An Optimized Weighted Naïve Bayes Method for Flood Risk Assessment.

Area of Science:

• Environmental Science
• Geospatial Analysis
• Risk Management

Background:

• Floods pose significant environmental risks and economic losses globally.
• Accurate flood risk assessment is crucial for effective disaster mitigation strategies.
• Existing methods often lack the direct integration needed for detailed flood probability mapping.

Purpose of the Study:

• To develop and validate a novel weighted Naïve Bayes (WNB) framework for enhanced flood risk assessment.
• To improve the generation of accurate conditional probability tables (MACPT) for flood probability calculations.
• To provide a more interpretable and extensible flood risk modeling approach.

Main Methods:

• Developed direct WNB functions for MATLAB integration with a sampling and verifying model.
• Employed the entropy weight method to determine attribute weights.
• Utilized a sampling and verifying model to generate a WNB-based MACPT for flood probability.
• Applied the framework to Shantou and Jieyang cities in China for spatial flood risk analysis.

Main Results:

• The WNB-based MACPT accurately reflects the spatial distribution of flood risk.
• The integrated WNB model demonstrated superior performance compared to the standard Naïve Bayes (NB) model.
• Gridded flood risk estimations revealed detailed spatial patterns for practical decision-making.

Conclusions:

• The developed WNB functions enhance flood risk assessment interpretability and extensibility.
• The proposed method offers a data-efficient approach, beneficial for regions with limited hydrologic monitoring.
• This study provides a valuable tool for informed flood risk management and disaster preparedness.