Spectral and Spatial Feature Integrated Ensemble Learning Method for Grading Urban River Network Water Quality

Abstract

Urban river networks have the characteristics of medium and micro scales, complex water quality, rapid change, and time–space incoherence. Aiming to monitor the water quality accurately, it is necessary to extract suitable features and establish a universal inversion model for key water quality parameters. In this paper, we describe a spectral- and spatial-feature-integrated ensemble learning method for urban river network water quality grading. We proposed an in situ sampling method for urban river networks. Factor and correlation analyses were applied to extract the spectral features. Moreover, we analyzed the maximum allowed bandwidth for feature bands. We demonstrated that spatial features can improve the accuracy of water quality grading using kernel canonical correlation analysis (KCCA). Based on the spectral and spatial features, an ensemble learning model was established for total phosphorus (TP) and ammonia nitrogen (NH3-N). Both models were evaluated by means of fivefold validation. Furthermore, we proposed an unmanned aerial vehicle (UAV)-borne water quality multispectral remote sensing application process for urban river networks. Based on the process, we tested the model in practice. The experiment confirmed that our model can improve the grading accuracy by 30% compared to other machine learning models that use only spectral features. Our research can extend the application field of water quality remote sensing to complex urban river networks.

Comparing Unmanned Aerial Multispectral and Hyperspectral Imagery for Harmful Algal Bloom Monitoring in Artificial Ponds Used for Fish Farming

“…As with Chl-a, the results in Table 3 also demonstrate the high potential of the Nano-Hyperspec camera to apply several cyanobacteria/HAB bio-optical models. Of the 14 models tested, 3 models obtained an R 2 ≥ 0.7, with emphasis on models #35, #28, and #29 (various studies have also tested and validated the efficiency of these models for estimating cyanobacteria/HAB in continental waters [9,[88][89][90][91]. The three-band model (model #35) originally proposed in [73] was robustly applied for monitoring cyanobacteria in fish-farming tanks, showing results compatible with those presented in this study.…”

Water Quality Prediction of Small-Micro Water Body Based on the Intelligent-Algorithm-Optimized Support Vector Machine Regression Method and Unmanned Aerial Vehicles Multispectral Data

“…Ren et al employed a GWO-SVR model in conjunction with Sentinel-2 satellite data to predict chlorophyll-a in Tangdao Bay, achieving an R 2 of 0.71 [67]. During the acquisition of spectral data, satellite remote sensing is susceptible to atmospheric scattering and surface light from the water body, and these influences cannot be eliminated [68]. In contrast, UAV remote sensing can eliminate atmospheric interference and efficiently capture water spectral parameters.…”

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

“…The performance of the regression models was assessed using four metrics. The first metric is the coefficient of determination (R 2 ) in Equation (7), which explains the variance score of a regression model, with values ranging from 0 to 1. A value closer to 1 indicates a more stable model, while a lower value suggests poorer performance.…”

“…However, for non-optically active parameters such as the total phosphorus (TP) and permanganate index (COD Mn ), the spectral characteristics within the visible-shortwave infrared range are imprecise, resulting in a limited accuracy from traditional satellite sensors for WQP inversion [6]. Moreover, in densely urbanized cities, urban rivers commonly exhibit widths ranging between 10 and 30 m [7]. These rivers present intricate spectral attributes and are highly susceptible to alterations in water quality due to human activities.…”