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.
Highlights
Among rivers, small- and medium-sized rivers are often ignored in daily water quality monitoring
We propose a spectral- and spatial-integrated ensemble learning method for urban river network water quality grading to meet the demand of comprehensive domain and high-frequency fine-scale monitoring
support vector machine (SVM) showed the worst results for total phosphorus (TP) grading, which was only approximately 0.25 for the three kinds of evaluation values
Summary
Small- and medium-sized rivers are often ignored in daily water quality monitoring. These rivers are places where water pollution often occurs [1], especially in the cities of developing countries [2,3]. There have been a large number of studies on remote sensing water quality monitoring [7]. A large quantity of these studies focused on Case 1 waters and Case 2 waters, such as seas, large rivers, and lakes [8,9,10]. These studies were mainly based on images of specific bands [11]. Considering the complexity of urban water systems, it is necessary to extract suitable features and establish a universal inversion model for key water quality parameters
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