Unpacking the Complexities of Armed Conflict Fatalities in Bangladesh: A Data-driven Study of Factors, Actors, and Spatial Patterns

Sondip Poul Singha, Md. Shamiul Islam, Susmoy Bless Singh and Julkar Naeem
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Sondip Poul Singha: Bangladesh University of Business and Technology, Dhaka, Bangladesh
Md. Shamiul Islam: Bangladesh University of Business and Technology, Dhaka, Bangladesh
Susmoy Bless Singh: Bangladesh University of Business and Technology, Dhaka, Bangladesh
Julkar Naeem: Bangladesh University of Business and Technology, Dhaka, Bangladesh

International Journal of Research and Innovation in Applied Science, 2023, vol. 8, issue 7, 212-220

Abstract: Bangladesh, a developing country, faces various challenges that hinder its progress. One significant issue is the high crime rate, along with its lower resilience score on the global peace scale compared with other Asian countries. This study investigates the underlying factors that contribute to armed conflict in Bangladesh. Key questions were explored, such as identifying the regions most affected by conflicts, understanding the involvement of different actors in these regions and events, and developing predictive models for fatality rates and future crime based on various related attributes. To address these objectives, machine learning algorithms and clustering techniques were employed in this research. The ACLED[1] Bangladesh dataset, encompassing conflict events from 2010 to 2021, was analyzed to obtain valuable insights. Clustering techniques, specifically k-means and hierarchical clustering, were applied to classify Bangladeshi Divisions and Cities based on shared characteristics. Furthermore, this study investigates the events and actors associated with each cluster to identify hidden factors. Machine learning algorithms are utilized to predict fatality rates by employing various techniques, such as pre-trained models and discretization methods. Finally, the focus shifts towards predicting future crimes by utilizing the Random Forest algorithm, which achieved a 97% accuracy rate. The results of this study demonstrated promising outcomes, with high R2 scores which is Goodness of fit measure, indicating a 99% satisfaction level for predicting fatalities. Overall, this study highlights the potential of machine learning to understand and mitigate conflicts in Bangladesh. It emphasizes the importance of interdisciplinary approaches and stakeholder engagement in developing context- specific tools for effective conflict analysis and mediation. By leveraging the findings of this study, policymakers and relevant authorities can make informed decisions to address the increasing prevalence of crime and work towards a more peaceful and secure Bangladesh

Date: 2023
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