Publications

Coastal storm-induced flooding risk of the New York City subway amid climate change
Y. Miura*, C.Y. Blackshow, M. Zhang, K.T. Mandli, G. Deodatis. Transportation Research Part D: Transport and Environment (2025). [DOI]

This study extends a high-speed, physics-based flood modeling framework (GISSR) to simulate subway flooding and economic losses in New York City. Validated against Hurricane Sandy data, the model estimates direct and indirect impacts within 90 seconds per scenario and evaluates adaptation measures under sea level rise. Results highlight the effectiveness of combining coastal and subway-specific protections to enhance urban flood resilience.

A Multimodal, Multilingual, and Multidimensional Pipeline for Fine-grained Crowdsourcing Earthquake Damage Evaluation
Z. Ma, L. Li, J. Li, W. Hua, Q. Feng, and Y. Miura*. (2025). Under Review.

We introduce a 3M (Multimodal, Multilingual, Multidimensional) pipeline that uses multimodal large language models (MLLMs) to assess disaster impacts from social media. Evaluated across major earthquake events in two countries, our approach integrates image and text data to provide timely, fine-grained damage assessments that correlate with seismic ground truth, highlighting the potential of MLLMs for real-time crisis response.

RoofNet: A Global Multimodal Dataset for Roof Material Classification
N. Law and Y. Miura. (2025). Under Review. [DOI] [GitHub]

We present RoofNet, the first geographically diverse dataset for global roof material classification. With over 51,500 EO image-text pairs from 184 sites and 112 countries, RoofNet enables scalable, AI-driven risk assessment of building vulnerability to natural hazards. Fine-tuned with a vision-language model (VLM) using expert annotations and prompt tuning, it supports downstream tasks in disaster preparedness, insurance, and infrastructure resilience.

Modeling storm surges with a bounded probability distribution
Y. Miura*, K.T. Mandli, G. Deodatis. (2024). Under Review.

NYC stakeholders’ interaction and feedback on a coastal protective strategy optimization
Y. Miura*, K.T. Mandli, H. Lazrus, R. Morss. (2024). Under Review.

Bracing for Impact: How Shifting Precipitation Extremes may Influence Physical Climate Risks in an Uncertain Future
S.H. Rahat*, A. Poresky, S. Saki, U.K. Choya, I.J. Dollan, A. Wasti, E. Bhuiyan, Y. Miura, J. Kucharski, P. Ray. Nature Scientific Reports, Scientific Reports (2024) 14(1), 1-12. [DOI]

Traditional 100-year return period models are inadequate due to intensified precipitation from climate change. This study shows high variability in risk across the U.S., with about 53 million people in high-risk zones, potentially doubling or tripling under higher warming. Increased drought frequency affecting 37% of major farmland highlights the need for improved adaptation strategies.

Optimization of coastal protections in the presence of climate change
Y. Miura*, P.C. Dinenis, K.T. Mandli, G. Deodatis, D. Bienstock. Frontiers in Climate (2021) 4:23. [DOI]

The paper introduces a method for optimizing protective measures for coastal infrastructure in New York, facing threats from storm-induced flooding and sea level rise (SLR). It employs GIS-based techniques and storm surge simulations to refine solutions within budget constraints. The approach evaluates various protective strategies and their effectiveness with stakeholder input.

High-speed GIS-based simulation of storm surge induced flooding accounting for sea level rise
Y. Miura*, K.T. Mandli, G. Deodatis. Natural Hazards Review (2021) 22(3): 04021018. [DOI]

The paper introduces the GIS-based subdivision-redistribution (GISSR) methodology for efficiently simulating storm surge floods in coastal urban areas. It combines GIS with Manning’s equation to calculate and redistribute water flow, accounting for protective measures and sea level rise. GISSR is highly accurate and computationally efficient compared to tools. It also can be used for nowcast using storm surge data.

A methodological framework for determining an optimal coastal protection strategy against storm surges and sea level rise
Y. Miura*, H. Qureshi, C. Ryoo, P.C. Dinenis, J. Li, K.T. Mandli, G. Deodatis, D. Bienstock, H. Lazrus, R. Morss. Natural Hazards (2021) 107(2): 1821-1843. [DOI]

This article proposes a framework for a methodology that combines multiple computational models, stakeholder interviews, and optimization to find an optimal protective strategy over time for critical coastal infrastructure while being constrained by budgetary considerations.