Artificial Intelligence in Crime Scene Reconstruction: Using Machine Learning for Predictive Analysis and Scenario Simulation

Abstract

Crime scene reconstruction remains a critical yet complex component of forensic investigation, requiring the integration of heterogeneous evidence sources to infer plausible sequences of criminal events. Traditional reconstruction methods rely heavily on expert interpretation, which can introduce subjectivity and limitations when dealing with large-scale or incomplete forensic data. This study proposes a machine learning-based framework for automated crime scene reconstruction, integrating predictive analytics and scenario simulation to enhance investigative decision-making. The framework leverages structured forensic features, including evidence distribution, temporal response characteristics, and scene complexity indicators, to model reconstruction outcomes using a combination of classical machine learning, ensemble methods, and deep learning architectures. In addition, sequential models and hybrid neural networks are employed to capture temporal dependencies and local evidence patterns, enabling more robust interpretation of event sequences. Scenario simulation is incorporated to generate multiple plausible crime narratives under varying evidence conditions, supporting probabilistic reasoning in uncertain investigative environments. The findings indicate that advanced ensemble and hybrid deep learning models offer superior capability for capturing nonlinear and temporal relationships in forensic datasets, leading to more consistent and reliable reconstruction outcomes. The proposed approach demonstrates the potential of artificial intelligence to augment forensic investigation processes by improving reconstruction accuracy, enhancing scenario exploration, and supporting more structured evidence interpretation in complex crime scenes.