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A dynamic visualization based on conceptual graphs to capture the knowledge for disaster education on floods

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Abstract

Enhancing the capacity and awareness of individuals in disaster prevention and mitigation requires an intuitive and comprehensible method for representing flood hazard education knowledge. To address the challenges of complex information transfer and limited knowledge expression in flood disaster education, this paper proposes a novel strategy. The approach utilizes conceptual graphs to organize and guide the visual representation of flood disaster knowledge. It involves connecting flood data and knowledge elements using concept nodes and relationships, and translating them into dynamic visual representations through instantiation methods. A prototype system was developed to visualize disaster data obtained from flood-affected areas. The visualization output was compared to expert-based reports using a questionnaire, focusing on attractiveness and comprehensibility. The results demonstrated the superiority of our approach, with higher scores of 0.433 and 0.22 (on a scale of 0–1) for attractiveness and comprehensibility, respectively. This highlights the effectiveness of our approach in displaying flood knowledge and facilitating its dissemination. In summary, this paper introduces a comprehensive and dynamic visualization approach for the entire flood process, integrating relevant disaster knowledge. It presents a fresh perspective on digital disaster education tailored to floods, aiming to enhance public awareness of flood risk prevention.

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References

  • Asbridge EF, Choy L, Mackey D, Serrao-Neumann B, Taygfeld S, Rogers K (2021) Coastal flood risk within a peri-urban area: SUSSEX Inlet district, SE Australia. Nat Hazards 109(1):999–1026

    Article  Google Scholar 

  • Bakhtyarikia M, Pirasteh S, Pradhan B, Ahmad RM, Wan NAS, Moradi A (2012) An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ Earth Sci Environ Earth Sci 67:251–264

    Article  Google Scholar 

  • Cañas AJ, Carff R, Hill G, Carvalho M, Arguedas M, Eskridge TC, Carvajal R (2005) Concept maps: integrating knowledge and information visualization. Lect Notes Comput Sci 3426:205–219

    Article  Google Scholar 

  • Cornel D, Buttinger-Kreuzhuber A, Konev A, Horváth Z, Wimmer M, Heidrich R, Waser J (2019) Interactive visualization of flood and heavy rain simulations. Comput Graphics Forum 38(3):25–39

    Article  Google Scholar 

  • Demir I, Krajewski WF (2013) Towards an integrated flood information system: centralized data access, analysis, and visualization. Environ Modell Softw 50:77–84

    Article  Google Scholar 

  • Devitt C, O’Neill E (2017) The framing of two major flood episodes in the irish print news media: implications for societal adaptation to living with flood risk. Public Underst Sci 26(7):872–888

    Article  Google Scholar 

  • Ding W, Wu J, Tang R, Chen X, Xu Y (2022) A review of flood risk in China during 1950–2019: urbanization, socioeconomic impact trends and flood risk management. Water 14(20):3246–3263

    Article  Google Scholar 

  • Dottori F, Szewczyk W, Ciscar JC, Zhao F, Alfieri L, Hirabayashi Y, Feyen L (2018) Increased human and economic losses from river flooding with anthropogenic warming. Nat Clim Change 8(9):781–786

    Article  Google Scholar 

  • Douben KJ (2006) Characteristics of river floods and flooding: a global overview, 1985–2003. Irrig Drain J Int Comm Irrig Drain 55(S1):S9–S21

    Google Scholar 

  • Eppler MJ, Burkhard RA (2008) Knowledge visualization. In: Knowledge Management: concepts, methodologies, tools, and applications, Pennsylvania, USA, IGI Global, pp 781–793

  • Fadiran OA, Van Biljon J, Schoeman MA (2020) Knowledge transfer in science education: the case for usability-based knowledge visualization guidelines. In Responsible Design, implementation and use of information and communication technology: 19th IFIP WG 6.11 conference on e-business, e-services, and e-society, I3E 2020, Skukuza, South Africa, April 6–8, 2020, Proceedings, Part II 19, Springer International Publishing, pp. 263–273

  • Farjad B, Helmi ZMS, Thamer AM, Pirasteh S (2012) Intrinsic vulnerability and risk mapping. Water Manag 165(8):441–450

    Google Scholar 

  • Farooq M, Shafique M, Khattak MS (2019) Flood hazard assessment and mapping of River Swat using HEC-RAS 2D model and high-resolution 12-m TanDEM-X DEM (WorldDEM). Nat Hazards 97(2):477–492

    Article  Google Scholar 

  • Fu L, Zhu J, Lai J, Li W, Dang P, Yin L, You J (2022) A three-dimensional visualization and optimization method of landslide disaster scenes guided by knowledge. ISPRS Int J Geo-Inf 11(6):340

    Article  Google Scholar 

  • Guo L, He B, Ma M, Chang Q, Li Q, Zhang K, Yang H (2019) A comprehensive flash flood defense system in China: overview, achievements, and outlook. Nat Hazards 99(2):1191–1191

    Article  Google Scholar 

  • Hadimlioglu IA, King SA, Starek MJ (2020) FloodSim: flood simulation and visualization framework using position-based fluids. ISPRS Int J Geo-Inf 9(3):163

    Article  Google Scholar 

  • Hammond MJ, Chen AS, Djordjević S, Butler D, Mark O (2015) Urban flood impact assessment: a state-of-the-art review. Urban Water J 12(1):14–29

    Article  Google Scholar 

  • Hofer B, Mäs S, Brauner J, Bernard L (2017) Towards a knowledge base to support geoprocessing workflow development. Int J Geogr Inf Sci 31(4):694–716

    Article  Google Scholar 

  • Islam R, Kamaruddin R, Ahmad SA, Jan S, Anuar AR (2016) A review on mechanism of flood disaster management in Asia. Int Rev Manag Mark 6(1):29–52

    Google Scholar 

  • Islam MR, Ingham V, Hicks J, Kelly E (2018) From coping to adaptation: flooding and the role of local knowledge in Bangladesh. Int J Disast Risk Res 28:531–538

    Google Scholar 

  • Kamsu-Foguem B, Tchuenté-Foguem G, Foguem C (2014) Using conceptual graphs for clinical guidelines representation and knowledge visualization. Inf Syst Front 16:571–589

    Article  Google Scholar 

  • Li Y, Gong J, Liu H, Zhu J, Song Y, Liang J (2015) Real-time flood simulations using CA model driven by dynamic observation data. Int J Geogr Inf Sci 29(4):523–535

    Article  Google Scholar 

  • Li W, Zhu J, Fu L, Zhu Q, Xie Y, Hu Y (2021) An augmented representation method of debris flow scenes to improve public perception. Int J Geogr Inf Sci 35(8):1521–1544

    Article  Google Scholar 

  • Li J, Xia H, Qin Y, Fu P, Guo X, Li R, Zhao X (2022a) Web GIS for sustainable education: towards natural disaster education for high school students. Sustainability 14(5):2694

    Article  Google Scholar 

  • Li W, Zhu J, Pirasteh S, Zhu Q, Fu L, Wu J (2022b) Investigations of disaster information representation from geospatial perspective: progress, challenges, and recommendations. Trans GIS. https://doi.org/10.1111/tgis.12922

    Article  Google Scholar 

  • Mahmoud SH, Gan TY (2018) Urbanization and climate change implications in flood risk management: developing an efficient decision support system for flood susceptibility mapping. Sci Total Environ 636:152–167

    Article  Google Scholar 

  • Mallick J, Salam R, Amin R, Islam ARMT, Islam A, Siddik MNA, Alam GM (2022) Assessing factors affecting drought, earthquake, and flood risk perception: empirical evidence from Bangladesh. Nat Hazards 112(2):1633–1656

    Article  Google Scholar 

  • Marchi L, Borga M, Preciso E, Gaume E (2010) Characterisation of selected extreme flash floods in Europe and implications for flood risk management. J Hydrol 394(1–2):118–133

    Article  Google Scholar 

  • McWilliam A, Wasson RJ, Rouwenhorst J, Amaral AL (2020) Disaster risk reduction, modern science and local knowledge: perspectives from Timor-Leste. Int J Disast Risk Res 50:101641

    Google Scholar 

  • Merz B, Blöschl G, Vorogushyn S, Dottori F, Aerts JC, Bates P, Macdonald E (2021) Causes, impacts and patterns of disastrous river floods. Nat Rev Earth Environ 2:592

    Article  Google Scholar 

  • Mustafa A, Bruwier M, Archambeau P, Erpicum S, Pirotton M, Dewals B, Teller J (2018) Effects of spatial planning on future flood risks in urban environments. J Environ Manag 225:193–204

    Article  Google Scholar 

  • Ostad-Ali-Askari K (2022a) Investigation of meteorological variables on runoff archetypal using SWAT: basic concepts and fundamentals. Appl Water Sci 12(8):177

    Article  Google Scholar 

  • Ostad-Ali-Askari K (2022b) Developing an optimal design model of furrow irrigation based on the minimum cost and maximum irrigation efficiency. Appl Water Sci 12(7):144

    Article  Google Scholar 

  • Raungratanaamporn IS, Pakdeeburee P, Kamiko A, Denpaiboon C (2014) Government-communities collaboration in disaster management activity: investigation in the current flood disaster management policy in Thailand. Proc Environ Sci 20:658–667

    Article  Google Scholar 

  • Sakurai M, Shaw R (2022) The potential of digitally enabled disaster education for sustainable development goals. Sustainability 14(11):65–68

    Article  Google Scholar 

  • Seipel S, Lim NJ (2017) Color map design for visualization in flood risk assessment. Int J Geogr Inf Sci 31(11):2286–2309

    Article  Google Scholar 

  • Sermet Y, Demir I (2018) An intelligent system on knowledge generation and communication about flooding. Environ Modell Softw 108:51–60

    Article  Google Scholar 

  • Shah AA, Ye J, Pan L, Ullah R, Shah SIA, Fahad S, Naz S (2018) Schools’ flood emergency preparedness in Khyber Pakhtunkhwa province, Pakistan. Int J Disast Risk Sci 9:181–194

    Article  Google Scholar 

  • Shiwaku K, Shaw R, Chandra Kandel R, Shrestha NS, Dixit M (2007) A Future perspective of school disaster education in Nepal. Disast Prev Manag Int J 16(4):576–587

    Article  Google Scholar 

  • Shoji M, Takafuji Y, Tetsuya Harada (2020) Formal education and disaster response of children: evidence from coastal villages in Indonesia. Nat Hazards 103(2):2183–2205

    Article  Google Scholar 

  • Tanoue M, Hirabayashi Y, Ikeuchi H (2016) Global-scale river flood vulnerability in the last 50 years. Sci Rep 6(1):1–9

    Article  Google Scholar 

  • UNISDR (United Nations International Strategy for Disaster Reduction) (2015) Sendai framework for disaster risk reduction 2015–2030. UNISDR, Geneva

  • Wang C, Hou J, Miller D, Brown I, Jiang Y (2019) Flood risk management in sponge cities: the role of integrated simulation and 3D visualization. Int J Disast Risk Res 39:101139

    Google Scholar 

  • Wu H, Zhu Q, Guo Y, Zheng W, Zhang L, Wang Q, Zho R, Ding Y, Wang W, Pirasteh S, Liu M (2022) Multi-level voxel representations for digital twin models of tunnel geological environment. Int J Appl Earth Obs 112:102887

    Google Scholar 

  • Shamsipour PS, Liu G, Hajibagher G, Ghasemzadeh R, Jokar A, Zarbakhsh M, Behnamfar HF, Li J (2020) Cloud-based geospatial platform in support of sustainable development goals 2030: how to be prepared for earthquake disasters? XXIV ISPRS Congress, June 14–20, 2020, Nice, France. https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1705-2020

  • Youssef AM, Abu-Abdullah MM, AlFadail EA, Skilodimou HD, Bathrellos GD (2021) The devastating flood in the arid region a consequence of rainfall and dam failure: case study, Al-Lith flood on 23th November 2018, Kingdom of Saudi Arabia. Z Für Geomorphol 63:115–136

    Article  Google Scholar 

  • Yuan M (2020) Why are events important and how to compute them in geospatial research? J Spat Inform Sci 21:47–61

    Google Scholar 

  • Yusoff Z, Katmon SA, Ahmad MN, Miswan SHM (2013) Visual representation: enhancing students’ learning engagement through knowledge visualization. In: 2013 international conference on informatics and creative multimedia. pp 242–247

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Funding

This paper was funded by National Natural Science Foundation of China (grant number 41941019 and U2034202 ), the Sichuan Science and Technology Program (grant number 2020JDTD0003) and A Study on the Enhancement of Interdisciplinary Innovative Ability of Graduate Students in a Large Geoscience Program (YJG5-2022-Z015).

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Authors and Affiliations

  1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China

    Yukun Guo, Jun Zhu, Jigang You, Saied Pirasteh, Jianlin Wu, Jianbo Lai & Pei Dang

  2. Institute of Geodesy and Geoinformation, University of Bonn, Bonn, Germany

    Weilian Li

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  1. Yukun Guo
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  2. Jun Zhu
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  3. Jigang You
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  4. Saied Pirasteh
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  5. Weilian Li
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  7. Jianbo Lai
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Correspondence to Jun Zhu.

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Guo, Y., Zhu, J., You, J. et al. A dynamic visualization based on conceptual graphs to capture the knowledge for disaster education on floods. Nat Hazards 119, 203–220 (2023). https://doi.org/10.1007/s11069-023-06128-1

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