A Holistic Approach to Early Warning Systems Using an Agent-Based Model

Abstract

Developing an early warning system requires four key components: risk knowledge, hazard detection (including monitoring and forecasting), dissemination (involving decision making and warning issuance), and response (including action implementation). Early warning system (EWS) provides an integrated system to facilitate timely responses to hazards. To assess the effectiveness of an EWS, a systems-based approach that holistically captures its critical components is required. Therefore, this study used a system-based modeling tool, an agent-based model (ABM), to examine the factors influencing evacuation response in a flooding scenario. The model was tested for an area nestled within the Ba catchment in Fiji. Surveys, interviews, and previous literature underpin the development of the model. Evacuation response was examined across key social and physical factors, with the dissemination of warning information kept as the central focus. The findings indicate that timely warnings, coupled with training, substantially improve response outcomes. However, factors such as belief in the warning and flood velocity can undermine outcomes even when warnings are issued promptly. This study underscores the critical need to assess the effectiveness of EWS holistically by accounting for a range of factors, extending beyond forecast development and dissemination.

Keywords

Agent-based model / Disaster response simulation / Flood risk reduction / Early warning systems / Community engagement

References

[1] Aalami S, Kattan L. Fairness and efficiency in pedestrian emergency evacuation: Modeling and simulation. Safety Science, 2020, 121: 373-384

[2] Abebe YA, Ghorbani A, Nikolic I, Manojlovic N, Gruhn A, Vojinovic Z. The role of household adaptation measures in reducing vulnerability to flooding: A coupled agent-based and flood modelling approach. Hydrology and Earth System Sciences, 2020, 24(11): 5329-5354

[3] Akerkar, S., J. Ayers, C. Boswell, M. DeCoster, A. Jimerson, and L. Woodside Alegre. 2020. Early warning for early action: Toward more behaviorally informed early warning systems. Washington, DC: Resilience Evaluation, Analysis and Learning (REAL) Associate Award.

[4] Alexander D. A magnitude scale for cascading disasters. International Journal of Disaster Risk Reduction, 2018, 30: 180-185

[5] Anshuka, A., X. Fu, and I. White. 2025. High water, high stakes: A global review of flood risk and housing price effects. International Journal of Disaster Risk Reduction 122: Article 105452.

[6] Anshuka A, van Ogtrop FF, Sanderson D, Leao SZ. A systematic review of agent-based model for flood risk management and assessment using the ODD protocol. Natural Hazards, 2022, 112(3): 2739-2771

[7] Anshuka, A., F.F. van Ogtrop, D. Sanderson, E. Thomas, and A. Neef. 2021. Vulnerabilities shape risk perception and influence adaptive strategies to hydro-meteorological hazards: A case study of Indo-Fijian farming communities. International Journal of Disaster Risk Reduction 62: Article 102401.

[8] Bañgate, J., J. Dugdale, C. Adam, and E. Beck. 2017. A review on the influence of social attachment on human mobility during crises. Paper presented at the 14th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2017), 21–24 May 2017, Albi, France.

[9] Bizzarri, M., A. de Guttry, G. Venturini, and M. Gestri. 2012. Protection of vulnerable groups in natural and man-made disasters. The Hague, The Netherlands: T. M. C. Asser Press.

[10] Caretta, M.A., A. Mukherji, M. Arfanuzzaman, R.A. Betts, A. Gelfan, Y. Hirabayashi, T.K. Lissner, J. Liu, et al. 2022. Water. In Climate change 2022: Impacts, adaptation and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, ed. D.C.R.H.-O. Pörtner, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, et al., 551–712. Cambridge, UK: Cambridge University Press.

[11] Chandra-Putra H, Andrews CJ. An integrated model of real estate market responses to coastal flooding. Journal of Industrial Ecology, 2020, 24(2): 424-435

[12] Chang H-S, Liao C-H. Planning emergency shelter locations based on evacuation behavior. Natural Hazards, 2015, 76(3): 1551-1571

[13] Chen F, Zhao Q, Cao M, Chen J, Fu G. Adaptive agent-based modeling framework for collective decision-making in crowd building evacuation. Journal of Shanghai Jiao Tong University (Medical Science), 2021, 26(4): 522-533

[14] Collier C. Flash flood forecasting: What are the limits of predictability? Quarterly journal of the royal meteorological society: A journal of the atmospheric sciences. Applied Meteorology and Physical Oceanography, 2007, 133(622): 3-23

[15] Collins, A., M. Koehler, and C. Lynch. 2024. Methods that support the validation of agent-based models: An overview and discussion. Journal of Artificial Societies and Social Simulation 27(1): Article 1.

[16] CRED (Centre for Research on the Epidemiology of Disasters)EM-DAT—The international disaster database, 2023BrusselsCRED

[17] Dashora LK, Basnayake S, Ganepola GAC, Jayasinghe S. Readiness stocktaking of multi-hazard early warning systems in Pacific small Island developing states. Asia-Pacific Sustainable Development Journal, 2024, 31(1): 47-75

[18] Devi, S., Z. Wu, L. Alfonso, and B. Bhattacharya. 2024. Enhancing flash flood warning using a frequentist approach: A case study of Ba catchment in Fiji. Paper presented at the 15th International Conference on Hydroinformatics, 27–30 May 2024, Beijing, China.

[19] Du E, Cai X, Sun Z, Minsker B. Exploring the role of social media and individual behaviors in flood evacuation processes: An agent-based modeling approach. Water Resources Research, 2017, 53(11): 9164-9180

[20] Gaillard JC, Cadag JRD, Rampengan MMF. People’s capacities in facing hazards and disasters: An overview. Natural Hazards, 2019, 95(3): 863-876

[21] Garcia C, Fearnley C. Evaluating critical links in early warning systems for natural hazards. Environmental Hazards, 2012, 11: 123-137

[22] Jenkins K, Surminski S, Hall J, Crick F. Assessing surface water flood risk and management strategies under future climate change: Insights from an agent-based model. Science of the Total Environment, 2017, 595: 159-168

[23] Kasmalkar, I., D. Wagenaar, A. Bill-Weilandt, J. Choong, S. Manimaran, T.N. Lim, M. Rabonza, and D. Lallemant. 2024. Flow-tub model: A modified bathtub flood model with hydraulic connectivity and path-based attenuation. MethodsX 12: Article 102524.

[24] Kelman I, Glantz MHSingh A, Zommers Z. Early warning systems defined. Reducing disaster: Early warning systems for climate change, 2014ChamSpringer89-108

[25] Ligmann-Zielinska, A., D.B. Kramer, K.S. Cheruvelil, and P.A. Soranno. 2014. Using uncertainty and sensitivity analyses in socioecological agent-based models to improve their analytical performance and policy relevance. PLoS One 9(10): Article e109779.

[26] Liu X, Lim S. An agent-based evacuation model for the 2011 Brisbane city-scale riverine flood. Natural Hazards, 2018, 94(1): 53-70

[27] Macal C, North M. Tutorial on agent-based modelling and simulation. Journal of Simulation, 2010, 4: 151-162

[28] Mariam I, Budhiana J, Permana I, Dewi R, Rahmanishati W, Noviyanti L, Utami RN, Sanjaya W, et al.. Knowledge, attitudes, disaster training and self efficacy on disaster preparedness. Research Horizon, 2021, 1(5): 179-188

[29] McAneney J, van den Honert R, Yeo S. Stationarity of major flood frequencies and heights on the Ba River, Fiji, over a 122-year record. International Journal of Climatology, 2017, 37(S1): 171-178

[30] Metherall, N., A.M. Vinaka, S. Beavis, and E.A. Holland. 2021. Characterisation of pH variations along the Ba River in Fiji utilising the GEF R2R framework during the 2019 sugarcane season. Environmental Monitoring and Assessment 193(12): Article 828.

[31] Šakić Trogrlić R, van den Homberg M, Budimir M, McQuistan C, Sneddon A, Golding BGolding B. Early warning systems and their role in disaster risk reduction. Towards the “perfect” weather warning: Bridging disciplinary gaps through partnership and communication, 2022ChamSpringer11-46

[32] Sanders BF, Wing OEJ, Bates PD. Flooding is not like filling a bath. Earth’s Future, 2024, 12(12): 1-7

[33] Shirvani M, Kesserwani G, Richmond P. Agent-based modelling of pedestrian responses during flood emergency: Mobility behavioural rules and implications for flood risk analysis. Journal of Hydroinformatics, 2020, 22(5): 1078-1092

[34] Shirvani, M., G. Kesserwani, and P. Richmond. 2021. Agent-based simulator of dynamic flood-people interactions. Journal of Flood Risk Management 14(2): Article e12695.

[35] UNDRR (United Nations Office for Disaster Risk Reduction) and WMO (World Meteorological Organization)Global status of multi-hazard early warning systems, 2023GenevaUNDRR

[36] Villamor, G.B., M. van Noordwijk, and K.G. Troitzsch. 2023. Triangulating agent-based models, role-playing games, and a stakeholder-centric approach to change scenarios. Current Opinion in Environmental Sustainability 64: Article 101323.

[37] Wang, Z., J. Huang, H. Wang, J. Kang, and W. Cao. 2020. Analysis of flood evacuation process in vulnerable community with mutual aid mechanism: An agent-based simulation framework. International Journal of Environmental Research and Public Health 17(2): Article 560.

[38] Wang H, Mostafizi A, Cramer LA, Cox D, Park H. An agent-based model of a multimodal near-field tsunami evacuation: Decision-making and life safety. Transportation Research Part C: Emerging Technologies, 2016, 64: 86-100

[39] World Bank. 2018. Fiji – Historical natural disasters. Washington, DC: Climate Change Knowledge Portal, World Bank.https://climateknowledgeportal.worldbank.org/country/fiji/natural-disasters-historical. Accessed 19 Nov 2025.

[40] Yang LE, Scheffran J, Süsser D, Dawson R, Chen YD. Assessment of flood losses with household responses: Agent-based simulation in an urban catchment area. Environmental Modeling & Assessment, 2018, 23(4): 369-388

[41] Yila O, Weber E, Neef A, Shaw R, Neef AThe role of social capital in post-flood response and recovery among downstream communities of the Ba River, Western Viti Levu, Fiji Islands, 2013BingleyEmerald Group Publishing Limited

[42] Yore R, Walker JF. Early warning systems and evacuation: Rare and extreme versus frequent and small-scale tropical cyclones in the Philippines and Dominica. Disasters, 2021, 45(3): 691-716

[43] Zhuo, L., and D. Han. 2020. Agent-based modelling and flood risk management: A compendious literature review. Journal of Hydrology 591: Article 125600.

Funding

Auckland University of Technology

RIGHTS & PERMISSIONS

The Author(s)