Data-Driven Modeling and Predictive Analysis of Internal Migration and Displacement Trends in Nigeria for Enhanced Humanitarian Planning

Penulis

  • Ajayi Ore-Ofe Ajayi Ore-Ofe Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria
  • Muhammad Bashir Yahya Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria
  • Shehu Mohammed Yusuf Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria
  • Abubakar Umar Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria
  • Hassan Maharazu Department of Electrical and Electronics Engineering, Federal University of Transportation, Daura, Katsina State Nigeria
  • Ibrahim Ibrahim Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria
  • Murtala Abduljalil Ahmad Electrical Engineering Department, Ahmadu Bello University, Zaria, Nigeria

DOI:

https://doi.org/10.26740/vubeta.v3i3.47643

Kata Kunci:

communal clashes, insurgency, internal displacement, internal migration, predictive analysis

Abstrak

Internal displacement in Nigeria remains a major humanitarian challenge driven by conflict, insurgency, communal clashes, and climate-related disasters. Existing responses are often reactive, highlighting the need for data-driven approaches to support proactive humanitarian planning. This study, titled Data-Driven Modeling and Predictive Analysis of Internal Migration and Displacement Trends in Nigeria for Enhanced Humanitarian Planning, develops a machine learning framework to analyze and predict displacement trends using data from the International Organization for Migration (IOM) Displacement Tracking Matrix, particularly the Baseline Assessment and Needs Monitoring surveys. The dataset comprised over 114,000 records and 19 variables, including demographic characteristics, site accessibility, household size, and priority needs. Data preprocessing involved missing-value handling, categorical standardization, one-hot encoding, and numerical normalization. The highly imbalanced distribution of displacement causes was also considered, with insurgency accounting for 95% of cases, communal clashes 4%, and natural disasters 1%. Exploratory analysis identified Borno, Adamawa, and Yobe as the most affected states, with insurgency consistently dominating and September 2022 recording the highest displacement peak. Logistic Regression, Random Forest, and XGBoost were evaluated. XGBoost achieved the best performance, obtaining a macro F1-score of 0.96 and balanced accuracy of 0.968. It demonstrated strong classification performance for communal clashes (F1 = 0.99) and natural disasters (F1 = 0.90), while correctly identifying nearly all insurgency cases. These findings demonstrate that displacement patterns can be effectively modeled using socio-economic and geographic indicators. The proposed framework can assist humanitarian agencies in anticipating displacement, optimizing resource allocation, and strengthening early-warning systems to improve crisis preparedness and resilience.

Biografi Penulis

Ajayi Ore-Ofe Ajayi Ore-Ofe, Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

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Ajayi Ore-Ofe is a lecturer at the Department of Computer Engineering, Ahmadu Bello University, Zaria, Nigeria. He received his MSc and Ph.D from Computer Engineering in Control Engineering, in 2017 and 2022 respectively. He received his MSc and Ph.D from the department of Computer Engineering in Ahmadu Bello University, Zaria, Nigeria. He is mainly research in control engineering. He can be contacted at email: [email protected].

Muhammad Bashir Yahya, Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

Shehu Mohammed Yusuf, Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

Abubakar Umar, Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

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Abubakar Umar is a lecturer in the Department of Computer Engineering at Ahmadu Bello University, Zaria, Nigeria. He earned his BEng, MSc, and Ph.D. degrees from the same department, specializing in various aspects of computer engineering. His primary research focus is in Control Engineering, where he explores the development and optimization of control systems for different applications. He is dedicated to advancing his research and contributing to academic knowledge in this field. He can be contacted via email at [email protected].

Hassan Maharazu, Department of Electrical and Electronics Engineering, Federal University of Transportation, Daura, Katsina State Nigeria

Department of Electrical and Electronics Engineering, Federal University of Transportation, Daura, Katsina State Nigeria

Ibrahim Ibrahim, Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria

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Ibrahim Ibrahim is a Computer Engineering graduate at Ahmadu Bello University, Zaria, Nigeria, with a strong interest in Data Science, Machine Learning, and Artificial Intelligence. He is passionate about using technology to solve real-world problems and is continuously expanding his expertise through self-learning and professional courses. In addition to his focus on AI, he is also developing skills in data analysis and backend development to build a solid foundation for intelligent system design. His goal is to become an innovative AI Engineer capable of applying data-driven solutions to both practical and research-based challenges, contributing meaningfully to technological advancement and social impact. He can be reached via email at [email protected].

Murtala Abduljalil Ahmad, Electrical Engineering Department, Ahmadu Bello University, Zaria, Nigeria

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Abduljalil Murtala Ahmad is an Electrical Engineering student at Ahmadu Bello University, Zaria, Nigeria, with a strong interest in high voltage, HV engineering, artificial intelligence, and intelligent systems. He holds a Professional Diploma in Applied Artificial Intelligence from Baze University, Abuja, and has completed several professional courses on Cisco Networking and IT Masters. His current project work on HV engineering plays a vital role on the stability of today's power systems. He continues to pursue advanced studies in AI and machine learning to further expand his expertise and apply these skills to practical and research-based innovations, particularly in robotics and intelligent automation. He can be reached via email at [email protected].

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Unduhan

Diterbitkan

2026-08-06

Cara Mengutip

[1]
A. O.-O. Ajayi Ore-Ofe, “Data-Driven Modeling and Predictive Analysis of Internal Migration and Displacement Trends in Nigeria for Enhanced Humanitarian Planning”, Vokasi UNESA Bull. Eng. Technol. Appl. Sci., vol. 3, no. 3, hlm. 461–480, Agu 2026.

Terbitan

Vol 3 No 3 (2026): (In Progress)

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Article

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Hak Cipta (c) 2026 Abubakar Umar

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