Assessment of catastrophic risk using Bayesian network constructed from domain knowledge and spatial data.

Area of Science:

• Environmental Science
• Data Science
• Risk Management

Background:

• Natural disaster prediction is complex due to numerous interacting factors and inherent uncertainties.
• Existing methods struggle to integrate diverse data sources and domain expertise effectively for catastrophic risk assessment.

Purpose of the Study:

• To develop and validate a Bayesian network (BN) methodology for robust natural disaster risk assessment.
• To integrate domain knowledge with spatial data analysis and machine learning for improved risk prediction.
• To quantify uncertainties within a consistent framework for catastrophic risk management.

Main Methods:

• Constructed a Bayesian network (BN) by combining domain knowledge with data-driven learning.
• Employed spatial data analysis and data mining to augment training data and identify key risk factors.
• Integrated advanced techniques including optimal discretization, feature selection, and Bayesian model averaging.
• Utilized cross-validation and historic data for evaluating prediction accuracy and performance.

Main Results:

• The proposed BN methodology demonstrated superior performance in predicting high-risk events.
• Achieved improved precision and a better ROC area compared to traditional methods in flood disaster assessment.
• The system effectively integrated multiple data sources and quantified uncertainties for risk assessment.

Conclusions:

• Bayesian networks offer a robust and adaptable framework for natural disaster risk assessment.
• The methodology provides a valuable decision-support tool for managing catastrophic risks.
• Combining domain expertise with advanced data analysis techniques enhances predictive capabilities for natural hazards.