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Explore our blogs to learn about the latest advancements in multimodal machine learning, environmental science, and our innovative approaches to weather prediction and climate data analysis.

July 8, 20257 min readFeatured

MML - Flood

Flooding is one of the most damaging natural disasters we face—and with climate change, the risks are only growing. In our latest research, we’ve developed a machine learning model that combines different types of data—like geographic information and past disaster records—to predict where floods are likely to happen over the next 1 to 5 years. By using both text and numbers together, our model performs significantly better than traditional methods, reaching up to 77% accuracy. It’s a step forward in using AI to plan ahead and reduce the impact of natural disasters.

by Nicole Zhang & Zein Mukhanov

Flood PredictionMachine LearningClimate Change
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July 8, 20255 min readFeatured

MML - Drought

Droughts are silent killers—slow-moving but devastating. Now, AI may help predict them years in advance by combining climate stats with geographic clues from sources like Wikipedia, outperforming traditional models by 23%. This breakthrough could buy crucial time to protect crops, economies, and lives before the water runs dry.

by Nicole Zhang & Zein Mukhanov

Drought PredictionMachine LearningClimate Change
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July 8, 20255 min readFeatured

MML - Hurricane

As hurricanes grow stronger and more unpredictable, the race to predict them years in advance has never been more urgent. Traditional weather models excel at short-term forecasts but struggle with long-range predictions—until now. Our latest research combines AI, historical storm data, and even Wikipedia’s geographic insights to forecast hurricane risks up to five years ahead, with a 20-25% improvement over conventional methods. The goal? To give coastal cities, insurers, and disaster responders a crucial head start against the storms of tomorrow.

by Nicole Zhang & Zein Mukhanov

Hurricane PredictionMachine LearningClimate Change
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