Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31656
Title: AI-Enhanced Agent-Based Modelling Approach for Forced Displacement Predictions
Authors: Suleimenova, D
Xue, Y
Tas, A
Low, W
Keywords: agent-based modelling;artificial intelligence;predictive models;forced displacement
Issue Date: 5-Jul-2025
Publisher: Springer Nature Switzerland
Citation: Suleimenova, D. et al. (2025) 'AI-Enhanced Agent-Based Modelling Approach for Forced Displacement Predictions', in M. Paszynski, A.S. Barnard and Y.J. Zhang (eds.) Computational Science – ICCS 2025 Workshops. ICCS 2025 (Lecture Notes in Computer Science, vol. 15911). Cham: Springer, pp. 79 - 86. doi: 10.1007/978-3-031-97570-7_8.
Series/Report no.: Lecture Notes in Computer Science;vol. 15911
Abstract: The increasing occurrence and complexity of forced displacement require robust predictive models to aid humanitarian responses. However, existing predictive models for forced displacement lack accurate and timely data, have gaps in existing datasets, struggle with the unpredictability of human behaviour, do not account for rapidly evolving political and environmental factors and introduce methodological uncertainties. Hence, this paper proposes a theoretical discussion of an artificial intelligence (AI)-enhanced agent-based modelling (ABM) approach to assist effective humanitarian planning and efficient resource allocation. This novel approach aims to predict the movements of internally displaced people and the arrival of forcibly displaced individuals in neighbouring countries. Importantly, it introduces a combination of quantitative models and qualitative insights from expert knowledge, along with humanitarian reports. Our AI-enhanced ABM approach (i) uses an agent-based simulation tool, Flee, incorporating behavioural assumptions and customisable rulesets for scenario modelling, (ii) explores innovative near real-time data sources from geospatial data and social media activity to satellite imagery with AI techniques, and (iii) discusses the ABM model with AI-generated inputs to enhance the granularity, accuracy, and reliability of predictions.
URI: https://bura.brunel.ac.uk/handle/2438/31656
DOI: https://doi.org/10.1007/978-3-031-97570-7_8
ISBN: 978-3-031-97569-1 (pbk)
978-3-031-97570-7 (ebk)
ISSN: 0302-9743
Other Identifiers: ORCiD: Diana Suleimenova https://orcid.org/0000-0003-4474-0943
ORCiD: Yani Xue https://orcid.org/0000-0002-7526-9085
Chapter 8
Appears in Collections:Dept of Computer Science Embargoed Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdfEmbargoed until 5 July 2026. Copyright © 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is a pre-copyedited, author-produced version of a book chapter accepted for publication in Computational Science – ICCS 2025 Workshops (ICCS 2025). following peer review. The final authenticated version is available online at https://doi.org/10.1007/978-3-031-97570-7_8 (see: https://www.springernature.com/gp/open-research/policies/book-policies).312.94 kBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.