Medium term municipal solid waste generation prediction by autoregressive integrated moving average

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.

Original languageEnglish
Title of host publicationStatistics and Operational Research International Conference, SORIC 2013
PublisherAmerican Institute of Physics Inc.
Pages427-435
Number of pages9
Volume1613
ISBN (Electronic)9780735412491
DOIs
Publication statusPublished - 1 Jan 2014
EventStatistics and Operational Research International Conference, SORIC 2013 - Sarawak, Malaysia
Duration: 3 Dec 20135 Dec 2013

Other

OtherStatistics and Operational Research International Conference, SORIC 2013
CountryMalaysia
CitySarawak
Period3/12/135/12/13

Fingerprint

solid wastes
predictions
forecasting
waste management
Malaysia
root-mean-square errors
planning
intervals
trends

Keywords

  • ARIMA
  • Solid Waste Forecasting
  • Solid Waste Generation
  • Solid Waste Management

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Younes, M. K., Mohd Nopiah, Z., Ahmad Basri, N. E., & Basri, H. (2014). Medium term municipal solid waste generation prediction by autoregressive integrated moving average. In Statistics and Operational Research International Conference, SORIC 2013 (Vol. 1613, pp. 427-435). American Institute of Physics Inc.. https://doi.org/10.1063/1.4894366

Medium term municipal solid waste generation prediction by autoregressive integrated moving average. / Younes, Mohammad K.; Mohd Nopiah, Zulkifli; Ahmad Basri, Noor Ezlin; Basri, Hassan.

Statistics and Operational Research International Conference, SORIC 2013. Vol. 1613 American Institute of Physics Inc., 2014. p. 427-435.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Younes, MK, Mohd Nopiah, Z, Ahmad Basri, NE & Basri, H 2014, Medium term municipal solid waste generation prediction by autoregressive integrated moving average. in Statistics and Operational Research International Conference, SORIC 2013. vol. 1613, American Institute of Physics Inc., pp. 427-435, Statistics and Operational Research International Conference, SORIC 2013, Sarawak, Malaysia, 3/12/13. https://doi.org/10.1063/1.4894366
Younes MK, Mohd Nopiah Z, Ahmad Basri NE, Basri H. Medium term municipal solid waste generation prediction by autoregressive integrated moving average. In Statistics and Operational Research International Conference, SORIC 2013. Vol. 1613. American Institute of Physics Inc. 2014. p. 427-435 https://doi.org/10.1063/1.4894366
Younes, Mohammad K. ; Mohd Nopiah, Zulkifli ; Ahmad Basri, Noor Ezlin ; Basri, Hassan. / Medium term municipal solid waste generation prediction by autoregressive integrated moving average. Statistics and Operational Research International Conference, SORIC 2013. Vol. 1613 American Institute of Physics Inc., 2014. pp. 427-435
@inproceedings{91335d2f9bcc4aca97f21f66fd10ded5,
title = "Medium term municipal solid waste generation prediction by autoregressive integrated moving average",
abstract = "Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95{\%} confident interval.",
keywords = "ARIMA, Solid Waste Forecasting, Solid Waste Generation, Solid Waste Management",
author = "Younes, {Mohammad K.} and {Mohd Nopiah}, Zulkifli and {Ahmad Basri}, {Noor Ezlin} and Hassan Basri",
year = "2014",
month = "1",
day = "1",
doi = "10.1063/1.4894366",
language = "English",
volume = "1613",
pages = "427--435",
booktitle = "Statistics and Operational Research International Conference, SORIC 2013",
publisher = "American Institute of Physics Inc.",

}

TY - GEN

T1 - Medium term municipal solid waste generation prediction by autoregressive integrated moving average

AU - Younes, Mohammad K.

AU - Mohd Nopiah, Zulkifli

AU - Ahmad Basri, Noor Ezlin

AU - Basri, Hassan

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.

AB - Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.

KW - ARIMA

KW - Solid Waste Forecasting

KW - Solid Waste Generation

KW - Solid Waste Management

UR - http://www.scopus.com/inward/record.url?scp=84941630284&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84941630284&partnerID=8YFLogxK

U2 - 10.1063/1.4894366

DO - 10.1063/1.4894366

M3 - Conference contribution

VL - 1613

SP - 427

EP - 435

BT - Statistics and Operational Research International Conference, SORIC 2013

PB - American Institute of Physics Inc.

ER -