Efficient Probabilistic Tsunami Hazard and Risk Assessment Using a Hybrid Modeling Approach: A Systematic Evaluation
- 1The University School for Advanced Studies - IUSS Pavia, Pavia, Italy (naveen.raguramalingam@iusspavia.it)
- 2Istituto Nazionale di Geofisica e Vulcanologia (INGV), Rome, Italy
- 3Norwegian Geotechnical Institute, Oslo, Norway
- 4Global Earthquake Model Foundation, Pavia, Italy
- 5Geoscience Australia, Canberra, Australia
The hybrid modelling approach combining machine learning and physics-based simulation has been used in a variety of ways to study tsunami and improve our understanding of this complex natural hazard. They are broadly applied for (1) Tsunami forecasting and early warning systems and (2) Tsunami hazard and risk assessment including sensitivity, analysis uncertainty studies and inverse modelling for estimating the source.
Rigorous evaluation of such a hybrid approach is constrained by the limited size of available simulation datasets which is important to guide their usage by practitioners. This study investigates the application of a hybrid tsunami modelling technique (Ragu Ramalingam et al., 2022, Ragu Ramalingam et al., 2022) which offers a computationally efficient approach for hazard assessment where large events-sets must be modelled typical of probabilistic tsunami hazard and risk assessment (PTHA/PTRA). We use a large tsunami simulation dataset for a coastal region of eastern Sicily, Italy and try to address the following question:
- How to efficiently sample scenarios used to train the ML models?
- Where and when are such methods accurate?
- How do they compare with other traditional modelling methods like Monte Carlo Sampling?
Additionally, the effort will deliver an open tsunami benchmarking dataset that can be utilised for further development, baseline comparison of various ML algorithms, and improved hyperparameter tuning.
References
Ragu Ramalingam, N., Johnson, K., Pagani, M., and Martina, M.: A hybrid ML-physical modelling approach for efficient approximation of tsunami waves at the coast for probabilistic tsunami hazard assessment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5642, https://doi.org/10.5194/egusphere-egu22-5642, 2022.
Ragu Ramalingam, N., Rao, A., Johnson, K., Pagani, M. and Martina, M. A hybrid ML-physical modelling approach for efficient probabilistic tsunami hazard and risk assessment, Proceedings of the 19th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2022), August 1-5, 2022, Virtual.
How to cite: Ragu Ramalingam, N., Abbate, A., Briseid Storrøsten, E., Johnson, K., Davies, G., Lorito, S., Pagani, M., and Martina, M.: Efficient Probabilistic Tsunami Hazard and Risk Assessment Using a Hybrid Modeling Approach: A Systematic Evaluation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16906, https://doi.org/10.5194/egusphere-egu23-16906, 2023.
