Week 9 Blog Post: My AI Model Is Now Predicting Snowmelt

This week, my project came full circle. After weeks of gathering data, comparing snow depth patterns, and theorizing about the influence of elevation and burn severity, I’ve now turned those insights into a predictive curve—generated by a functional AI model.

The graph below shows the model’s simulated snow water equivalent (SWE) for the 2022–2023 water year. It follows a realistic seasonal pattern: snow begins to accumulate around November, peaks in March or April, and melts steadily into summer. This curve wasn’t pulled from historical data—it was created by code I built, informed by the trends I studied through camera site observations and statistical comparisons over the past two months.

The AI model works by ingesting elevation, burn severity, and simulated seasonal conditions, then applying a regression-based algorithm to estimate SWE values week by week. While the model is simplified, it offers a valuable tool that could one day be integrated into dam and reservoir infrastructure—predicting when water will be most abundant or dangerously scarce.

This visualization isn’t just a visual milestone—it’s a proof-of-concept that a student-led project can model environmental behavior and propose real-world solutions. I’m incredibly proud of this stage and can’t wait to document how this work can grow even further.