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Aavruti : Weather Nowcasting using Deep Learning and Satellite Imagery
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Weather prediction in India is a critical concern due to the diverse and often unpredictable climate patterns across the country. Traditional weather forecasting methods have limitations, particularly in predicting sudden and extreme weather changes. These limitations result in challenges for various sectors, including agriculture, disaster management, and public safety. The problem addressed by the "Aavruti" project is the need for more precise and timely weather predictions, especially for critical weather events like heatwaves, cloud bursts, cyclones, heavy rainfall, and wind speed. The project seeks to overcome these limitations by harnessing the power of deep learning and satellite imagery, aiming to provide more accurate nowcasting of short-term weather conditions.
The primary aim of the "Aavruti" project is to develop an advanced weather nowcasting system that utilizes deep learning techniques and satellite imagery to improve the accuracy and reliability of short-term weather predictions. The specific objectives of the project include:
Implementing Attention Transformers for feature extraction and Generative Adversarial Networks (GANs) for image generation to enhance the precision of weather forecasts.
Creating a weather prediction model for temperature using CATBoosting and StackGRU algorithms.
Providing real-time predictions for various weather phenomena, including heatwaves, cloud bursts, cyclones, rainfall, wind speed, and temperature.
Ensuring regular updates and refinements for improved performance and accuracy.
Developing a user-friendly interface for easy access and visualization of nowcasting results.
Optimizing the system for scalability and efficient real-time predictions.
The primary aim of the "Aavruti" project is to develop an advanced weather nowcasting system that utilizes deep learning techniques and satellite imagery to improve the accuracy and reliability of short-term weather predictions. The specific objectives of the project include:
Implementing Attention Transformers for feature extraction and Generative Adversarial Networks (GANs) for image generation to enhance the precision of weather forecasts.
Creating a weather prediction model for temperature using CATBoosting and StackGRU algorithms.
Providing real-time predictions for various weather phenomena, including heatwaves, cloud bursts, cyclones, rainfall, wind speed, and temperature.
Ensuring regular updates and refinements for improved performance and accuracy.
Developing a user-friendly interface for easy access and visualization of nowcasting results.
Optimizing the system for scalability and efficient real-time predictions.
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