Early Detection of Extreme Storm Tide Events Using Multimodal Data Processing

Marcel Barros, Andressa Pinto, Andres Monroy, Felipe Moreno, Jefferson Coelho, Aldomar Pietro Silva, Caio Fabricio Deberaldini Netto, José Roberto Leite, Marlon Mathias, Eduardo Tannuri, Artur Jordão, Edson Gomi, Fábio Cozman, Marcelo Dottori, Anna Helena Reali Costa

Research output: Contribution to journalConference articlepeer-review

Abstract

Sea-level rise is a well-known consequence of climate change. Several studies have estimated the social and economic impact of the increase in extreme flooding. An efficient way to mitigate its consequences is the development of a flood alert and prediction system, based on highresolution numerical models and robust sensing networks. However, current models use various simplifying assumptions that compromise accuracy to ensure solvability within a reasonable timeframe, hindering more regular and costeffective forecasts for various locations along the shoreline. To address these issues, this work proposes a hybrid model for multimodal data processing that combines physics-based numerical simulations, data obtained from a network of sensors, and satellite images to provide refined wave and seasurface height forecasts, with real results obtained in a critical location within the Port of Santos (the largest port in Latin America). Our approach exhibits faster convergence than data-driven models while achieving more accurate predictions. Moreover, the model handles irregularly sampled time series and missing data without the need for complex preprocessing mechanisms or data imputation while keeping low computational costs through a combination of time encoding, recurrent and graph neural networks. Enabling raw sensor data to be easily combined with existing physics-based models opens up new possibilities for accurate extreme storm tide events forecast systems that enhance community safety and aid policymakers in their decision-making processes.

Original languageEnglish (US)
Pages (from-to)21923-21931
Number of pages9
JournalProceedings of the AAAI Conference on Artificial Intelligence
Volume38
Issue number20
DOIs
StatePublished - Mar 25 2024
Externally publishedYes
Event38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, Canada
Duration: Feb 20 2024Feb 27 2024

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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