Results

• Above metrics indicate that Catboost model is perfectly fitted on the data with test RMSE of 10.19 & R2 of 0.88.
• The system successfully predicts cloud coverage (ranging from 0% to 100%) from skycam images, providing valuable weather information.
• Future opportunities include integrating the model with Skycamera and creating early alerting systems for climatic shifts.

Cloud coverage prediction is critical in weather forecasting and a variety of applications such as solar energy generation, aviation, and climate monitoring. Accurate forecasts help decision-makers and sectors plan for and adapt to changing weather conditions. The advancement of artificial intelligence and computer vision techniques in recent years has created new opportunities for enhancing cloud coverage forecasts.

One promising approach is the use of SkyCam images.

• In the face of rapidly changing global climate patterns, there is an urgent need for innovative tools and technologies to better understand and predict weather-related phenomena.
• One crucial aspect of climate analysis is the assessment of cloud coverage, which plays a pivotal role in influencing weather conditions and climate trends.
• Experts may not always be available to monitor climatic shifts. Therefore, developing an automated weather monitoring system is crucial for various applications, including agriculture and disaster management.

The purpose of this research is to estimate the opaque Cloud Coverage from a Skycam Image using AI/ML methodologies.