Methods and systems for predicting weather events and their extremes using deep learning are disclosed. This invention focuses on improving the prediction of weather systems on local (10 km x 10 km) and hyperlocal (2 km x 2 km) scales using deep learning techniques. It integrates extensive data from models, satellite imagery, ground sensors, and other measurement techniques with advanced data mining algorithms to handle large datasets intelligently and provide statistical correlations. The approach employs Long Short-Term Memory (LSTM) based neural networks to create a meteorology-driven machine learning model that significantly enhances predictive accuracy and reliability for various meteorological parameters such as wind speed, direction, air pressure, temperature, humidity, and solar insolation. This method has demonstrated improvements in forecast accuracy by 15% to 100% over traditional Numerical Weather Prediction (NWP) models. The outputs from this model are suitable for creating city-scale disaster maps and can be integrated with hardware systems for real-time forecasts and early warning messages. The invention is particularly beneficial for applications in disaster management, agriculture, energy, and urban planning.
Type of IP
Faculty
Category of Patent
Department
- Enhanced ability to predict and manage extreme weather events can lead to better preparedness and reduced impact on human life and property.
- Improved agricultural planning can increase food security.
- Better resource management in urban and rural areas.
- Use of LSTM-based neural networks for weather prediction.
- Intelligent data mining from various sources such as models, satellite imagery, and ground sensors.
- Enhanced accuracy and reliability of forecasts for local (10 km x 10 km) and hyperlocal (2 km x 2 km) scales.
- Capability to predict various meteorological parameters like temperature, rainfall, and wind speed.
- Incorporation of scaling factors for bias correction.
- Integration with hardware for real-time forecasts and early warning messages.
- The combination of deep learning techniques with meteorological data for improved predictive accuracy.
- Significant improvement in accuracy (15% to 100%) over existing NWP models.
- Ability to provide city-scale disaster maps and integrate forecasts with real-time warning systems.
7
Currently testing this product with industry requirements and trying to find a suitable alignment for licensing and commercialization.
Meteorology and Weather Forecasting, Disaster Management, Agriculture, Energy and Water Management, Urban Planning
- Weather prediction for local and hyperlocal areas.
- Forecasting extreme weather events such as thunderstorms, localized flooding, and heatwaves.
- City-scale disaster management and planning.
- Agricultural planning and water resource management.