Severe weather events, from hurricanes and tornadoes to floods and blizzards, can have devastating impacts on communities, infrastructure and economies. Something the southeast of the United States knows very well.

Accurate forecasting plays a crucial role in helping people prepare for these events, giving them time to take action and minimize the damage. While traditional weather forecasting methods have come a long way, machine learning is taking severe weather predictions to the next level, making forecasts faster and more accurate than ever before. This important and interesting information came to our attention in their article, “Machine learning could improve extreme weather warnings.”

Predicting severe weather has always been tricky. Weather systems are incredibly complex, influenced by a wide range of factors, such as temperature, humidity, air pressure and wind patterns. These factors interact in ways that can be hard to predict, especially when it comes to sudden or extreme events like tornadoes or flash floods. Traditional forecasting models rely on physics-based equations to simulate these interactions, but they have limitations, particularly when it comes to short-term or highly localized predictions.

That’s where machine learning comes in. Algorithms can analyze vast amounts of data, detect patterns and make predictions faster and more accurately than traditional methods.

Machine learning models are already being used to predict the path and intensity of hurricanes. By analyzing data from past hurricanes, as well as real-time satellite and radar information, these models can give more accurate estimates of where a hurricane will make landfall and how strong it will be when it gets there.

As machine learning technology continues to advance, its role in severe weather forecasting will only grow. Machine learning models will likely work alongside other AI technologies to create even more sophisticated weather prediction systems. As this technology continues to develop, we can expect even more improvements in how we predict and respond to extreme weather.

The biggest challenge is that most organizations have little knowledge on how artificial intelligence (AI) systems make decisions and how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and it potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, uniquely positioned to help you in your AI journey.