EGU General Assembly 2023
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the Creative Commons Attribution 4.0 License.

Risk-based Decision Support System for Early Warning of Chemical Emissions in Flood Event: A Case Study of Crystallization Factories in Liberec City, Czech Republic 

Mohamad Gheibi1, Masoud Khaleghiabbasabadi2, Barbara Socha2, Stanisław Wacławek2, and Miroslav Černík2
Mohamad Gheibi et al.
  • 1Department of Civil Engineering, Ferdowsi University of Mashhad , Mashhad, Iran
  • 2Institute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, Studentská 1402/2, 461 17 Liberec 1, Czech Republic

Based on the reports presented in various studies, it appears that in the Czech Republic, in 2002, as a result of a fluvial flood, chlorine and other chemicals in a factory were washed away and led to various epidemiological effects [1]. This paper presents a Decision Support System (DSS) based on Random Forest (RF) artificial intelligence technique and Failure Modes and Effects Analysis (FMEA) [2] to minimize the chemical risks in industrial factories and control possible pollution from crystallization plants. The methodology is demonstrated by its application on real crystallization plant in Liberec, Czech Republic. The investigations demonstrated that the RF algorithm has the ability to predict the severity of the occurrence and the Risk Probability Number (RPN) of spreading pollution with more than 90% regression coefficient. On the other hand, combining the machine learning method with the risk analysis has the possibility of heavy metal emission risk detection as well as the presentation of available solutions using the classic Delphi technique [3,4]. The evaluations of this research proved that the proposed methdology can significantly increase the biological security of citizens in crisis conditions.

Keywords: Flood; Decision Support System; Machine Learning; Risk Analysis; Czech Republic.



[1] Gautam, K.P. and Van Der Hoek, E.E., 2003. Literature study on environmental impact of floods. DC1-233-13.

[2] Gheibi, M., Karrabi, M. and Eftekhari, M., 2019. Designing a smart risk analysis method for gas chlorination units of water treatment plants with combination of Failure Mode Effects Analysis, Shannon Entropy, and Petri Net Modeling. Ecotoxicology and Environmental Safety, 171, pp.600-608.

[3] Zabihi, O., Siamaki, M., Gheibi, M., Akrami, M. and Hajiaghaei-Keshteli, M., 2023. A smart sustainable system for flood damage management with the application of artificial intelligence and multi-criteria decision-making computations. International Journal of Disaster Risk Reduction, 84, p.103470.

[4] Akbarian, H., Gheibi, M., Hajiaghaei-Keshteli, M. and Rahmani, M., 2022. A hybrid novel framework for flood disaster risk control in developing countries based on smart prediction systems and prioritized scenarios. Journal of environmental management, 312, p.114939.

How to cite: Gheibi, M., Khaleghiabbasabadi, M., Socha, B., Wacławek, S., and Černík, M.: Risk-based Decision Support System for Early Warning of Chemical Emissions in Flood Event: A Case Study of Crystallization Factories in Liberec City, Czech Republic , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9622,, 2023.