Is this project an undergraduate, graduate, or faculty project?
Graduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Isaac Morrison, Graduate student Kaitlyn Cavanaugh, Graduate student
Lead Presenter's Name
Isaac Morrison
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Dr. Gamage Dumindu Samaraweera
Abstract
Aviation accidents are rarely the result of a single failure but rather from a complex causal chain of latent failures. While traditional data mining models often predict incident occurrence, they frequently overlook the sequential mechanics defined by known accident causation theoretical frameworks like the Swiss Cheese Model and the FAA's HFACS. This project addresses the need for interpretable, reliable, multi-stage forecasting by proposing a Sequential Causal Architecture that transforms theoretical causation models into a structured Directed Acyclic Graph (DAG) for multimodal accident causation chain prediction. Data from the NTSB and DOT is used and connected together in a meaningful way to integrate diverse attributes that align with the designed accident causation DAG. A comparative analysis of three different data mining approaches: Random Forest, Bayesian Networks, and Long Short-Term Memory (LSTM), is conducted, maintaining causal integrity in each implementation. Models are holistically evaluated to enable trust within the aerospace safety community with evaluations such as accuracy, weighted F1-score, feature importance, and sensitivity. By shifting focus on predicting "what" happened to "how" and "why" it happened, this project intends to provide an explainable and reliable machine learning model for proactive and actionable risk mitigation in aerospace safety management systems.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Included in
Aviation Safety and Security Commons, Databases and Information Systems Commons, Data Science Commons
Sequential Causal Architecture for Multimodal Aviation Accident Prediction
Aviation accidents are rarely the result of a single failure but rather from a complex causal chain of latent failures. While traditional data mining models often predict incident occurrence, they frequently overlook the sequential mechanics defined by known accident causation theoretical frameworks like the Swiss Cheese Model and the FAA's HFACS. This project addresses the need for interpretable, reliable, multi-stage forecasting by proposing a Sequential Causal Architecture that transforms theoretical causation models into a structured Directed Acyclic Graph (DAG) for multimodal accident causation chain prediction. Data from the NTSB and DOT is used and connected together in a meaningful way to integrate diverse attributes that align with the designed accident causation DAG. A comparative analysis of three different data mining approaches: Random Forest, Bayesian Networks, and Long Short-Term Memory (LSTM), is conducted, maintaining causal integrity in each implementation. Models are holistically evaluated to enable trust within the aerospace safety community with evaluations such as accuracy, weighted F1-score, feature importance, and sensitivity. By shifting focus on predicting "what" happened to "how" and "why" it happened, this project intends to provide an explainable and reliable machine learning model for proactive and actionable risk mitigation in aerospace safety management systems.