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10 papers analyzed
These studies suggest that predicting and tracking hurricanes and other natural disasters can be effectively achieved using machine learning frameworks, deep convolutional neural networks, convection-permitting forecast systems, ensemble Kalman filters, recurrent neural networks, the Advanced Hurricane WRF model, consensus models, underwater hydrophone arrays, and statistical downscaling approaches.
20 papers analyzed
Predicting and tracking hurricanes and other natural disasters is crucial for mitigating their impact on human life and property. Advances in machine learning, data assimilation, and numerical modeling have significantly improved the accuracy and reliability of these predictions.
Machine Learning and Deep Learning Models:
Data Assimilation Techniques:
Numerical and Statistical Models:
Acoustic Detection:
The prediction and tracking of hurricanes and other natural disasters have seen significant advancements through the integration of machine learning, data assimilation, and numerical modeling techniques. Machine learning models, particularly deep learning, have enhanced the accuracy of intensity and trajectory predictions. Data assimilation of high-resolution observations has further improved forecast reliability. Numerical models like the Advanced Hurricane WRF and statistical downscaling methods have also contributed to better hurricane tracking. Additionally, innovative approaches such as acoustic detection offer potential solutions for regions with limited satellite coverage. These advancements collectively enhance our ability to predict and mitigate the impacts of natural disasters.
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