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README.md
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# Cloud Cover Nowcasting
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This project aims to predict cloud cover using a sequence-to-sequence ConvLSTM (Convolutional Long Short-Term Memory) model. The goal is to predict future cloud cover based on past satellite images. The model uses satellite .tif images taken at regular intervals to forecast cloud patterns, aiding in weather prediction and climate monitoring.
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## Run Predict
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To make predictions on a new set of satellite images, use the following command:
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`python /predict.py`
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---
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license: mit
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language:
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- en
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metrics:
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- accuracy
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- mse
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pipeline_tag: time-series-forecasting
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library_name: keras
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tags:
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- deeplearning
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- ConvoLSTM
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- Cloudcover-prediction
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- Satellite
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- data
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- dataanalysis
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- timeseries
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- code
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- tensorflow
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- machinelearning
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---
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# Cloud Cover Nowcasting
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This project aims to predict cloud cover using a sequence-to-sequence ConvLSTM (Convolutional Long Short-Term Memory) model. The goal is to predict future cloud cover based on past satellite images. The model uses satellite .tif images taken at regular intervals to forecast cloud patterns, aiding in weather prediction and climate monitoring.
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## Run Predict
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To make predictions on a new set of satellite images, use the following command:
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`python /predict.py`
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