Integrating Methods for Causal Analysis in Nuclear Licensee Event Documentation

Abstract

This article explores the integration of advanced methodologies for causal analysis in Nuclear Licensee Event Documentation (LERs). It underscores the critical importance of accurately identifying causal relationships within LERs to enhance safety and regulatory compliance in the nuclear industry. The integrated methods encompass sophisticated techniques such as Natural Language Processing (NLP), Machine Learning (ML), and causal inference models. These methodologies synergistically contribute to improving the precision and depth of causal analysis, thereby facilitating comprehensive incident understanding and informed decision-making. Key findings highlight significant advancements in causality extraction accuracy and efficiency, offering profound implications for enhancing nuclear safety protocols and regulatory frameworks.