

Conference	14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023
Country/Territory	Greece
City	Volos
Period	10/07/23 → 12/07/23

UN SDGs

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

SDG 11 Sustainable Cities and Communities

Keywords

Cost Prediction
Internet of Things
Machine Learning
Waste Management

Access to Document

10.1109/IISA59645.2023.10345856

Cite this

Davrazos, G., Panagiotakopoulos, T., Kotsiantis, S., & Kameas, A. (2023). Predicting Cost of Municipal Waste Management using IoT Data and Machine Learning. In 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023 (14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IISA59645.2023.10345856

Davrazos, Gregory ; Panagiotakopoulos, Theodor ; Kotsiantis, Sotiris et al. / Predicting Cost of Municipal Waste Management using IoT Data and Machine Learning. 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023).

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title = "Predicting Cost of Municipal Waste Management using IoT Data and Machine Learning",

abstract = "Internet of Things (IoT) technologies have been proliferated as a solution able to deliver on the promise of efficient waste management. Their application has been explored in various stages of the waste management process leading to significant improvements compared to conventional methods. Machine learning techniques for IoT sensor data analysis make the implementation of smart waste management systems feasible and a offer a valuable tool to municipal authorities in the challenging process of waste management. This paper applies a set of machine learning regression models and a soft voting meta ensemble for predicting the cost of waste management using an open online dataset. Our study reveals that the CatBoost Regressor performs better than the rest of the models tested with the meta ensemble slightly improving its performance.",

keywords = "Cost Prediction, Internet of Things, Machine Learning, Waste Management",

author = "Gregory Davrazos and Theodor Panagiotakopoulos and Sotiris Kotsiantis and Achilles Kameas",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023 ; Conference date: 10-07-2023 Through 12-07-2023",

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language = "English",

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Davrazos, G, Panagiotakopoulos, T, Kotsiantis, S & Kameas, A 2023, Predicting Cost of Municipal Waste Management using IoT Data and Machine Learning. in 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023. 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023, Institute of Electrical and Electronics Engineers Inc., 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023, Volos, Greece, 10/07/23. https://doi.org/10.1109/IISA59645.2023.10345856

Predicting Cost of Municipal Waste Management using IoT Data and Machine Learning. / Davrazos, Gregory; Panagiotakopoulos, Theodor; Kotsiantis, Sotiris et al.
14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023).

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

TY - GEN

T1 - Predicting Cost of Municipal Waste Management using IoT Data and Machine Learning

AU - Davrazos, Gregory

AU - Panagiotakopoulos, Theodor

AU - Kotsiantis, Sotiris

AU - Kameas, Achilles

PY - 2023

Y1 - 2023

N2 - Internet of Things (IoT) technologies have been proliferated as a solution able to deliver on the promise of efficient waste management. Their application has been explored in various stages of the waste management process leading to significant improvements compared to conventional methods. Machine learning techniques for IoT sensor data analysis make the implementation of smart waste management systems feasible and a offer a valuable tool to municipal authorities in the challenging process of waste management. This paper applies a set of machine learning regression models and a soft voting meta ensemble for predicting the cost of waste management using an open online dataset. Our study reveals that the CatBoost Regressor performs better than the rest of the models tested with the meta ensemble slightly improving its performance.

AB - Internet of Things (IoT) technologies have been proliferated as a solution able to deliver on the promise of efficient waste management. Their application has been explored in various stages of the waste management process leading to significant improvements compared to conventional methods. Machine learning techniques for IoT sensor data analysis make the implementation of smart waste management systems feasible and a offer a valuable tool to municipal authorities in the challenging process of waste management. This paper applies a set of machine learning regression models and a soft voting meta ensemble for predicting the cost of waste management using an open online dataset. Our study reveals that the CatBoost Regressor performs better than the rest of the models tested with the meta ensemble slightly improving its performance.

KW - Cost Prediction

KW - Internet of Things

KW - Machine Learning

KW - Waste Management

UR - https://www.scopus.com/pages/publications/85182024272

U2 - 10.1109/IISA59645.2023.10345856

DO - 10.1109/IISA59645.2023.10345856

M3 - Conference contribution

AN - SCOPUS:85182024272

T3 - 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

BT - 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

Y2 - 10 July 2023 through 12 July 2023

ER -

Davrazos G, Panagiotakopoulos T, Kotsiantis S, Kameas A. Predicting Cost of Municipal Waste Management using IoT Data and Machine Learning. In 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023). doi: 10.1109/IISA59645.2023.10345856

Predicting Cost of Municipal Waste Management using IoT Data and Machine Learning

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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