Drift compensation of commercial water quality sensors using machine learning to extend the calibration lifetime

Remote sensing monitoring has the characteristics of wide monitoring range, celerity, low cost for long-term dynamic monitoring of water environment. With the flourish of artificial intelligence, machine learning has enabled remote sensing inversion of seawater quality to achieve higher prediction accuracy. However, due to the physicochemical property of the water quality parameters, the performance of algorithms differs a lot. In order to improve the predictive accuracy of seawater quality parameters, we proposed a technical framework to identify the optimal machine learning algorithms using Sentinel-2 satellite and in-situ seawater sample data. In the study, we select three algorithms, i.e. support vector regression (SVR), XGBoost and deep learning (DL), and four seawater quality parameters, i.e. dissolved oxygen (DO), total dissolved solids (TDS), turbidity(TUR) and chlorophyll-a (Chla). The results show that SVR is a more precise algorithm to inverse DO (R2 = 0.81). XGBoost has the best accuracy for Chla and Tur inversion (R2 = 0.75 and 0.78 respectively) while DL performs better in TDS (R2 =0.789). Overall, this research provides a theoretical support for high precision remote sensing inversion of offshore seawater quality parameters based on machine learning.

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Predicting stream water quality under different urban development pattern scenarios with an interpretable machine learning approach

The Science of The Total Environment ◽

10.1016/j.scitotenv.2020.144057 ◽

2021 ◽

Vol 761 ◽

pp. 144057

Author(s):

Runzi Wang ◽

Jun-Hyun Kim ◽

Ming-Han Li

Keyword(s):

Machine Learning ◽

Water Quality ◽

Urban Development ◽

Stream Water ◽

Development Pattern ◽

Learning Approach ◽

Stream Water Quality ◽

Interpretable Machine Learning ◽

Machine Learning Approach

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Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15091881 ◽

2018 ◽

Vol 15 (9) ◽

pp. 1881 ◽

Cited By ~ 18

Author(s):

Sina Keller ◽

Philipp Maier ◽

Felix Riese ◽

Stefan Norra ◽

Andreas Holbach ◽

...

Keyword(s):

Machine Learning ◽

Water Quality ◽

Chlorophyll A ◽

Green Algae ◽

Hyperspectral Data ◽

Coefficient Of Determination ◽

Inland Waters ◽

Learning Approaches ◽

River Elbe ◽

Regression Framework

Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters are spatially limited, costly and time-consuming. In this paper, we propose a combination of hyperspectral data and machine learning methods to estimate and therefore to monitor different parameters for water quality. In contrast to commonly-applied techniques such as band ratios, this approach is data-driven and does not rely on any domain knowledge. We focus on CDOM, chlorophyll a and turbidity as well as the concentrations of the two algae types, diatoms and green algae. In order to investigate the potential of our proposal, we rely on measured data, which we sampled with three different sensors on the river Elbe in Germany from 24 June–12 July 2017. The measurement setup with two probe sensors and a hyperspectral sensor is described in detail. To estimate the five mentioned variables, we present an appropriate regression framework involving ten machine learning models and two preprocessing methods. This allows the regression performance of each model and variable to be evaluated. The best performing model for each variable results in a coefficient of determination R 2 in the range of 89.9% to 94.6%. That clearly reveals the potential of the machine learning approaches with hyperspectral data. In further investigations, we focus on the generalization of the regression framework to prepare its application to different types of inland waters.

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