Using Machine Learning to Predict Ceasefire Violations in the Israel – Palestinian Conflict: A Data-Driven Approach

Abstract

The study examines machine learning to predict ceasefire violations in the Israel – Palestinian conflict: A data-driven approach. The study used secondary sources of data. Complexity Theory was used to guide the study. The empirical studies revealed that datadriven approaches, combining machine learning and various data types (such as military, social, and political data), can be applied to predict ceasefire violations and better understand the dynamics of the Israel-Palestinian conflict. Findings from the study revealed that the use of machine learning in warfare introduces complex issues regarding international humanitarian law (IHL) and human rights. The study concluded that Machine learning is transforming the landscape of automated warfare, offering unprecedented capabilities in decision-making, targeting, and defense systems. However, its integration also presents significant ethical, legal, and strategic challenges. As technology advances, it will be essential to establish clear guidelines for the responsible use of ML in military contexts, balancing technological progress with human rights, accountability, and the protection of civilians. The study recommends that, in order to build an effective machine learning model, gathering comprehensive and high-quality data is essential. Data sources could include historical ceasefire agreements, recorded violations, military movements, political statements, and social media activity. Structured data such as satellite imagery and sensor data can complement unstructured data like news articles and diplomatic communications. Pre-processing techniques, including feature engineering, data normalization, and handling missing values, will enhance model performance