At present, numerical model forecasting consumes too many resources and takes too long to compute, while neural network forecasting lacks regional data to train regional forecasting models In this study, we used the dual wind model. In this study, we used the dual wind model to.
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Machine learning avoids these, but it also faces some problems, such as delays in predicting results, short prediction durations, and large data demands
Therefore, we built a pinn model to integrate storm surge physics with neural networks to reduce the need for data and improve the accuracy of storm surge forecasting.
In addition to improving those methods, my team and other researchers have been developing ways to use ai for storm surge prediction using observed data, assessing the damage after hurricanes and. Abstract accurate and timely storm surge prediction is critical information in coastal zone management and risk reduction strategies The bohai sea, a semi‐enclosed bay in the northwest pacific that used to be less prone to typhoon disasters, has been witnessing a paradigm shift in typhoon activities in the recent past. Storm surge disasters result in severe casualties and economic losses
Accurate prediction of storm surge water level is crucial for disaster assessment, early warning, and effective disaster management Machine learning methods are relatively more efficient and straightforward compared to numerical simulation approaches However, most of the current research on storm surge water level. <p>timely and accurate forecasting of storm surges can effectively prevent typhoon storm surges from causing large economic losses and casualties in coastal areas
