How Machine Learning Can Help Fill In Missing Rainfall Data

Rainfall is super important for understanding how water moves around our planet. It affects things like river flows, soil moisture, and even how much water plants use. But sometimes, we don't have complete data from weather stations. This can make it tough to get accurate results from studies about water. To solve this, scientists created a tool using something called machine learning. They tested it with 30 weather stations in South Korea. They looked at things like air pressure, temperature, humidity, and wind speed to predict missing rainfall data.

The tool was really good at finding when rain was missing and even predicting how much rain fell. It used two methods: a kind of smart computer network (artificial neural network) and something called a random forest. The smart network seemed to do a bit better. Even though both methods worked well, the smart network could predict rainfall a little more accurately. This means scientists can use this tool to improve how they study water cycles.

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