I made an LSTM neural network model that uses 30+ years of weather and streamflow data to quite accurately predict what the streamflow will be tomorrow.

The problem with river forecasts

The main reason I practice data science is to apply it to real-world problems. As a kayaker, I have spent many, many hours poring over weather forecasts, hydrologic forecasts, and SNOTEL station data to make a prediction about a river’s flow. There are good places out there that make this prediction- NOAA runs prediction centers throughout each major river basin in the country, including the South Fork.

But these forecasts often fall short. In particular, I’ve noticed that the forecasts are susceptible to major rain events (flashy rivers in the Pacific Northwest are notoriously hard to predict), and the forecasts are typically only put out once or twice per day, which is often not frequent enough to react to rapidly changing mountain weather forecasts. NOAA also only gives forecasts on a select group of rivers. If you want a forecast for a smaller or more remote drainage, even if it’s gauged, you’re out of luck.

So I’m setting out to create a model that will meet or exceed NOAA’s forecasts, and build models for some drainages that are not covered by NOAA.

To start out, I’m benchmarking my model against an industry-standard model created by Upstream Tech.

The South Fork Payette is a great place to start, for several reasons:

  1. The South Fork above Lowman is undammed, so the confounding variables of reservoirs are avoided.
  2. The USGS operates a gauge on the South Fork, NOAA has weather stations and a river forecast, and there are SNOTEL sites in the basin. There is a lot of easily accessible data to start with.
  3. I used to teach kayaking on the Payette and I’ve paddled almost every section of the river system, so I know the region and its hydrology well!

#machine-learning #lstm #hydrology #idaho #neural-networks

Predicting the flow of the South Fork Payette River using an LSTM neural network
1.45 GEEK