Cloud-Coverage-Detection

The purpose of this project is to build predictive models for cloud coverage prediction after the next 15, 25 and 30 minutes, given the data of past 360 minutes (6 hours) .

Documentation: https://docs.google.com/document/d/1NSPt0MgcluKspor2198lxPtN2d6iwSOQ0dYxeXy9AS4/edit?usp=sharing

Models Implemented

1. Simple RNN (Recurrent Neural Network)
2. GRU (Gated Recurrent Unit)
3. LSTM (Long Short Term Memory)
4. Custom LSTM (LSTM with concat layers)
5. AutoEncoders
6. Transformers
7. Conv1D + LSTM layers

Set-Up

Paste these commands in the command prompt of your working directory to get started.

git clone https://github.com/Kanishk-03-Jain/Cloud-Coverage-Detection.git
cd Cloud-Coverage-Detection
python3 -m pip install -r requirements.txt

Get weights from the drive link https://drive.google.com/drive/folders/1AHORQ4eyikaWXs-oBANKckeCJJS0M735?usp=sharing and save them in the weights folder which is present in the cloned file directory.

Also get the train.csv and CCD test.csv file from the above drive link