TY - JOUR
T1 - Early Detection of Extreme Storm Tide Events Using Multimodal Data Processing
AU - Barros, Marcel
AU - Pinto, Andressa
AU - Monroy, Andres
AU - Moreno, Felipe
AU - Coelho, Jefferson
AU - Silva, Aldomar Pietro
AU - Netto, Caio Fabricio Deberaldini
AU - Leite, José Roberto
AU - Mathias, Marlon
AU - Tannuri, Eduardo
AU - Jordão, Artur
AU - Gomi, Edson
AU - Cozman, Fábio
AU - Dottori, Marcelo
AU - Costa, Anna Helena Reali
N1 - Publisher Copyright:
© 2024, Association for the Advancement of Artifcial Intelligence (www.aaai.org). All rights reserved.
PY - 2024/3/25
Y1 - 2024/3/25
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85189639716&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85189639716&partnerID=8YFLogxK
U2 - 10.1609/aaai.v38i20.30194
DO - 10.1609/aaai.v38i20.30194
M3 - Conference article
AN - SCOPUS:85189639716
SN - 2159-5399
VL - 38
SP - 21923
EP - 21931
JO - Proceedings of the AAAI Conference on Artificial Intelligence
JF - Proceedings of the AAAI Conference on Artificial Intelligence
IS - 20
T2 - 38th AAAI Conference on Artificial Intelligence, AAAI 2024
Y2 - 20 February 2024 through 27 February 2024
ER -