Analysing the effect of a dynamic physical environment network on the travel dynamics of forcibly displaced persons in Mali

Boesjes Freek; Jahani Alireza; Ooink Bas; Derek Groen

doi:10.53941/ijndi.2024.100003

Abstract

As of 2023, the world has approximately 100 million refugees, many of whom have been displaced by violent conflicts. Accurately predicting where these people may go can help non-government organisations (NGOs) and other support organisations to more effectively help these refugees. In this paper, we extend the existing flee migration forecasting model which models migration using intelligent agents with a dynamic network that represents the physical environment. In doing so, we integrate time-dependent data into four different characteristics from three public data sources. We obtain data from aspects such as the slope, drainage, soil and infrastructure, and use these aspects to systematically modify the movement preferences of forcibly displaced agents in the flee model. We showcase our approach by applying it to the 2012 northern Mali conflict. We find that numerous routes previously deemed traversable are actually inaccessible for prolonged periods according to sensor data, and a range of off-road routes are instead traversable for vehicles. We also perform a validation comparison with the original modelling approach, and find that our revised representation of travel routes leads to a reduction of 4.5% in the averaged relative difference. Our approach can be reused in other flee conflict contexts, of which five are present in the EU-funded ITFLOWS project alone. Our work provides the ability to represent a dynamic physical environment and potentially improves the simulation accuracy in a range of flee conflict situations.

References

1.
Abel, G.J.; Brottrager, M.; Cuaresma, J.C.; et al. Climate, conflict and forced migration. Global Environ. Change, 2019, 54: 239−249. doi: 10.1016/j.gloenvcha.2018.12.003
2.
Mach, K.J.; Kraan, C.M.; Adger, W.N.; et al. Climate as a risk factor for armed conflict. Nature, 2019, 571: 193−197. doi: 10.1038/s41586-019-1300-6
3.
Suleimenova, D.; Bell, D.; Groen, D. A generalized simulation development approach for predicting refugee destinations. Sci. Rep., 2017, 7: 13377. doi: 10.1038/s41598-017-13828-9
4.
Anderson, J.; Chaturvedi, A.; Cibulskis, M. Simulation tools for developing policies for complex systems: Modeling the health and safety of refugee communities. Health Care Manage. Sci., 2007, 10: 331−339. doi: 10.1007/s10729-007-9030-y
5.
Frydenlund, E.; Foytik, P.; Padilla, J.J.; et al. Where are they headed next? Modeling emergent displaced camps in the DRC using agent-based models. In Proceedings of 2018 Winter Simulation Conference (WSC), Gothenburg, Sweden, 09–12 December 2018; IEEE: New York, 2018; pp. 22–32. doi: 10.1109/WSC.2018.8632555
6.
Sokolowski, J.A.; Banks, C.M.; Hayes, R.L. Modeling population displacement in the Syrian city of Aleppo. In Proceedings of the Winter Simulation Conference 2014, Savannah, GA, USA, 07-10 December 2014; IEEE: New York, 2014; pp. 252–263. doi: 10.1109/WSC.2014.7019893
7.
Sokolowski, J.A.; Banks, C.M. A methodology for environment and agent development to model population displacement. In Proceedings of the 2014 Symposium on Agent Directed Simulation, Tampa, Florida, 13 April 2014; Society for Computer Simulation International: San Diego, 2014; p. 3. doi: 10.5555/2665049.2665052
8.
Termos, A.; Picascia, S.; Yorke-Smith, N. Agent-based simulation of west asian urban dynamics: Impact of refugees. J. Artif. Soc. Soc. Simul., 2021, 24: 2. doi: 10.18564/jasss.4472
9.
Bayraktar, O.B.; Günneç, D.; Salman, F.S.; et al. Relief aid provision to en route refugees: Multi-period mobile facility location with mobile demand. Eur. J. Oper. Res., 2022, 301: 708−725. doi: 10.1016/j.ejor.2021.11.011
10.
Xue, Y.N.; Li, M.Q.; Arabnejad, H.; et al. Camp location selection in humanitarian logistics: A multiobjective simulation optimization approach. In Proceedings of the 22nd International Conference on Computational Science, London, UK, 21–23 June 2022; Springer: Berlin/Heidelberg, Germany, 2022; pp. 497–504. doi: 10.1007/978-3-031-08757-8_42
11.
De Kock, C. A framework for modelling conflict-induced forced migration according to an agent-based approach. 2019. Available online: https://scholar.sun.ac.za:443/handle/10019.1/107038
12.
Hoffmann Pham, K.; Luengo-Oroz, M. Predictive modelling of movements of refugees and internally displaced people: Towards a computational framework. J. Ethnic Migr. Stud., 2023, 49: 408−444. doi: 10.1080/1369183X.2022.2100546
13.
Searle, C.; van Vuuren, J.H. Modelling forced migration: A framework for conflict-induced forced migration modelling according to an agent-based approach. Comput. Environ. Urban Syst., 2021, 85: 101568. doi: 10.1016/j.compenvurbsys.2020.101568
14.
Helbing, D.; Buzna, L.; Johansson, A.; et al. Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions. Transp. Sci., 2005, 39: 1−24. doi: 10.1287/trsc.1040.0108
15.
Thober, J.; Schwarz, N.; Hermans, K. Agent-based modeling of environment-migration linkages: A review. Ecol. Soc., 2018, 23: 41.
16.
Carammia, M.; Iacus, S.M.; Wilkin, T. Forecasting asylum-related migration flows with machine learning and data at scale. Sci. Rep., 2022, 12: 1457. doi: 10.1038/s41598-022-05241-8
17.
Huynh, B.Q.; Basu, S. Forecasting internally displaced population migration patterns in Syria and Yemen. Disaster Med. Public Health Prep., 2020, 14: 302−307. doi: 10.1017/dmp.2019.73
18.
Frydenlund, E. Modeling and simulation as a bridge to advance practical and theoretical insights About forced migration studies. J. Migr. Human Secur., 2021, 9: 165−181. doi: 10.1177/23315024211035771
19.
Suleimenova, D.; Arabnejad, H.; Edeling, W.N.; et al. Sensitivity-driven simulation development: A case study in forced migration. Phil. Trans. Roy. Soc. A: Math., Phys. Eng. Sci., 2021, 379: 20200077. doi: 10.1098/rsta.2020.0077
20.
Gore, R.; Wozny, P.; Dignum, F.P.M.; et al. A value sensitive ABM of the refugee crisis in the Netherlands. In Proceedings of 2019 Spring Simulation Conference (SpringSim), Tucson, AZ, USA, 29 April 2019 - 02 May 2019; IEEE: New York, 2019; pp. 1–12. doi: 10.23919/SpringSim.2019.8732867
21.
Boshuijzen-Van Burken, C.; Gore, R.J.; Dignum, F.; et al. Agent-based modelling of values: The case of value sensitive design for refugee logistics. J. Artif. Soc. Soc. Simul., 2020, 23: 6. doi: 10.18564/jasss.4411
22.
Groen, D. Simulating refugee movements: Where would you go. Procedia Comput. Sci., 2016, 80: 2251−2255. doi: 10.1016/j.procs.2016.05.400
23.
Suleimenova, D.; Groen, D. How policy decisions affect refugee journeys in South Sudan: A study using automated ensemble simulations. J. Artif. Soc. Soc. Simul., 2020, 23: 2. doi: 10.18564/jasss.4193
24.
Anastasiadis, P.; Gogolenko, S.; Papadopoulou, N.; et al. P-flee: An efficient parallel algorithm for simulating human migration. In Proceedings of 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Portland, OR, USA, 17–21 June 2021; IEEE: New York, 2021; pp. 1008–1011. 10.1109/IPDPSW52791.2021.00159
25.
Groen, D.; Papadopoulou, N.; Anastasiadis, P.; et al. Large-scale parallelization of human migration simulation. IEEE Trans. Comput. Soc. Syst. 2023 , in press. doi: 10.1109/TCSS.2023.3292932
26.
Jahani, A.; Arabnejad, H.; Suleimanova, D.; et al. Towards a coupled migration and weather simulation: South Sudan conflict. In Proceedings of the 21st International Conference on Computational Science, Krakow, Poland, 16–18 June 2021; Springer: Berlin/Heidelberg, Germany, 2021; pp. 502–515. doi: 10.1007/978-3-030-77977-1_40
27.
Bencherif, A.; Campana, A.; Stockemer, D. Lethal violence in civil war: Trends and micro-dynamics of violence in the northern Mali Conflict (2012–2015). Stud. Conflict. Terrorism, 2023, 46: 659−681. doi: 10.1080/1057610X.2020.1780028
28.
Shaw, S. Fallout in the Sahel: The geographic spread of conflict from Libya to Mali. Can. Foreign Policy J., 2013, 19: 199−210. doi: 10.1080/11926422.2013.805153
29.
R4Sahel. Situation Sahel crisis. 2021. Available online: https://r4sahel.info/en/situations/sahelcrisis/location/8695
30.
The World Bank. Refugee population by country or territory of asylum - Mali. 2021. Available online: https://data.worldbank.org/indicator/SM.POP.REFG?end=2020&locations=ML&start=2012&type=shaded&view=chart
31.
UNHCR. Global trends in forced displacement - 2020. 2021. Available online: https://www.unhcr.org/media/global-trends-forced-displacement-2020
32.
Chai, T.; Draxler, R.R. Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geosci. Model Dev., 2014, 7: 1247−1250. doi: 10.5194/gmd-7-1247-2014
33.
Groen, D.; Suleimenova, D.; Jahani, A.; et al. Facilitating simulation development for global challenge response and anticipation in a timely way. J. Comput. Sci., 2023, 72: 102107. doi: 10.1016/j.jocs.2023.102107
34.
Groen, D.; Arabnejad, H.; Suleimenova, D.; et al. FabSim3: An automation toolkit for verified simulations using high performance computing. Comput. Phys. Commun., 2023, 283: 108596. doi: 10.1016/j.cpc.2022.108596

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