Bayesian Network Model for River Accident Risk Prediction in Indonesia from 2007 to 2025: An SDG Approach to Water Transportation Safety

The objectives of this study are to analyze accident patterns across Indonesian inland waterways from 2007 to 2025 and to build a fuzzy Bayesian Network (BN) for risk prediction and causal mapping. The Sustainable Development Goals (SDGs), established by the United Nations as a global framework for social, economic, and environmental development, provide the policy context for this work. Our study draws on system safety and probabilistic reasoning, positioning a BN as both an empirical tool and a theoretical lens. This framework allows us to capture uncertainty, nonlinear interactions, and hidden dependencies that are often overlooked by conventional regression. We analyze 1,257 recorded river accidents using SAS Studio (version 3.8), combining descriptive statistics, inferential tests, and a Naïve Bayes classifier. We split the data set into training (70%) and testing (30%) partitions. Notably, we apply Laplace smoothing to stabilize sparse categories and evaluate accuracy via confusion matrices and area under the curve (AUC). Our analysis indicates that fatal incidents constitute 28% of total cases, with a marked increase after 2015. The BN model achieves an accuracy of 81% and an AUC of 0.84, outperforming logistic regression benchmarks. A closer look reveals that incident type and province consistently drive fatality probabilities, while seasonal patterns are surprisingly weaker than expected. This finding may reflect policy inertia in addressing high-risk routes rather than climatic cycles. Practically, the BN framework contributes to early warning systems for transport regulators. Theoretically, it refines accident modeling by integrating uncertainty and context-specific causal pathways. We demonstrate, for the first time in Indonesia, that Bayesian Network analysis meaningfully predicts river accident risks. This novelty bridges theory and practice, offering both methodological innovation and actionable policy guidance.