Early Surge Warning Using a Machine Learning System with Real-Time Surveillance Camera Images

Abstract

While extreme oceanic phenomena can often be accurately predicted, sudden abnormal waves along the shore (surge) are often difficult to foresee; therefore, an immediate sensing system was developed to monitor sudden and extreme events to take necessary actions to prevent further risks and damage. Real-time images from coastal surveillance video and meteorological data were used to construct a warning model for incoming waves using long short-term memory (LSTM) machine learning. This model can predict the wave magnitude that will strike the destination area seconds later and issue an alarm before the surge arrives. The warning model was trained and tested using 110 h of historical data to predict the wave magnitude in the destination area 6 s ahead of its arrival. If the forecasting wave magnitude exceeds the threshold value, a warning will be issued, indicating that a surge will strike in 6 s, alerting personnel to take the necessary actions. This configuration had an accuracy of 60% and 88% recall. The proposed prediction model could issue a surge alarm 5 s ahead with an accuracy of 90% and recall of 80%. For surge caused by a typhoon, this approach could offer 10 s of early waring with recall of 76% and an accuracy of 74%.

Keywords

• sea wave
• nearshore
• coastal management
• machine learning
• long short-term memory
• surge