Hurricane Path Prediction using a Recurring Neural Network
In this repository you will find the code necessary to train a LSTM-based Neural Network to predict the next position of a hurricane.
The data used was retrieved from the NOAA HURDAT2 database.
As explained on their website it contains the geographic progression data of each hurricane since 1851 (currently am using a version downloaded in 2020, only goes up to 2015)
The hurricanes' coordinates are available for the duration of the hurricane at 4 times in the day : 0000, 0600, 1200, 1800.
I built the model using PyTorch.
It's a very shallow model, as I found out the resultss only worsened when I deepened the LSTM part.
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1 Input Layer with Batch Normalization
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1 LSTM Layer with 256 hidden features
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1 Dense (Fuully Connected Layer)
git clone https://github.com/Zhack47/Hurricane-Path-Prediction.git
cd Hurricane-Path-Prediction/
pip3 install -r requirements.txt
python3 train.py
python3 test.py