DOI: https://doi.org/10.15587/1729-4061.2018.141056

Development of decision support in the structure of the information­analytical system of atmospheric air environmental monitoring

Olena Kortsova, Volodymyr Bakharev, Igor Shevchenko, Svitlana Koval

Abstract


The main problem that determines the efficiency of environmental monitoring systems is the lack of validity of management decisions on correction of environmental situations. In such conditions, a formal versatile basis that describes the information-analytical system (IAS) of environmental monitoring is required.

For the development and description of the IAS composition and structure, elements of the theory of fuzzy logic and fuzzy sets and methods of system analysis are used. Thus, the theoretical basis for the development of a versatile IAS structure of environmental monitoring is formed. The set-theoretical model of the information-analytical system of environmental monitoring of atmospheric air at the municipal level, which includes subsystems of the urban system parameter monitoring, decision support, the information system “parameter database - situation knowledge base” is proposed.

The decision support subsystem is presented as the decision model that determines the allowable transformations of situations and a set of strategies for applying these transformations to solve the problem of eliminating an adverse situation. The adaptive fuzzy model of situation recognition in the process of environmental monitoring, which allows producing diagnostic conclusions is developed. The diagnostic process is represented by a sequence of actions, which involves three steps: determining the criticality for each situation feature; determining the degree of criticality; providing linguistic features. The advantage of the proposed IAS architecture is the possibility of fast scaling of the decision support system. This is achieved by simply expanding the feature and situation dictionary and the knowledge base, as well as the flexible configuration of the knowledge base by correction of weight ratios of elementary premises of the rules. The general description of the information technology of monitoring and support of operational decision-making on correction of environmentally hazardous situations is formed. The results of setting the fuzzy model of situation recognition by means of experimental learning of the system on the examples – specific results of observations of atmospheric air quality are obtained. The self-learning ability of the system is found, which ultimately will allow limiting the involvement of real individuals as experts in the assessment of environmental situations by automating the diagnostic process

Keywords


environmental monitoring; information-analytical system; model; situation recognition; decision support; information technology

References


Bakharev, V. S. (2016). Nedoskonalist isnuiuchoi systemy ekolohichnoho monitorynhu atmosfernoho povitria na rivni urbosystemy: prychyny, naslidky, shliakhy vdoskonalennia. Visnyk KrNU imeni Mykhaila Ostrohradskoho, 5 (100), 76–81.

Yatsyshyn, A. V. (2011). Ekolohichna bezpeka tekhnohenno-navantazhenykh rehioniv: aspekty upravlinnia. Visnyk Nats. tekhn. un-tu "KhPI", 24, 72–76.

Balis, B., Bartynski, T., Bubak, M., Harezlak, D., Kasztelnik, M., Malawski, M. et. al. (2017). Smart levee monitoring and flood decision support system: reference architecture and urgent computing management. Procedia Computer Science, 108, 2220–2229. doi: https://doi.org/10.1016/j.procs.2017.05.192

Balis, B., Bubak, M., Harezlak, D., Nowakowski, P., Pawlik, M., Wilk, B. (2017). Towards an operational database for real-time environmental monitoring and early warning systems. Procedia Computer Science, 108, 2250–2259. doi: https://doi.org/10.1016/j.procs.2017.05.193

Ferreira, L., Putnik, G. D., Lopes, N., Lopes, A., Cruz-Cunha, M. M. (2015). A Cloud and Ubiquitous Architecture for Effective Environmental Sensing and Monitoring. Procedia Computer Science, 64, 1256–1262. doi: https://doi.org/10.1016/j.procs.2015.09.240

Lokers, R., Knapen, R., Janssen, S., van Randen, Y., Jansen, J. (2016). Analysis of Big Data technologies for use in agro-environmental science. Environmental Modelling & Software, 84, 494–504. doi: https://doi.org/10.1016/j.envsoft.2016.07.017

Xiaomin, Z., Jianjun, Y., Xiaoci, H., Shaoli, C. (2016). An Ontology-based Knowledge Modelling Approach for River Water Quality Monitoring and Assessment. Procedia Computer Science, 96, 335–344. doi: https://doi.org/10.1016/j.procs.2016.08.146

Korobko, A. V., Penkova, T. G. (2010). On-line analytical processing based on formal concept analysis. Procedia Computer Science, 1 (1), 2311–2317. doi: https://doi.org/10.1016/j.procs.2010.04.259

Jones, W. R., Spence, M. J., Bowman, A. W., Evers, L., Molinari, D. A. (2014). A software tool for the spatiotemporal analysis and reporting of groundwater monitoring data. Environmental Modelling & Software, 55, 242–249. doi: https://doi.org/10.1016/j.envsoft.2014.01.020

Zulkafli, Z., Perez, K., Vitolo, C., Buytaert, W., Karpouzoglou, T., Dewulf, A. et. al. (2017). User-driven design of decision support systems for polycentric environmental resources management. Environmental Modelling & Software, 88, 58–73. doi: https://doi.org/10.1016/j.envsoft.2016.10.012

Kameneva, I. P. (2013). Komp'yuternye sredstva ocenivaniya ekologicheskih riskov s ispol'zovaniem strukturnogo analiza dannyh monitoringa. Elektronnoe modelirovanie, 35 (6), 99–114.

Shevchenko, I., Tertyshnyi, V., Koval, S. (2017). Designing a model of a decision support system based on a multi-aspect factographic search. Eastern-European Journal of Enterprise Technologies, 4 (2 (88)), 20–26. doi: https://doi.org/10.15587/1729-4061.2017.108569

Bakharev, V. S., Shevchenko, I. V., Koval, S. S., Kortsova, O. L. (2017). Informatsiyno-tekhnolohichni aspekty upravlinnia ekolohichnoiu bezpekoiu v systemakh munitsypalnoho monitorynhu atmosfernoho povitria. Visnyk KrNU imeni Mykhaila Ostrohradskoho, 4 (105), 68–73.

Bakharev, V., Kortsova, O., Marenych, A., Kyrylaha, N., Moroz, M. (2017). Some aspects of the analysis of citizens' appeals to municipalities on environmental issues. International Journal of Innovative Science, Engineering & Technology, 4 (8), 272–278. Available at: http://ijiset.com/vol4/v4s8/IJISET_V4_I08_29.pdf


GOST Style Citations


Bakharev V. S. Nedoskonalist isnuiuchoi systemy ekolohichnoho monitorynhu atmosfernoho povitria na rivni urbosystemy: prychyny, naslidky, shliakhy vdoskonalennia // Visnyk KrNU imeni Mykhaila Ostrohradskoho. 2016. Issue 5 (100). P. 76–81.

Yatsyshyn A. V. Ekolohichna bezpeka tekhnohenno-navantazhenykh rehioniv: aspekty upravlinnia // Visnyk Nats. tekhn. un-tu "KhPI". 2011. Issue 24. P. 72–76.

Smart levee monitoring and flood decision support system: reference architecture and urgent computing management / Balis B., Bartynski T., Bubak M., Harezlak D., Kasztelnik M., Malawski M. et. al. // Procedia Computer Science. 2017. Vol. 108. P. 2220–2229. doi: https://doi.org/10.1016/j.procs.2017.05.192 

Towards an operational database for real-time environmental monitoring and early warning systems / Balis B., Bubak M., Harezlak D., Nowakowski P., Pawlik M., Wilk B. // Procedia Computer Science. 2017. Vol. 108. P. 2250–2259. doi: https://doi.org/10.1016/j.procs.2017.05.193 

A Cloud and Ubiquitous Architecture for Effective Environmental Sensing and Monitoring / Ferreira L., Putnik G. D., Lopes N., Lopes A., Cruz-Cunha M. M. // Procedia Computer Science. 2015. Vol. 64. P. 1256–1262. doi: https://doi.org/10.1016/j.procs.2015.09.240 

Analysis of Big Data technologies for use in agro-environmental science / Lokers R., Knapen R., Janssen S., van Randen Y., Jansen J. // Environmental Modelling & Software. 2016. Vol. 84. P. 494–504. doi: https://doi.org/10.1016/j.envsoft.2016.07.017 

An Ontology-based Knowledge Modelling Approach for River Water Quality Monitoring and Assessment / Xiaomin Z., Jianjun Y., Xiaoci H., Shaoli C. // Procedia Computer Science. 2016. Vol. 96. P. 335–344. doi: https://doi.org/10.1016/j.procs.2016.08.146 

Korobko A. V., Penkova T. G. On-line analytical processing based on formal concept analysis // Procedia Computer Science. 2010. Vol. 1, Issue 1. P. 2311–2317. doi: https://doi.org/10.1016/j.procs.2010.04.259 

A software tool for the spatiotemporal analysis and reporting of groundwater monitoring data / Jones W. R., Spence M. J., Bowman A. W., Evers L., Molinari D. A. // Environmental Modelling & Software. 2014. Vol. 55. P. 242–249. doi: https://doi.org/10.1016/j.envsoft.2014.01.020 

User-driven design of decision support systems for polycentric environmental resources management / Zulkafli Z., Perez K., Vitolo C., Buytaert W., Karpouzoglou T., Dewulf A. et. al. // Environmental Modelling & Software. 2017. Vol. 88. P. 58–73. doi: https://doi.org/10.1016/j.envsoft.2016.10.012 

Kameneva I. P. Komp'yuternye sredstva ocenivaniya ekologicheskih riskov s ispol'zovaniem strukturnogo analiza dannyh monitoringa // Elektronnoe modelirovanie. 2013. Vol. 35, Issue 6. P. 99–114.

Shevchenko I., Tertyshnyi V., Koval S. Designing a model of a decision support system based on a multi-aspect factographic search // Eastern-European Journal of Enterprise Technologies. 2017. Vol. 4, Issue 2 (88). P. 20–26. doi: https://doi.org/10.15587/1729-4061.2017.108569 

Informatsiyno-tekhnolohichni aspekty upravlinnia ekolohichnoiu bezpekoiu v systemakh munitsypalnoho monitorynhu atmosfernoho povitria / Bakharev V. S., Shevchenko I. V., Koval S. S., Kortsova O. L. // Visnyk KrNU imeni Mykhaila Ostrohradskoho. 2017. Issue 4 (105). P. 68–73.

Some aspects of the analysis of citizens' appeals to municipalities on environmental issues / Bakharev V., Kortsova O., Marenych A., Kyrylaha N., Moroz M. // International Journal of Innovative Science, Engineering & Technology. 2017. Vol. 4, Issue 8. P. 272–278. URL: http://ijiset.com/vol4/v4s8/IJISET_V4_I08_29.pdf







Copyright (c) 2018 Olena Kortsova, Volodymyr Bakharev, Igor Shevchenko, Svitlana Koval

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ISSN (print) 1729-3774, ISSN (on-line) 1729-4061