A Data-Driven Probabilistic Framework for Cascading Urban Risk Analysis Using Bayesian Networks
Chunduru Rohith Kumar, PHD Surya Shanmuk, Prabhala Naga Srinivas, Sri Venkatesh Lankalapalli, Debasis Dwibedy
TL;DR
The paper tackles cascading risk in urban systems by learning cross‑domain dependencies with Bayesian Belief Networks (BBNs). It introduces a data‑driven workflow that fuses real urban indicators with GAN‑generated synthetic data, balances the dataset with SMOTE, binarizes features, and learns Directed Acyclic Graphs (DAGs) via Hill‑Climbing optimized by Bayesian Information Criterion (BIC) and K2 scoring, producing Conditional Probability Tables (CPTs) for probabilistic inference, including expressions like $P(C \mid X) = \frac{P(X \mid C) P(C)}{P(X)}$. The framework is demonstrated across eight urban domains (air, water, electricity, agriculture, health, infrastructure, weather, climate), uncovering intra‑ and inter‑domain risk pathways and showing that moderate multi‑domain stress can drive cascading failures more than isolated extreme events. This leads to actionable insights for urban resilience in energy, health, and infrastructure planning and provides a scalable foundation for future temporal and multi‑modal extensions, including potential use of $BIC$ and $K2$ scoring in dynamic settings and model fidelity checks with measures like $P(C\mid X)$.
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.