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Precipitation nowcasting: This promotes sustainable environments by providing accurate and timely information on precipitation patterns, allowing for better management of natural resources such as water and land.
Some examples of smart applications for precipitation nowcasting include:
Weather Radar: This involves the use of advanced radar systems to detect precipitation in real-time, providing accurate and up-to-date information on rainfall patterns and intensity.
Weather Models: This involves the use of computer models to simulate and predict precipitation patterns, allowing for accurate forecasting and nowcasting.
Mobile Applications: This involves the use of mobile applications that use GPS and other sensors to provide real-time information on precipitation patterns, allowing for better planning and decision-making.
Smart Irrigation Systems: This involves the use of sensors and weather data to optimize irrigation practices, reducing water waste and promoting sustainable water use.
Flood Prediction and Management: This involves the use of sensors and predictive models to identify areas at risk of flooding, allowing for early warning and effective management of flood events.
Precipitation nowcasting is the process of predicting rainfall at short periods ahead like 0-6hours. This is not the general rainfall forecast we hear everyday in the news. Instead this focusses on giving an accurate prediction with a high resolution in both time and space. The project use AI technologies to provide precise, short-term rainfall predictions, enhancing decision-making across various sectors. This offers a deep learning model trained on multivariate spatio-temporal datasets, including rain radar, satellite, and wind speed data that predicts rainfall 30 minutes ahead with high spatial resolution, offering detailed insights into weather patterns. The key feature the solution is the integration of the Integrated Gradients method for explainable artificial intelligence(XAI). This technique allows to break down the model’s decision-making process, identifying the contribution of each data source and highlighting the specific regions and features that influence predictions. By providing a visual representation of these contributions, our approach enhances transparency and trust in AI-generated forecasts. This is particularly crucial for sectors like aviation and maritime industries, where accurate and explainable weather predictions are vital.
Awarded as the Best Solution Addressing National Disasters at National ICT Awards - NBQSA 2024
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