Medium-Sized Lake Water Quality Parameters Retrieval Using Multispectral UAV Image and Machine Learning Algorithms: A Case Study of the Yuandang Lake, China

Abstract

Water quality monitoring of medium-sized inland water is important for water environment protection given the large number of small-to-medium size water bodies in China. A case study was conducted on Yuandang Lake in the Yangtze Delta region, with a surface area of 13 km2. This study proposed utilising a multispectral uncrewed aerial vehicle (UAV) to collect large-scale data and retrieve multiple water quality parameters using machine learning algorithms. An alternate processing method is proposed to process large and repetitive lake surface images for mapping the water quality data to the image. Machine learning regression methods (Random Forest, Gradient Boosting, Backpropagation Neural Network, and Convolutional Neural Network) were used to construct separate water quality inversion models for ten water parameters. The results showed that several water quality parameters (CODMn, temperature, pH, DO, and NC) can be retrieved with reasonable accuracy (R2 = 0.77, 0.75, 0.73, 0.67, and 0.64, respectively), although others (NH3-N, BGA, TP, Turbidity, and Chl-a) have a determination coefficient (R2) less than 0.6. This work demonstrated the tremendous potential of employing multispectral data in conjunction with machine learning algorithms to retrieve multiple water quality parameters for monitoring medium-sized bodies of water.

A Machine-Learning-Based Framework for Retrieving Water Quality Parameters in Urban Rivers Using UAV Hyperspectral Images

“…In contrast, XGBR incorporates regularization techniques to reduce overfitting, enhancing generalization capabilities and resulting in more excellent performance, but relatively conservative results where specific extreme values might not be precisely estimated [49]. Compared with published articles [30,50], the regression accuracy of the COD Mn in this paper was relatively higher. Therefore, it is crucial to select appropriate models based on actual WQPs.…”

“…The sensitive bands for SD were predominantly distributed within 400-650 nm in Figure 9. Previous studies have indicated that the most correlated spectral bands for TUB are the blue bands within 400-500 nm [51] and the near-infrared band within 720-850 nm [50], with the optimal sensitive bands at 808 nm, 850 nm, and 880 nm [52]. In Figure 9, the sensitive bands corresponding to the optimal WQP inversion fell within the 404-635 nm range, consistent with previous research [51] and encompassed some spectral characteristics specific to the study area's rivers.…”

Retrieval of Water Quality Parameters in Dianshan Lake Based on Sentinel-2 MSI Imagery and Machine Learning: Algorithm Evaluation and Spatiotemporal Change Research

“…The selection of these methods was grounded in their performance and characteristics across various data scenarios. Moreover, these techniques have been demonstrated as successful applications in estimating water quality parameters in several inland lakes previously [18,24,29,38,[50][51][52][53][54].…”

CoastalWQL: An Open-Source Tool for Drone-Based Mapping of Coastal Turbidity Using Push Broom Hyperspectral Imagery

“…This major issue was highlighted in a recent study mapping submerged plastic in a coastal environment using the Bayspec's OCI-F pushbroom hyperspectral sensor [14] when limited illumination together with the dynamic sea surface and featureless water surface resulted in the failure in orthomosaicking of push-broom imagery. As such, most of the current studies involving UAV-based water quality monitoring are conducted at near-shore regions, over small lakes/reservoirs, or flown at higher altitudes to capture more textures in the scene, such as shorelines [3,[14][15][16][17][18][19][20]. Instead of the above feature-based approach, direct georeferencing of the UAV imagery using the coordinate information and positioning data from the GPS module and Inertial Measurement Unit (IMU) have been used to facilitate orthomosaicking.…”