Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data

Chen, Botao; Mu, Xuemei; Chen, Peng; Wang, Biao; Choi, Jaewon; Park, Honglyun; Xu, Sheng; Wu, Yue; Yang, Hui

doi:10.1016/j.ecolind.2021.108434

Cited by 71 publications

(50 citation statements)

References 33 publications

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“…The AdaBoost algorithm is an integrated learning algorithm, based on the boosting algorithm framework. As an effective statistical learning algorithm, the AdaBoost algorithm is not susceptible to overfitting issues and is widely used in classification and regression problems [27,28,67,68]. It serially constructs a strong learner, with a weak learner that is continuously used to make up for the previous weak learner's shortcomings.…”

Section: (C) Adaptive Boostingmentioning

confidence: 99%

“…The final prediction result was then selected according to the vote results, which consumes a lot of time [26]. The AdaBoost model is weighted and iterated in the training process, and the weight is adjusted according to the error; thus, it also consumes more time [26,28,68]. The time difference of the AdaBoost model between the entire VNIR and chlorophyll-sensitive bands is the greatest between the three models.…”

Section: Analysis Of Time Efficiencymentioning

confidence: 99%

“…Machine learning (ML) is a branch of computer science that has been widely used for ecological and environmental remote sensing. Owing to their good computing performance and nonlinear mapping capabilities, ML models have increasingly been adopted as effective models for the inversion of water quality parameters in recent years, such as support vector machines (SVM), random forest (RF), genetic algorithms (GA), extreme learning machines (ELM), and artificial neural networks (ANN) [24,[26][27][28][29][30]. Numerous researchers have analyzed the relationship between the water quality parameters and spectral data, using ML algorithms based on the measured water quality and spectral data, indicating that the ML algorithms may be capable of handling the nonlinear relationships between reflectance and water quality parameters [24,28,31].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Total Phosphorus Concentration in Macrophytic Lakes Using Chlorophyll-Sensitive Bands: A Case Study of Lake Baiyangdian

Zhang

Cen

et al. 2022

Remote Sensing

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Total phosphorus (TP) is a significant indicator of water eutrophication. As a typical macrophytic lake, Lake Baiyangdian is of considerable importance to the North China Plain’s ecosystem. However, the lake’s eutrophication is severe, threatening the local ecological environment. The correlation between chlorophyll and TP provides a mechanism for TP prediction. In view of the absorption and reflection characteristics of the chlorophyll concentrations in inland water, we propose a method to predict TP concentration in a macrophytic lake with spectral characteristics dominated by chlorophyll. In this study, water spectra noise is removed by discrete wavelet transform (DWT), and chlorophyll-sensitive bands are selected by gray correlation analysis (GRA). To verify the effectiveness of the chlorophyll-sensitive bands for TP concentration prediction, three different machine learning (ML) algorithms were used to build prediction models, including partial least squares (PLS), random forest (RF) and adaptive boosting (AdaBoost). The results indicate that the PLS model performs well in terms of TP concentration prediction, with the least time consumption: the coefficient of determination (R2) and root mean square error (RMSE) are 0.821 and 0.028 mg/L in the training dataset, and 0.741 and 0.029 mg/L in the testing dataset, respectively. Compared with the empirical model, the method proposed herein considers the correlation between chlorophyll and TP concentration, as well as a higher accuracy. The results indicate that chlorophyll-sensitive bands are effective for predicting TP concentration.

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Section: (C) Adaptive Boostingmentioning

confidence: 99%

Section: Analysis Of Time Efficiencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Total Phosphorus Concentration in Macrophytic Lakes Using Chlorophyll-Sensitive Bands: A Case Study of Lake Baiyangdian

Zhang

Cen

et al. 2022

Remote Sensing

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“…The NPS pollutants' monitoring and identi cation are the basis of NPS pollution's control and management. Techniques of automatic monitoring, stable isotopic tracer, three-dimensional uorescence spectrum and molecular biology have been widely used in the determination and source identi cation of NPS pollutants (Zhu et al, 2017;Kruk et al, 2020;Rudra et al, 2020;Chen et al, 2021). The frequency of keywords related to monitoring and identi cation was 361 times, while 133 times occurred in China, accounting for 37% (Fig.…”

Section: The Types Of Nps Pollutants Are Diversi Edmentioning

confidence: 99%

Recent progress on the non-point source pollution research from the World and China’s perspective: A bibliometric analysis from 2012 to 2021

Xie

et al. 2022

Preprint

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Background With the effective control of point source pollution, the non-point source (NPS) pollution has been widely concerned as the primary reason for the improvement of global water environmental quality. At present, China is one of the countries with the highest research attention on NPS pollution. The Chinese government pays great attention to the environmental problems caused by the NPS pollution. Therefore, it is of great significance to clarify the global trends of NPS pollution and illustrate the importance of China on the NPS pollution research around the world. Results A bibliometric analysis was conducted based on 3407 publications retrieved from the Web of Science during 2012–2021. China, the USA and UK were the most productive countries. Articles published from China accounting from 23–50% between 2012–2021. Regarding the top 20 most productive authors, 17 of them from China. The most productive 21 institutions were located in China (15) and the UK (6). The Chinese Academy of Sciences stood out, followed by the Beijing Normal University. The Sci. Total Environ ranked first in the most productive journals, followed by Environ. Sci. Pollut. Res and Water. China was the major contributor to these journals, accounting from 24–79%. The most highly cited articles were published in Proc. Natl. Acad. Sci. U.S.A. The keywords analysis showed that nitrogen and phosphorus were the common NPS pollutants. Agricultural NPS pollution and urban NPS pollution were the main NPS types. The land use change, rainfall and irrigation were the main driving factors of NPS pollution. Load calculation and Integrated management were the key points of NPS pollution technology research. The NPS pollution research objects were mainly distributed in the surface water and groundwater, while the ocean might be the hot areas in the future research. Conclusions This study demonstrates the global trend of NPS pollution research during 2012–2021 according to analyzing the annual publication outputs, source journals, source country, author, institution and the high-frequency keywords. Meanwhile, the importance of China on NPS pollution research is explained from the bibliometrics perspective. It is beneficial for guiding the future research of NPS pollution.

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“…Unmanned aerial vehicles (UAVs) have huge advantages in monitoring water pollution in small areas because of the simplicity of their operation and their affordability, flexibility, and nonsusceptibility to interference by clouds; moreover, they can acquire near-real-time high-resolution imagery [19][20][21]. UAVs have been used to monitor chlorophyll-a (Chl-a), total suspended solids (TSS), total nitrogen (TN), total phosphorus (TP), permanganate index (COD Mn ), and metal ions in water bodies [22][23][24]. Commonly, a UAV might be equipped with visible, multispectral, and hyperspectral sensors when used for water quality monitoring [25,26].…”

Section: Introductionmentioning

confidence: 99%

UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms—A Case Study of the Zhanghe River, China

et al. 2022

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Timely monitoring of inland water quality using unmanned aerial vehicle (UAV) remote sensing is critical for water environmental conservation and management. In this study, two UAV flights were conducted (one in February and the other in December 2021) to acquire images of the Zhanghe River (China), and a total of 45 water samples were collected concurrently with the image acquisition. Machine learning (ML) methods comprising Multiple Linear Regression, the Least Absolute Shrinkage and Selection Operator, a Backpropagation Neural Network (BP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were applied to retrieve four water quality parameters: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphors (TP), and permanganate index (CODMn). Then, ML models based on the stacking approach were developed. Results show that stacked ML models could achieve higher accuracy than a single ML model; the optimal methods for Chl-a, TN, TP, and CODMn were RF-XGB, BP-RF, RF, and BP-RF, respectively. For the testing dataset, the R2 values of the best inversion models for Chl-a, TN, TP, and CODMn were 0.504, 0.839, 0.432, and 0.272, the root mean square errors were 1.770 μg L−1, 0.189 mg L−1, 0.053 mg L−1, and 0.767 mg L−1, and the mean absolute errors were 1.272 μg L−1, 0.632 mg L−1, 0.045 mg L−1, and 0.674 mg L−1, respectively. This study demonstrated the great potential of combined UAV remote sensing and stacked ML algorithms for water quality monitoring.

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Machine learning-based inversion of water quality parameters in typical reach of the urban river by UAV multispectral data

Cited by 71 publications

References 33 publications

Prediction of Total Phosphorus Concentration in Macrophytic Lakes Using Chlorophyll-Sensitive Bands: A Case Study of Lake Baiyangdian

Prediction of Total Phosphorus Concentration in Macrophytic Lakes Using Chlorophyll-Sensitive Bands: A Case Study of Lake Baiyangdian

Recent progress on the non-point source pollution research from the World and China’s perspective: A bibliometric analysis from 2012 to 2021

UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms—A Case Study of the Zhanghe River, China

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