Data-Driven Modelling and Predictive Analysis of Internal Migration and Displacement Trends in Nigeria for Enhanced Humanitarian Planning
Keywords:
communal clashes, insurgency, internal displacement, internal migration, predictive analysisAbstract
Internal displacement in Nigeria remains a major humanitarian challenge, driven by conflict, insurgency, communal clashes, and climate-related disasters, yet existing responses are often reactive and inadequate for proactive planning. This study, titled Data-Driven Modelling and Predictive Analysis of Internal Migration and Displacement Trends in Nigeria for Enhanced Humanitarian Planning, applies a machine learning framework to displacement data to forecast trends and support timely humanitarian interventions. Using datasets from the International Organization for Migration’s (IOM) Displacement Tracking Matrix, particularly the Baseline Assessment and Needs Monitoring surveys, the research processed over 114,000 records across 19 variables, including demographic indicators, site accessibility, household size, and priority needs. Data preprocessing involved handling missing values, categorical standardization, one-hot encoding, and normalization of numeric features, with attention to balancing the skewed distribution of displacement reasons where insurgency accounted for 95% of cases, communal clashes 4%, and natural disasters 1%. Exploratory analysis revealed that Borno, Adamawa, and Yobe states remain most affected, with insurgency consistently dominating and September 2022 marking the highest peak in displacement. Three algorithms: Logistic Regression, Random Forest, and XGBoost were evaluated, with XGBoost outperforming others by achieving a macro F1-score of 0.96, and balanced accuracy of 0.968 across classes. The model correctly classified nearly all insurgency cases and demonstrated strong performance for communal clashes (F1-score = 0.99) and natural disasters (F1-score = 0.90). These findings show that displacement follows identifiable socio-economic and geographic patterns, and predictive modeling can capture them effectively.
Author Biographies
Ajayi Ore-Ofe, Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria
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
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
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].
Abduljalil Murtala Ahmad, Electrical Engineering Department, Ahmadu Bello University, Zaria, Nigeria
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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