Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/26014
Title: Towards a Coupled Migration and Weather Simulation: South Sudan Conflict
Authors: Jahani, A
Arabnejad, H
Suleimanova, D
Vuckovic, M
Mahmood, I
Groen, D
Keywords: agent-based modelling;multiscale simulation;refugee movements;data coupling
Issue Date: 9-Jun-2021
Publisher: Springer Nature, Cham
Citation: Jahani, A. et al. (2021) 'Towards a Coupled Migration and Weather Simulation: South Sudan Conflict', Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS 12746 (Part V), pp. 502 - 515. doi: 10.1007/978-3-030-77977-1_40.
Abstract: Multiscale simulations present a new approach to increase the level of accuracy in terms of forced displacement forecasting, which can help humanitarian aid organizations to better plan resource allocations for refugee camps. People’s decisions to move may depend on perceived levels of safety, accessibility or weather conditions; simulating this combination realistically requires a coupled approach. In this paper, we implement a multiscale simulation for the South Sudan conflict in 2016-2017 by defining a macroscale model covering most of South Sudan and a microscale model covering the region around the White Nile, which is in turn coupled to weather data from the Copernicus project.We couple these models cyclically in two different ways: using file I/O and using the MUSCLE3 coupling environment. For the microscale model, we incorporated weather factors including precipitation and river discharge datasets. To investigate the effects of the multiscale simulation and its coupling with weather data on refugees’ decisions to move and their speed, we compare the results with single-scale approaches in terms of the total validation error, total execution time and coupling overhead.
URI: https://bura.brunel.ac.uk/handle/2438/26014
DOI: https://doi.org/10.1007/978-3-030-77977-1_40
ISBN: 978-3-030-77976-4 (hbk)
978-3-030-77977-1 (ebk)
ISSN: 0302-9743
Other Identifiers: ORCID iDs: Hamid Arabnejad https://orcid.org/0000-0002-0789-1825; Diana Suleimenova https://orcid.org/0000-0003-4474-0943; Imran Mahmood https://orcid.org/0000-0003-0138-7510; Derek Groen https://orcid.org/0000-0001-7463-3765.
Appears in Collections:Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdfCopyright © 2021 Springer Nature. This is a pre-copyedited, author-produced version of an article accepted for publication in Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) LNCS 12746 Part V following peer review. The final authenticated version is available online at https://doi.org/10.1007/978-3-030-77977-1_40 (see: https://www.springernature.com/gp/open-research/policies/journal-policies).2.25 MBAdobe PDFView/Open


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