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Improved Transferability of Data-Driven Damage Models Through Sample Selection Bias Correction
Dennis Wagenaar1,2, Tiaravanni Hermawan1, Marc J C van den Homberg3
1Deltares, Delft, The Netherlands.
Summary
Data-driven damage models for natural hazards can be improved by correcting for sample selection bias. This approach enhances model accuracy when transferring models to new situations, reducing errors by over 30%.
Area of Science:
- Natural hazard modeling
- Data-driven risk assessment
- Machine learning applications
Background:
- Damage models are crucial for natural hazard risk management, but their accuracy is affected by complex variable relationships.
- Data-driven modeling techniques show promise but often suffer from limited and unrepresentative datasets, leading to sample selection bias.
- Model transfer to different contexts exacerbates bias, impacting the reliability of damage estimates.
Purpose of the Study:
- To enhance data-driven damage models by addressing sample selection bias before machine learning model training.
- To investigate the effectiveness of bias correction methods in improving model performance for natural hazard damage assessment.
- To explore the synergy between bias correction techniques and synthetic data generation for robust damage modeling.
Main Methods:
- Application of two machine learning-based sample selection bias correction methods, with one adaptation for damage modeling.
- Integration of bias correction techniques with stochastic generation of synthetic damage data.
- Case studies on flooding in Europe and typhoon wind damage in the Philippines to validate the methods.
Main Results:
- Bias correction methods significantly reduced model errors, particularly the mean bias error, by over 30% in both case studies.
- The novel combination of sample selection bias correction with stochastic data generation demonstrated enhanced performance.
- Improved accuracy in damage estimation was observed when transferring models to new geographical and hazard contexts.
Conclusions:
- Sample selection bias correction methods are effective in improving the transferability and accuracy of data-driven damage models.
- The integration of these methods with synthetic data generation offers a promising approach for more reliable natural hazard risk assessment.
- This research highlights the importance of addressing data representativeness for robust predictive modeling in disaster risk reduction.
Keywords:
damage modelingdisaster risk managementdomain adaptationflood risk managementloss modelingmachine learningsample selection bias correctionMore Related Videos
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