Deep Learning Weather Prediction System
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A predictive weather system that combines computational fluid dynamics with machine learning and meteorological data-sources to give a more accurate and near real-time alternative to current weather prediction models. This dynamic and fluid forecast software aggregates a multitude of climate and weather-related data derived from sensors and satellites. This data is rated, ranked, and weighted by machine learning to process immediate comparisons between historical data and weather forecasts.
Apart from developing rankings, machine-learning models powered with deep learning show promising results in predicting cyclones, atmospheric rivers, weather fronts, and forest fires and can even assess deforestation and other sustainability measurements. The weather model can support decision-making processes in farming, city planning, or as a way for companies and governments to enhance their sustainability practices regarding natural systems.
Without the appropriate actions and democratization of knowledge provided by this model, the general population might not use this technology, keeping the predictive abilities within the boundaries of privileged groups that will use the information solely to advance their own interests.
Improves the informational systems that enhance the adaptive capacity of women and ethnic minorities, especially in rural settings.
For populations that are subject to natural disasters, this may enable a faster emergency response.