Machine learning regression on hyperspectral data to estimate multiple water parameters
This work addresses water quality monitoring for environmental science, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of estimating multiple water parameters from hyperspectral data using machine learning regression, achieving results that demonstrate the potential of this approach, though no concrete numbers are provided.
In this paper, we present a regression framework involving several machine learning models to estimate water parameters based on hyperspectral data. Measurements from a multi-sensor field campaign, conducted on the River Elbe, Germany, represent the benchmark dataset. It contains hyperspectral data and the five water parameters chlorophyll a, green algae, diatoms, CDOM and turbidity. We apply a PCA for the high-dimensional data as a possible preprocessing step. Then, we evaluate the performance of the regression framework with and without this preprocessing step. The regression results of the framework clearly reveal the potential of estimating water parameters based on hyperspectral data with machine learning. The proposed framework provides the basis for further investigations, such as adapting the framework to estimate water parameters of different inland waters.