Journal Article

Assessing the Risk of Extreme Storm Surges from Tropical Cyclones under Climate Change Using Bidirectional Attention-Based LSTM for Improved Prediction

Ian, Vai-Kei, Pau, Giovanni, Tang, Su-Kit

2023

Overview

Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical and real-time data, BALSSA significantly enhances prediction accuracy. Utilizing a bidirectional attention-based LSTM framework, it captures complex, non-linear relationships and long-term dependencies, improving the accuracy of storm surge predictions. The enhanced model, D-BALSSA, further amplifies predictive capability through a doubled bidirectional attention-based structure. Training and evaluation involve a comprehensive dataset from over 70 typhoon incidents in Macao between 2017 and 2022. The results showcase the outstanding performance of BALSSA, delivering highly accurate storm surge forecasts with a lead time of up to 72 h. Notably, the model exhibits a low Mean Absolute Error (MAE) of 0.0287 m and Root Mean Squared Error (RMSE) of 0.0357 m, crucial indicators measuring the accuracy of storm surge predictions in water level anomalies. These metrics comprehensively evaluate the model’s accuracy within the specified timeframe, enabling timely evacuation and early warnings for effective disaster mitigation. An adaptive system, integrating real-time alerts, tropical cyclone (TC) chaser, and prospective visualizations of meteorological and tidal measurements, enhances BALSSA’s capabilities for improved storm surge prediction. Positioned as a comprehensive tool for risk management, BALSSA supports decision makers, civil protection agencies, and governments involved in disaster preparedness and response. By leveraging advanced machine learning techniques and extensive data, BALSSA enables precise and timely predictions, empowering coastal communities to proactively prepare and respond to extreme weather events. This enhanced accuracy strengthens the resilience of coastal communities and protects lives and infrastructure from the escalating threats of climate change.

• Publisher: MDPI AG
• Subject: Accuracy / Adaptive systems / Anomalies / Artificial intelligence / Atmospheric pressure / China / Climate and weather / Climate change / Climatic changes / coastal hazards / Coastal protection / Cyclones / Datasets / Decision making / Deep learning / Disaster management / Emergency communications systems / Emergency preparedness / Environmental aspects / Extreme weather / Floods / Hurricanes / Lead time / Learning algorithms / Long short-term memory / Machine learning / Methods / Mitigation / Model accuracy / Modelling / Neural networks / Predictions / Property damage / Real time / Risk assessment / Risk management / Root-mean-square errors / Sea level / Storm damage / Storm forecasting / storm surge / Storm surge forecasting / Storm surge prediction / Storm surges / Storms / Tidal waves / tropical cyclone / Tropical cyclones / Typhoons / Water levels / Weather / Weather forecasting