Though predictive analytics has been around for decades, it’s a technology whose time has come. More and more organizations are turning to predictive analytics to increase their bottom line and competitive advantage. However, the one that comes to mind is meteorology. This interesting information came to us from EurekAlert! in their article, “Machine learning produces more accurate rainfall predictions.”

As you might expect, data and data analytics rule in generating weather forecast success. But it is more than just knowing how to prepare the kids for school or to roll the car windows up. Understanding the timing and scale of local precipitation is crucial for managing water resources. Management of water resources and planning for water-related disasters require accurate estimates of local precipitation. 

Precipitation can vary enormously in time and space. Researchers are using a machine learning method that recognizes the complex relationships involved in local precipitation. Using these methods, they were able to reproduce the long-term spatial distribution of local precipitation with high accuracy. Understanding local precipitation patterns is vital for planning, both in terms of management of water resources and response to hazards, such as flooding events.

Most organizations have little knowledge on how artificial intelligence (AI) systems make the decisions they do, and as a result, how the results are being applied in the various fields that AI and machine learning are being applied. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.

Melody K. Smith

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

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.