Modeling the Climate-Conflict-Migration Interplay in Sudan by Integrating GAMS and Spatio-Temporal Neural Networks

Ndegwa Ruth Wambui; Mwalili Samuel; Wamwea Charity

doi:doi:10.11648/j.ijdsa.20251103.12

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Modeling the Climate-Conflict-Migration Interplay in Sudan by Integrating GAMS and Spatio-Temporal Neural Networks

Ndegwa Ruth Wambui^*

, Mwalili Samuel, Wamwea Charity

Published in International Journal of Data Science and Analysis (Volume 11, Issue 3)

Received: 22 April 2025 Accepted: 7 May 2025 Published: 16 June 2025

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Abstract

The ongoing humanitarian crises in Sudan, which are being exacerbated by escalating climate change, persistent conflict, and migratory waves, highlight the critical need for predictive, data-driven models to guide efficient response plans. In order to investigate and predict the intricate links between climate variability, conflict intensity, and migration patterns in Sudan at the subnational level, this study combines Spatio-Temporal Neural Networks (STNNs) with Generalized Additive Models (GAMs). The hybrid modeling framework provides strong insights into displacement patterns and conflict dynamics by capturing both spatial-temporal dependencies and nonlinear effects. GAMs showed a high positive correlation between conflict severity and precipitation levels, as well as statistically significant nonlinear relationships between food prices and relocation. In the meantime, the STNNs performed better than traditional modeling techniques, with R2 values of 0.89 and 0.84 for conflict intensity and regional displacement prediction, respectively. These excellent performance measures show how well the model captures real-world dynamics and provide a useful tool for humanitarian predictions. Prototype app for visualizing migration and conflict forecasts are included in the study, but they are still in the early stages of development. Future studies aim to improve operational utility and decision-making support in the field through real-time application. The results highlight how crucial it is for humanitarian research to combine machine learning and statistical modeling. This study offers practical insights that can enhance early warning systems, policymaking, and disaster preparedness in climate-vulnerable areas like Sudan by identifying important drivers of displacement and violence.

Published in	International Journal of Data Science and Analysis (Volume 11, Issue 3)
DOI	10.11648/j.ijdsa.20251103.12
Page(s)	63-75
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Climate Change, Conflict, Migration, Sudan, Machine Learning, Spatio-temporal Neural Networks, Humanitarian, Forecasting

References

[1]	G. J. Abel, M. Brottrager, J. C. Cuaresma, and R. Muttarak, “Climate, conflict and forced migration,” Global Environmental Change, vol. 54, pp. 239–249, 2019. https://doi.org/10.1016/j.gloenvcha.2018.12.003
[2]	Africa Institute on Internal Displacements (AFIID), “Climate change and displacement in Africa: A growing crisis,” Nov. 12, 2024.
[3]	A. A. Ahmed and H. M. I. S., “Environmental variability and migration patterns in Sudan: A gravity model approach,” Journal of Environmental Studies, vol. 24, no. 3, pp. 45–60, 2018.
[4]	A. H. Ali, “Sudan’s puzzle: Confronting climate change in a war-torn state,” Middle East Council on Global Affairs, 2024.
[5]	S. Berkani, E. Benkhelifa, and S. Meraghni, “Deep learning models for spatio-temporal forecasting: A comprehensive review,” Neural Computing and Applications, vol. 35, no. 1, pp. 123–145, 2023. https://doi.org/10.1007/s00521-023-08469-0
[6]	M. Bertetti, P. Agnolucci, A. Calzadilla, and L. Capra, “Improving the accuracy of food security predictions by integrating conflict data,” arXiv preprint, arXiv: 2410.22342, 2024.
[7]	R. Black, W. N. Adger, N. W. Arnell, S. Dercon, A. Geddes, and D. Thomas, “The effect of environmental change on human migration,” Global Environmental Change, vol. 21, Suppl. 1, pp. S3–S11, 2011. https://doi.org/10.1016/j.gloenvcha.2011.10.001
[8]	C. Conte, M. M. Miglietta, and M. C. Llasat, “Climate change and its impact on the occurrence of floods in urban areas: A case study in the metropolitan area of Barcelona,” Atmosphere, vol. 10, no. 5, p. 272, 2019. https://doi.org/10.3390/atmos10050272
[9]	J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” The Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001. https://doi.org/10.1214/aos/1013203451
[10]	International Centre for Migration Policy Development, “Bridging climate, conflict, and displacement in the Horn of Africa: Lessons from the Khartoum Process,” 2024.
[11]	International Organization for Migration – DTM, A Year in Review: Displacement in Sudan (2024), 2024.
[12]	J. Mark and A. Naureen, “Machine learning approach to conflict prediction in Sub-Saharan Africa,” Sustainability, vol. 13, no. 13, p. 7366, 2021. https://doi.org/10.3390/su13137366
[13]	United Nations Development Programme (UNDP), “Climate change adaptation in Sudan: Fostering resilience and peace,” UNDP Reports, 2020. https://www.adaptation-undp.org/projects/sudan-climate-change-adaptation
[14]	R. Reuveny, “Climate change-induced migration and violent conflict,” Political Geography, vol. 26, no. 6, pp. 656–673, 2007. https://doi.org/10.1016/j.polgeo.2007.05.001
[15]	L. Solaa, Y. Chen, S. K. Murphy, and V. S. Subrahmanian, “Quantifying the risk of pastoral conflict in 4 Central African countries,” arXiv preprint, arXiv: 2412.18799, 2024.
[16]	D. Ubilava, “Climate, crops, and postharvest conflict,” arXiv preprint, arXiv: 2311.16370, 2023. https://doi.org/10.48550/arXiv.2311.1637

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APA Style

Wambui, N. R., Samuel, M., Charity, W. (2025). Modeling the Climate-Conflict-Migration Interplay in Sudan by Integrating GAMS and Spatio-Temporal Neural Networks. International Journal of Data Science and Analysis, 11(3), 63-75. https://doi.org/10.11648/j.ijdsa.20251103.12

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ACS Style

Wambui, N. R.; Samuel, M.; Charity, W. Modeling the Climate-Conflict-Migration Interplay in Sudan by Integrating GAMS and Spatio-Temporal Neural Networks. Int. J. Data Sci. Anal. 2025, 11(3), 63-75. doi: 10.11648/j.ijdsa.20251103.12

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AMA Style

Wambui NR, Samuel M, Charity W. Modeling the Climate-Conflict-Migration Interplay in Sudan by Integrating GAMS and Spatio-Temporal Neural Networks. Int J Data Sci Anal. 2025;11(3):63-75. doi: 10.11648/j.ijdsa.20251103.12

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@article{10.11648/j.ijdsa.20251103.12,
  author = {Ndegwa Ruth Wambui and Mwalili Samuel and Wamwea Charity},
  title = {Modeling the Climate-Conflict-Migration Interplay in Sudan by Integrating GAMS and Spatio-Temporal Neural Networks
},
  journal = {International Journal of Data Science and Analysis},
  volume = {11},
  number = {3},
  pages = {63-75},
  doi = {10.11648/j.ijdsa.20251103.12},
  url = {https://doi.org/10.11648/j.ijdsa.20251103.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20251103.12},
  abstract = {The ongoing humanitarian crises in Sudan, which are being exacerbated by escalating climate change, persistent conflict, and migratory waves, highlight the critical need for predictive, data-driven models to guide efficient response plans. In order to investigate and predict the intricate links between climate variability, conflict intensity, and migration patterns in Sudan at the subnational level, this study combines Spatio-Temporal Neural Networks (STNNs) with Generalized Additive Models (GAMs). The hybrid modeling framework provides strong insights into displacement patterns and conflict dynamics by capturing both spatial-temporal dependencies and nonlinear effects. GAMs showed a high positive correlation between conflict severity and precipitation levels, as well as statistically significant nonlinear relationships between food prices and relocation. In the meantime, the STNNs performed better than traditional modeling techniques, with R2 values of 0.89 and 0.84 for conflict intensity and regional displacement prediction, respectively. These excellent performance measures show how well the model captures real-world dynamics and provide a useful tool for humanitarian predictions. Prototype app for visualizing migration and conflict forecasts are included in the study, but they are still in the early stages of development. Future studies aim to improve operational utility and decision-making support in the field through real-time application. The results highlight how crucial it is for humanitarian research to combine machine learning and statistical modeling. This study offers practical insights that can enhance early warning systems, policymaking, and disaster preparedness in climate-vulnerable areas like Sudan by identifying important drivers of displacement and violence.
},
 year = {2025}
}

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TY  - JOUR
T1  - Modeling the Climate-Conflict-Migration Interplay in Sudan by Integrating GAMS and Spatio-Temporal Neural Networks

AU  - Ndegwa Ruth Wambui
AU  - Mwalili Samuel
AU  - Wamwea Charity
Y1  - 2025/06/16
PY  - 2025
N1  - https://doi.org/10.11648/j.ijdsa.20251103.12
DO  - 10.11648/j.ijdsa.20251103.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 63
EP  - 75
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20251103.12
AB  - The ongoing humanitarian crises in Sudan, which are being exacerbated by escalating climate change, persistent conflict, and migratory waves, highlight the critical need for predictive, data-driven models to guide efficient response plans. In order to investigate and predict the intricate links between climate variability, conflict intensity, and migration patterns in Sudan at the subnational level, this study combines Spatio-Temporal Neural Networks (STNNs) with Generalized Additive Models (GAMs). The hybrid modeling framework provides strong insights into displacement patterns and conflict dynamics by capturing both spatial-temporal dependencies and nonlinear effects. GAMs showed a high positive correlation between conflict severity and precipitation levels, as well as statistically significant nonlinear relationships between food prices and relocation. In the meantime, the STNNs performed better than traditional modeling techniques, with R2 values of 0.89 and 0.84 for conflict intensity and regional displacement prediction, respectively. These excellent performance measures show how well the model captures real-world dynamics and provide a useful tool for humanitarian predictions. Prototype app for visualizing migration and conflict forecasts are included in the study, but they are still in the early stages of development. Future studies aim to improve operational utility and decision-making support in the field through real-time application. The results highlight how crucial it is for humanitarian research to combine machine learning and statistical modeling. This study offers practical insights that can enhance early warning systems, policymaking, and disaster preparedness in climate-vulnerable areas like Sudan by identifying important drivers of displacement and violence.

VL  - 11
IS  - 3
ER  -

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Author Information

Ndegwa Ruth Wambui

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Juja, Kenya

Contact Email

http://orcid.org/0009-0007-7795-2607
Mwalili Samuel

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Juja, Kenya
Wamwea Charity

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Juja, Kenya

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APA Style

Wambui, N. R., Samuel, M., Charity, W. (2025). Modeling the Climate-Conflict-Migration Interplay in Sudan by Integrating GAMS and Spatio-Temporal Neural Networks. International Journal of Data Science and Analysis, 11(3), 63-75. https://doi.org/10.11648/j.ijdsa.20251103.12

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ACS Style

Wambui, N. R.; Samuel, M.; Charity, W. Modeling the Climate-Conflict-Migration Interplay in Sudan by Integrating GAMS and Spatio-Temporal Neural Networks. Int. J. Data Sci. Anal. 2025, 11(3), 63-75. doi: 10.11648/j.ijdsa.20251103.12

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AMA Style

Wambui NR, Samuel M, Charity W. Modeling the Climate-Conflict-Migration Interplay in Sudan by Integrating GAMS and Spatio-Temporal Neural Networks. Int J Data Sci Anal. 2025;11(3):63-75. doi: 10.11648/j.ijdsa.20251103.12

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@article{10.11648/j.ijdsa.20251103.12,
  author = {Ndegwa Ruth Wambui and Mwalili Samuel and Wamwea Charity},
  title = {Modeling the Climate-Conflict-Migration Interplay in Sudan by Integrating GAMS and Spatio-Temporal Neural Networks
},
  journal = {International Journal of Data Science and Analysis},
  volume = {11},
  number = {3},
  pages = {63-75},
  doi = {10.11648/j.ijdsa.20251103.12},
  url = {https://doi.org/10.11648/j.ijdsa.20251103.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20251103.12},
  abstract = {The ongoing humanitarian crises in Sudan, which are being exacerbated by escalating climate change, persistent conflict, and migratory waves, highlight the critical need for predictive, data-driven models to guide efficient response plans. In order to investigate and predict the intricate links between climate variability, conflict intensity, and migration patterns in Sudan at the subnational level, this study combines Spatio-Temporal Neural Networks (STNNs) with Generalized Additive Models (GAMs). The hybrid modeling framework provides strong insights into displacement patterns and conflict dynamics by capturing both spatial-temporal dependencies and nonlinear effects. GAMs showed a high positive correlation between conflict severity and precipitation levels, as well as statistically significant nonlinear relationships between food prices and relocation. In the meantime, the STNNs performed better than traditional modeling techniques, with R2 values of 0.89 and 0.84 for conflict intensity and regional displacement prediction, respectively. These excellent performance measures show how well the model captures real-world dynamics and provide a useful tool for humanitarian predictions. Prototype app for visualizing migration and conflict forecasts are included in the study, but they are still in the early stages of development. Future studies aim to improve operational utility and decision-making support in the field through real-time application. The results highlight how crucial it is for humanitarian research to combine machine learning and statistical modeling. This study offers practical insights that can enhance early warning systems, policymaking, and disaster preparedness in climate-vulnerable areas like Sudan by identifying important drivers of displacement and violence.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Modeling the Climate-Conflict-Migration Interplay in Sudan by Integrating GAMS and Spatio-Temporal Neural Networks

AU  - Ndegwa Ruth Wambui
AU  - Mwalili Samuel
AU  - Wamwea Charity
Y1  - 2025/06/16
PY  - 2025
N1  - https://doi.org/10.11648/j.ijdsa.20251103.12
DO  - 10.11648/j.ijdsa.20251103.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 63
EP  - 75
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20251103.12
AB  - The ongoing humanitarian crises in Sudan, which are being exacerbated by escalating climate change, persistent conflict, and migratory waves, highlight the critical need for predictive, data-driven models to guide efficient response plans. In order to investigate and predict the intricate links between climate variability, conflict intensity, and migration patterns in Sudan at the subnational level, this study combines Spatio-Temporal Neural Networks (STNNs) with Generalized Additive Models (GAMs). The hybrid modeling framework provides strong insights into displacement patterns and conflict dynamics by capturing both spatial-temporal dependencies and nonlinear effects. GAMs showed a high positive correlation between conflict severity and precipitation levels, as well as statistically significant nonlinear relationships between food prices and relocation. In the meantime, the STNNs performed better than traditional modeling techniques, with R2 values of 0.89 and 0.84 for conflict intensity and regional displacement prediction, respectively. These excellent performance measures show how well the model captures real-world dynamics and provide a useful tool for humanitarian predictions. Prototype app for visualizing migration and conflict forecasts are included in the study, but they are still in the early stages of development. Future studies aim to improve operational utility and decision-making support in the field through real-time application. The results highlight how crucial it is for humanitarian research to combine machine learning and statistical modeling. This study offers practical insights that can enhance early warning systems, policymaking, and disaster preparedness in climate-vulnerable areas like Sudan by identifying important drivers of displacement and violence.

VL  - 11
IS  - 3
ER  -

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