yty3805595/typhoon_prediction

# typhoon Analysis satellite images of typhoons by deep-learning (CNN), based on PyTorch. This CNN learns to map the satellite images of typhoons to their max wind speed from. The labeled train set is obtained from agora/JMA. ## Requirements * BeautifulSoup * PIL * Pytorch ## Usage 1. Run `download.py`, download the satellite images of typhoons to folder `tys_raw`. 2. Run `create_samples.py`, convert raw data into the legal samples for our CNN, create two new forlder `train_set` and `test_set`. 3. Train CNN using `train_net.py`, the trained CNN will be saved as a disk file `net_relu.pt`. 4. Run `test_net.py`, analysis the test set. After 10 epoches training the CNN regressor reached mean loss about 8 (knots) in train set and about 10 (knots) in test set.  Here is what this CNN thinks of the top 20 typhoons sorted by max wind. ``` 1 ('197920', 130.27679443359375) 2 ('200914', 127.7662582397461) 3 ('199019', 122.92172241210938) 4 ('200918', 122.84004211425781) 5 ('201614', 122.66597747802734) 6 ('201601', 122.03250885009766) 7 ('201513', 121.75947570800781) 8 ('200922', 121.35771942138672) 9 ('201013', 120.0194091796875) 10 ('201330', 118.92587280273438) 11 ('201419', 117.6025390625) 12 ('198305', 117.10270690917969) 13 ('201422', 116.77259063720703) 14 ('198522', 116.46116638183594) 15 ('201327', 116.42304992675781) 16 ('201216', 116.36921691894531) 17 ('198221', 116.18096923828125) 18 ('199230', 115.96656799316406) 19 ('198210', 115.96611022949219) 20 ('201328', 115.57132720947266) ``` ## Tips * Memory should be at least 1.5G . * This project is written without `cuda()`, while you can use `cuda()` to transfer the CNN onto GPU and speedup the training. * The images and labels are crawled from agora.ax.nii.ac.jp/digital-typhoon , and the labels are refered to JMA(Japan Meteorological agency).

typhoon_prediction

typhoon Analysis satellite images of typhoons by deep-learning (CNN), based on PyTorch. This CNN learns to map the satellite images of typhoons to their max wind speed from. The labeled train set is obtained from agora/JMA.

Requirements * BeautifulSoup * PIL * Pytorch

Usage

1. Run download.py, download the satellite images of typhoons to folder tys_raw.
2. Run create_samples.py, convert raw data into the legal samples for our CNN, create two new forlder train_set and test_set.
3. Train CNN using train_net.py, the trained CNN will be saved as a disk file net_relu.pt. 4. Run test_net.py, analysis the test set.
   After 10 epoches training the CNN regressor reached mean loss about 8 (knots) in train set and about 10 (knots) in test set.

2 ('200914', 127.7662582397461)   
3 ('199019', 122.92172241210938)   
4 ('200918', 122.84004211425781)   
5 ('201614', 122.66597747802734)   
6 ('201601', 122.03250885009766)   
7 ('201513', 121.75947570800781)   
8 ('200922', 121.35771942138672)   
9 ('201013', 120.0194091796875)   
10 ('201330', 118.92587280273438)   
11 ('201419', 117.6025390625)   
12 ('198305', 117.10270690917969)   
13 ('201422', 116.77259063720703)  
14 ('198522', 116.46116638183594)   
15 ('201327', 116.42304992675781)   
16 ('201216', 116.36921691894531)   
17 ('198221', 116.18096923828125)   
18 ('199230', 115.96656799316406)   
19 ('198210', 115.96611022949219)   
20 ('201328', 115.57132720947266)   `
``  ## Tips * Memory should be at least 1.5G .   
* This project is written without `cuda()`, while you can use `cuda()` to transfer the CNN onto GPU and speedup the training.   * The images and labels are crawled from agora.ax.nii.ac.jp/digital-typhoon , and the labels are refered to JMA(Japan Meteorological agency).