Random forest models for predicting water quality of inland waters from remotely sensed imagery
Description
Inland waterbodies (i.e. rivers, lakes, reservoirs, ponds, etc.) can face issues of poor water quality which may pose issues to water users, water infrastructure, and ecosystems. While it is important to monitor the water quality of these waterbodies, it can be costly and labor intensive to continuously monitor sites across the US. This project aims to improve the efficiency of monitoring by using Machine Learning (Random Forest) models to predict two water quality parameters of waterbodies seen in remotely sensed images. These remotely sensed images span the entire Conterminous US (CONUS) and are collected approximately every 5 days, allowing for water quality monitoring at high temporal and spatial resolutions with relatively low effort and cost.
Detailed example
Prediction of chlorophyll concentrations and turbidity values in remotely sensed images of water bodies.
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
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
cost savings by estimating water quality parameters across the entire country on a frequent (5 day) schedule with minimal labor. By making these modeled water quality products publicly available, it may aid in managing water resources
Controls / human review
ATO: No; PIA: Not published
Data needed
Data used to train the models are remotely sensed images from the European Space Agency's Sentinel-2 satellite program and in-situ USGS water quality data