USGS Flow Photo Explorer [2024 INV#WO0000000109196]
Description
The Flow Photo Explorer (FPE) is an integrated database, machine learning, and data visualization platform for monitoring streamflow and other hydrologic conditions using timelapse images. The goal of this project is to develop new approaches for collecting hydrologic data in streams, lakes, and other waterbodies, especially in places where traditional monitoring methods and technologies are not feasible or cost-prohibitive. FPE uses an artificial intelligence/machine learning (AI/ML) deep learning model to estimate relative streamflow using timelapse imagery. The model is trained using pairs of images for which a person (a.k.a. an annotator) has selected which of the two images in each pair appears to have more flow. From this, the model learns how to sort the images from lowest to highest apparent flow. The rankings of the sorted images then serve as indicators of the relative amount of streamflow.
Detailed example
Predicted relative flow hydrograph.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
cost savings, increased safety for employees and the public
Controls / human review
ATO: No; PIA: Not published
Data needed
The model is trained on human annotations of images captured by trail cameras. See the USGS Flow Photo Explorer to view training data and interface. Model performance is evaluated with timeseries streamflow data collected by collocated USGS streamgages. The streamflow data is made publicly available via the USGS National Water Information System (NWIS).