Research on the Interest of Local Governments in the Use of Artificial Intelligence in Flood Risk Management in Poland

• Authors: Artur Wrzalik

Title variants

PL

Analiza stopnia zainteresowania samorządów lokalnych wykorzystaniem sztucznej inteligencji w zarządzaniu ryzykiem powodziowym w Polsce

Abstracts

PL

Celem artykułu jest analiza stopnia zainteresowania implementacją metod sztucznej inteligencji w obszarze zarządzania kryzysowego związanego z ryzykiem powodziowym w jednostkach samorządu terytorialnego w Polsce. Badania, mające charakter pilotażowy, przeprowadzono z wykorzystaniem kwestionariusza ankietowego i bezpośredniego wywiadu telefonicznego w 47 wybranych samorządach lokalnych zlokalizowanych w Polsce. Respondenci dokonali oceny obecnego systemu zarządzania kryzysowego i odnieśli się do możliwości wykorzystania rozwiązań sztucznej inteligencji na analizowanym obszarze. Na podstawie uzyskanych wyników można stwierdzić, że badane podmioty wykazują duże zainteresowanie proponowanym rozwiązaniem i mają wobec niego określone oczekiwania. Warto podkreślić, że większość prac poświęconych zarządzaniu ryzykiem powodziowym w Polsce koncentruje się na rozwiązaniach przeznaczonych na poziom krajowy, a nie lokalny. W artykule podjęto próbę wypełnienia tej luki badawczej.

EN

The aim of the article was to analyse the degree of interest in the implementation of artificial intelligence methods in the area of crisis management related to flood risk in local government units in Poland. Pilot studies were carried out with the use of a questionnaire and direct telephone interview in 47 selected local governments located in Poland. The respondents assessed the current crisis management system and referred to the possibility of using artificial intelligence solutions in the analysed area. On the basis of the obtained results, it can be concluded that the surveyed entities show significant interest in the proposed solution and have specific expectations towards them. Most of the works devoted to flood risk management in Poland focus on solutions intended for the national level, not local one. The article aimed to highlight the need to fill this research gap.

Keywords

EN

crisis management; flood risk management; artificial intelligence

PL

zarządzanie kryzysowe; zarządzanie ryzykiem powodziowym; sztuczna inteligencja

• Publisher: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
• Journal: Informatyka Ekonomiczna
• Year: 2021
• Issue: 3
• Pages: 58-72

Dates

published

2021

Contributors

author

Artur Wrzalik

• Czestochowa University of Technology

References

• Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., and Arshad, H. (2018). State-of-the-art in artificial neural network applications: a survey. Heliyon, 4. doi:10.1016/j.heliyon.2018.e00938
• Begg, C., Callsen, I., Kuhlicke, C., and Kelman, I. (2018). The role of local stakeholder participation in flood defence decisions in the United Kingdom and Germany. Journal of Flood Risk Management, 11, 180-190. doi:10.1111/jfr3.12305
• Chatziioannou, I., Bakogiannis, E., Kyriakidis, C., and Alvarez-Icaza, L. (2020). A prospective study for the mitigation of the climate change effects: the case of the North Aegean region of Greece. Sustainability, 12(24), 10420. doi:10.3390/su122410420
• Da Silva, L. B. L., Alencar, M. H., and De Almeida, A. T. (2020). Multidimensional flood risk management under climate changes: bibliometric analysis, trends and strategic guidelines for decision-making in urban dynamics. International Journal of Disaster Risk Reduction, 50, 101865. doi:10.1016/j.ijdrr.2020.101865
• Dernoncourt, F. (2013). Introduction to fuzzy logic. Massachusett Institute of Technology. Retrieved November 10, 2021 from https://www.academia.edu/download/39356551/fuzzy_logic.pdf
• Dumienski, G., Lisowska, A., Bedryj, M., and Tiukalo, A. (2017). Assessment of the state's sustainable flood policy based on the integrated environmental risk. In R.F. Sadowski, and Z. Lepko (Eds.), Theoria i Praxis zrównowazonego rozwoju. 30 lat od opublikowania Raportu Brundtland (pp. 579-590). Warszawa: Towarzystwo Naukowe Franciszka Salezego.
• Fotovatikhah, F., Herrera, M., Shamshirband, S., Chau, K., Ardabili, S. F., and Piran, M. J. (2018). Survey of computational intelligence as the basis for big flood management: challenges, research directions and future work. Engineering Applications of Computational Fluid Mechanics, 12, 411- -437. doi:10.1080/19942060.2018.1448896
• Iqbal, U., Perez, P., Li, W., and Barthelemy, J. (2021). How computer vision can facilitate flood management: a systematic review. International Journal of Disaster Risk Reduction. doi:10.1016/j.ij drr.2020.102030
• Jarosinska, E. (2016). Local flooding in the USA, Europe, and Poland - an overview of strategies and actions in face of climate change and urbanisation. Infrastructure and Ecology of Rural Areas, 3. doi:10.14597/infraeco.2016.3.1.059
• Kejriwal, M., and Zhou, P. (2019, August 27-30). SAVIZ: Interactive exploration and visualization of situation labeling classifiers over crisis social media data (International Conference on Advances in Social Networks Analysis and Mining). Vancouver, 705-708.
• Khalil, K. M., Abdel-Aziz, M., Nazmy, T. T. and Salem, A.-B. M. (2008). The role of artificial intelligence technologies in crisis response. CoRR, abs/0806.1280.
• Madhuri, R., Sistla, S., and Srinivasa Raju, K. (2021). Application of machine learning algorithms for flood susceptibility assessment and risk management. Journal of Water and Climate Change, 12, 2608-2623. doi:10.2166/wcc.2021.051
• McCallum, I., Liu, W., See, L., Mechler, R., Keating, A., Hochrainer-Stigler, S., ... Moorthy, I. (2016). Technologies to support community flood disaster risk reduction. International Journal of Disaster Risk Science, 7. doi:10.1007/s13753-016-0086-5
• Mosavi, A., Ozturk, P., and Kwok-Wing, C. (2018). Flood prediction using machine learning models: Literature review. Water, 10. doi:10.3390/w10111536
• Nguyen, D. T., Joty, S., Imran, M., Sajjad, H., and Mitra, P. (2016). Applications of online deep learning for crisis response using social media information. arXiv preprint, arXiv:1610.01030
• Pham, B. T., Luu, C., Phong, T. V., Nguyen, H. D., Le, H. V., Tran, T. Q., Ta, H. T., and Praksa, I. (2021). Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam. Journal of Hydrology, 592. doi:10.1016/j.jhydrol.2020.125815
• Ruiz-Mezcua, B., Garcia-Crespo, A., Lopez-Cuadrado, J. L., and Gonzalez-Carrasco, I. (2011). An expert system development tool for non AI experts. Expert Systems with Applications, 38, 597-609. doi:10.1016/j.eswa.2010.07.009
• Saaty, T. L. (2004). Decision making - the Analytic Hierarchy and Network Processes (AHP/ANP). Journal of Systems Science and Systems Engineering, 13. doi:10.1007/S11518-006-0151-5
• Sayersa, W., Savića, D., Kapelana, Z., and Kellagherb, R. (2014). Artificial intelligence techniques for flood risk management in the urban environment. Procedia Engineering, 70, 1505-1512. doi:10.1016/j.proeng.2014.02.165
• Sun, W., Bocchini, P., and Davison, B. D. (2020). Applications of artificial intelligence for disaster management. Natural Hazards, 103. doi:10.1007/s11069-020-04124-3
• Thaler, T. and Levin-Keitel, M. (2016). Multi-level stakeholder engagement in flood risk management - a question of roles and power: lessons from England. Environmental Science & Policy, 55(2), 292-301. doi:10.1016/j.envsci.2015.04.007
• Wagner, W. P. (2017). Trends in expert system development: a longitudinal content analysis of over thirty years of expert system case studies, Expert Systems with Application, 17, 85-96. doi:10.1016/ j.eswa.2017.01.028
• Wehn, U., Rusca, M., Evers, J., and Lanfranchi, V. (2015). Participation in flood risk management and the potential of citizen observatories: a governance analysis. Environmental Science & Policy, 48, 225-236. doi:10.1016/j.envsci.2014.12.017
• Wrzalik, A. and Jereb, B. (2019) Use of expert systems in crisis management. In R. Ulewicz, and R. R. Nikolic (eds.) System safety: Human - technical facility - environment. Warsaw: De Gruyter Poland. doi:10.2478/czoto-2019-0052
• Wu, B. (1991). An introduction to neural networks and their applications in manufacturing. Journal of Intelligence Manufacturing, 3. doi:10.1007/BF01473534
• Yuan, C., Zhang, Y., and Liu, Z. (2015). A survey on technologies for automatic forest fire monitoring, detection and fighting using uavs and remote sensing techniques. Canadian Journal of Forest Research, 45. doi:10.1139/cjfr-2014-0347
• Zadeh, L. A. (1975). The concept of a linguistic variable to approximate reasoning - II. Information Sciences, 8. doi:10.1016/0020-0255(75)90046-8

Identifiers

DOI

10.15611/ie.2021.3.06

Biblioteka Nauki

2033018