Retrospective Reconstruction of Time Series Data for Integrated Waste Management
The development, operation and maintenance of
Integrated Waste Management Systems (IWMS) affects essentially
the sustainable concern of every region. The features of such systems
have great influence on all of the components of sustainability. In
order to reach the optimal way of processes, a comprehensive
mapping of the variables affecting the future efficiency of the system
is needed such as analysis of the interconnections among the
components and modeling of their interactions. The planning of a
IWMS is based fundamentally on technical and economical
opportunities and the legal framework. Modeling the sustainability
and operation effectiveness of a certain IWMS is not in the scope of
the present research. The complexity of the systems and the large
number of the variables require the utilization of a complex approach
to model the outcomes and future risks. This complex method should
be able to evaluate the logical framework of the factors composing
the system and the interconnections between them. The authors of
this paper studied the usability of the Fuzzy Cognitive Map (FCM)
approach modeling the future operation of IWMS’s. The approach
requires two input data set. One is the connection matrix containing
all the factors affecting the system in focus with all the
interconnections. The other input data set is the time series, a
retrospective reconstruction of the weights and roles of the factors.
This paper introduces a novel method to develop time series by
content analysis.
[1] M. S. Khan, M., Quaddus, Group Decision Support Using Fuzzy
Cognitive Maps for Causal Reasoning, Group Decision and Negotiation,
September 2004, Volume 13, Issue 5, pp 463-480.
[2] U. Özesmi, S. L. Özesmi, Ecological models based on people’s
knowledge: a multi-step fuzzy cognitive mapping approach, Ecological
Modelling, Volume 176, Issues 1–2, 15 August 2004, Pages 43–64.
[3] J. M. Gottman, R. M. McFALL, J. T. Barnett, Design and analysis of
research using time series, Psychological Bulletin, 1969, Vol. 72, No. 4,
299-306.
[4] G. E. Box, G. M. Jenkins, D. W. Bacon, Models for forecasting seasonal
and non-seasonal time-series. In B. Harris (Ed.), Spectral analysis of
time-series. New York: Wiley, 1967.
[5] D. T. Campbell, J. Stanley, Experimental and quasi-experimental
designs for research and teaching. In N. L. Gaye (Ed.), Handbook of
research on teaching. Chicago: Rand McNally, 1963.
[6] D. Allemag, J. Hendler, Semantic Web for the Working Ontologist, 2nd
edition, Morgan Kaufmann, 2011.
[7] A. Buruzs, M. F. Hatwágner, A. Torma, L. T. Kóczy, Expert Based
System Design for Integrated Waste Management, Paper Code
14AU120236, submitted to ICEESD Melbourne 2014.
[8] V. K. Bhatia et al., Legal discourse: Opportunities and threats for corpus
linguistics, in: Connor, U - Upton, A. Thomas (eds): Discourse in the
Professions, Perspectives from corpus linguistics, John Benjamins, 2004,
pp. 203-231.
[9] Apache Tika, Apache Software Foundation, https://tika.apache.org/.
[10] Zsibrita, János; Vincze, Veronika; Farkas, Richárd 2013: magyarlanc: A
Toolkit for Morphological and Dependency Parsing of Hungarian. In:
Proceedings of RANLP 2013, pp. 763-771.
[11] McEnry, Tony - Hardie, Andrew: Corpus Linguistics: Method, Theory
and Practice, Cambridge University Press, 2011
[12] Hung, M-L., Ma, H-w. and Yang W-F. (2007). A Novel Sustainable
Decision Making Model for Municipal Solid Waste Management. J. of
Waste Management, 27(2): 209–219.
[13] Papageorgiou, E. and Kontogianni, A. (2012). Using Fuzzy Cognitive
Mapping in Environmental Decision Making and Management: A
Methodological Primer and an Application. Int. Perspectives on Global
Environmental Change, S. Young (ed.), ISBN: 978-953-307-815-1,
InTech, doi: 10.5772/29375.
[14] Salhofer, S., Wassermann, G. and Binner, E. (2007). Strategic
Environmental Assessment as an Approach to Assess Waste
Management Systems. Experiences from an Austrian Case Study. J. of
Environmental Modelling & Software, 22(5): 610–618.
[15] Tanskanen, J-H. (2000). Strategic Planning of Municipal Solid Waste
Management. J. of Resources, Conservation and Recycling 30(2): 111–
133.
[16] Kalakula, S., Malakulb, P., Siemanondb, K. and Gania, R. (2014).
Integration of Life Cycle Assessment Software with Tools for Economic
and Sustainability Analyses and Process Simulation for Sustainable
Process Design. J. of Cleaner Production, Vol. 17, pp. 98-109.
[17] Wilson, E. J., McDougall, F. R. and Willmore, J. (2001). Euro-Trash:
Searching Europe for a More Sustainable Approach to Waste
management. J. of Resources Conservation and Recyclin,g 31(4), 327-
346.
[18] Stylos C. D., Georgopoulos V. C., Groumpos P. P. The use of Fuzzy
cognitive maps in modeling systems, in Proceedings of 5th IEEE
Mediterranean Conference on Control and Systems, Paphos, Cyprus,
July 21 - 23 1997, IEEE.
[19] Stylos C. D., Georgopoulos V. C., Groumpos P. P., The Use of Fuzzy
Cognitive Maps in Modeling Systems, Proc. of 5th IEEE Mediterranean
Conference on Control and Systems, Paphos, Cyprus, 1997. IEEE.
[1] M. S. Khan, M., Quaddus, Group Decision Support Using Fuzzy
Cognitive Maps for Causal Reasoning, Group Decision and Negotiation,
September 2004, Volume 13, Issue 5, pp 463-480.
[2] U. Özesmi, S. L. Özesmi, Ecological models based on people’s
knowledge: a multi-step fuzzy cognitive mapping approach, Ecological
Modelling, Volume 176, Issues 1–2, 15 August 2004, Pages 43–64.
[3] J. M. Gottman, R. M. McFALL, J. T. Barnett, Design and analysis of
research using time series, Psychological Bulletin, 1969, Vol. 72, No. 4,
299-306.
[4] G. E. Box, G. M. Jenkins, D. W. Bacon, Models for forecasting seasonal
and non-seasonal time-series. In B. Harris (Ed.), Spectral analysis of
time-series. New York: Wiley, 1967.
[5] D. T. Campbell, J. Stanley, Experimental and quasi-experimental
designs for research and teaching. In N. L. Gaye (Ed.), Handbook of
research on teaching. Chicago: Rand McNally, 1963.
[6] D. Allemag, J. Hendler, Semantic Web for the Working Ontologist, 2nd
edition, Morgan Kaufmann, 2011.
[7] A. Buruzs, M. F. Hatwágner, A. Torma, L. T. Kóczy, Expert Based
System Design for Integrated Waste Management, Paper Code
14AU120236, submitted to ICEESD Melbourne 2014.
[8] V. K. Bhatia et al., Legal discourse: Opportunities and threats for corpus
linguistics, in: Connor, U - Upton, A. Thomas (eds): Discourse in the
Professions, Perspectives from corpus linguistics, John Benjamins, 2004,
pp. 203-231.
[9] Apache Tika, Apache Software Foundation, https://tika.apache.org/.
[10] Zsibrita, János; Vincze, Veronika; Farkas, Richárd 2013: magyarlanc: A
Toolkit for Morphological and Dependency Parsing of Hungarian. In:
Proceedings of RANLP 2013, pp. 763-771.
[11] McEnry, Tony - Hardie, Andrew: Corpus Linguistics: Method, Theory
and Practice, Cambridge University Press, 2011
[12] Hung, M-L., Ma, H-w. and Yang W-F. (2007). A Novel Sustainable
Decision Making Model for Municipal Solid Waste Management. J. of
Waste Management, 27(2): 209–219.
[13] Papageorgiou, E. and Kontogianni, A. (2012). Using Fuzzy Cognitive
Mapping in Environmental Decision Making and Management: A
Methodological Primer and an Application. Int. Perspectives on Global
Environmental Change, S. Young (ed.), ISBN: 978-953-307-815-1,
InTech, doi: 10.5772/29375.
[14] Salhofer, S., Wassermann, G. and Binner, E. (2007). Strategic
Environmental Assessment as an Approach to Assess Waste
Management Systems. Experiences from an Austrian Case Study. J. of
Environmental Modelling & Software, 22(5): 610–618.
[15] Tanskanen, J-H. (2000). Strategic Planning of Municipal Solid Waste
Management. J. of Resources, Conservation and Recycling 30(2): 111–
133.
[16] Kalakula, S., Malakulb, P., Siemanondb, K. and Gania, R. (2014).
Integration of Life Cycle Assessment Software with Tools for Economic
and Sustainability Analyses and Process Simulation for Sustainable
Process Design. J. of Cleaner Production, Vol. 17, pp. 98-109.
[17] Wilson, E. J., McDougall, F. R. and Willmore, J. (2001). Euro-Trash:
Searching Europe for a More Sustainable Approach to Waste
management. J. of Resources Conservation and Recyclin,g 31(4), 327-
346.
[18] Stylos C. D., Georgopoulos V. C., Groumpos P. P. The use of Fuzzy
cognitive maps in modeling systems, in Proceedings of 5th IEEE
Mediterranean Conference on Control and Systems, Paphos, Cyprus,
July 21 - 23 1997, IEEE.
[19] Stylos C. D., Georgopoulos V. C., Groumpos P. P., The Use of Fuzzy
Cognitive Maps in Modeling Systems, Proc. of 5th IEEE Mediterranean
Conference on Control and Systems, Paphos, Cyprus, 1997. IEEE.
@article{"International Journal of Business, Human and Social Sciences:69015", author = "A. Buruzs and M. F. Hatwágner and A. Torma and L. T. Kóczy", title = "Retrospective Reconstruction of Time Series Data for Integrated Waste Management", abstract = "The development, operation and maintenance of
Integrated Waste Management Systems (IWMS) affects essentially
the sustainable concern of every region. The features of such systems
have great influence on all of the components of sustainability. In
order to reach the optimal way of processes, a comprehensive
mapping of the variables affecting the future efficiency of the system
is needed such as analysis of the interconnections among the
components and modeling of their interactions. The planning of a
IWMS is based fundamentally on technical and economical
opportunities and the legal framework. Modeling the sustainability
and operation effectiveness of a certain IWMS is not in the scope of
the present research. The complexity of the systems and the large
number of the variables require the utilization of a complex approach
to model the outcomes and future risks. This complex method should
be able to evaluate the logical framework of the factors composing
the system and the interconnections between them. The authors of
this paper studied the usability of the Fuzzy Cognitive Map (FCM)
approach modeling the future operation of IWMS’s. The approach
requires two input data set. One is the connection matrix containing
all the factors affecting the system in focus with all the
interconnections. The other input data set is the time series, a
retrospective reconstruction of the weights and roles of the factors.
This paper introduces a novel method to develop time series by
content analysis.
", keywords = "Content analysis, factors, integrated waste
management system, time series.", volume = "8", number = "12", pages = "3994-4", }
