A Data-Driven Probabilistic Framework for Cascading Urban Risk Analysis Using Bayesian Networks

TL;DR

This paper introduces a data-driven Bayesian network framework for analyzing cascading risks across urban systems, integrating real and synthetic data to identify key risk factors and support urban resilience planning.

Contribution

It develops a novel probabilistic approach using Bayesian networks trained on hybrid datasets, enabling interpretable cross-domain risk analysis in urban environments.

Findings

Identifies key intra- and inter-domain risk factors.

Demonstrates the framework's utility for proactive resilience planning.

Establishes a scalable, interpretable foundation for urban risk assessment.

Abstract

The increasing complexity of cascading risks in urban systems necessitates robust, data-driven frameworks to model interdependencies across multiple domains. This study presents a foundational Bayesian network-based approach for analyzing cross-domain risk propagation across key urban domains, including air, water, electricity, agriculture, health, infrastructure, weather, and climate. Directed Acyclic Graphs (DAGs) are constructed using Bayesian Belief Networks (BBNs), with structure learning guided by Hill-Climbing search optimized through Bayesian Information Criterion (BIC) and K2 scoring. The framework is trained on a hybrid dataset that combines real-world urban indicators with synthetically generated data from Generative Adversarial Networks (GANs), and is further balanced using the Synthetic Minority Over-sampling Technique (SMOTE). Conditional Probability Tables (CPTs) derived from the learned structures enable interpretable probabilistic reasoning and quantify the likelihood of cascading failures. The results identify key intra- and inter-domain risk factors and demonstrate the framework's utility for proactive urban resilience planning. This work establishes a scalable, interpretable foundation for cascading risk assessment and serves as a basis for future empirical research in this emerging interdisciplinary field.