Publication Date

1-1-2024

Document Type

Article

Publication Title

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volume

17

DOI

10.1109/JSTARS.2024.3364020

First Page

5121

Last Page

5136

Abstract

Continuous monitoring of water quality parameters (WQPs) is crucial due to the global degradation of water quality, primarily caused by climate change and population growth. Typically, machine learning (ML) models are employed to retrieve WQPs, but they require a large amount of training samples to accurately capture the data relationships. Even with sufficient training data, discrepancies still exist between values of predicted and in-situ WQPs. This study proposes a fuzzy similarity analysis (FSA) technique to enhance ML estimates of WQPs by using the prediction errors in effective training samples. The method was successfully applied to retrieve turbidity (Turb) and specific conductance (SC) in Lake Houston, USA, using Sentinel-2 remote sensing data. Three ML algorithms, namely mixture density networks, support vector regression, and partial least squares regression, were tested to evaluate the method's effectiveness. The results showed that FSA significantly improved the accuracy of all ML predictions. This improvement resulted in up to a 9.15% reduction in mean absolute percentage error and a 12% increase in R2 for Turb, while for SC, the improvements were 5.47% in MAPE and 7% in R2. The adaptability of the proposed method to other WQPs, various satellite data, and different ML models is promising for monitoring water quality in inland waters.

Keywords

Fuzzy similarity analysis (FSA), google earth engine, machine learning (ML), remote sensing (RS), water quality

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Department

Civil and Environmental Engineering

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