The optimal method for water quality parameters retrieval of urban river based on machine learning algorithms using remote sensing images

Jiang, Yizhu; Kong, Jinling; Zhong, Yanling; Zhang, Jingya; Zheng, Zijia; Wang, Lizheng; Liu, Dingming

doi:10.1080/01431161.2023.2209918

Cited by 3 publications

(2 citation statements)

References 45 publications

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“…FC1 features were primarily associated with the visible and NIR regions of the electromagnetic spectrum, as outlined in Table II. Similar findings have been reported in other studies [25,45,[65][66][67], indicating that Turb and SC are mainly influenced by the reflectance radiations within these wavelengths. On the other hand, utilizing all spectral bands (FC4) yielded the lowest accuracy in Turb and SC estimations.…”

Section: A Selection Of the Best Input Fcsupporting

confidence: 91%

Fuzzy Similarity Analysis of Effective Training Samples to Improve Machine Learning Estimations of Water Quality Parameters Using Sentinel-2 Remote Sensing Data

Dehkordi,

Zoej,

Mehran

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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 (ETS).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 (MAPE) and a 12% increase in R 2 for Turb, while for SC, the improvements were 5.47% in MAPE and 7% in R 2 . 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.

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Section: A Selection Of the Best Input Fcsupporting

confidence: 91%

Fuzzy Similarity Analysis of Effective Training Samples to Improve Machine Learning Estimations of Water Quality Parameters Using Sentinel-2 Remote Sensing Data

Dehkordi,

Zoej,

Mehran

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…Finally, the average of n iterations' computation results is taken as the final weight of each feature. The Relief F-RFE weight calculation formula for the four types of parameters is shown as follows [40]:…”

Section: Methodology 231 Potential Feature Dataset Constructionmentioning

confidence: 99%

A Feature Selection Method Based on Relief Feature Ranking with Recursive Feature Elimination for the Inversion of Urban River Water Quality Parameters Using Multispectral Imagery from an Unmanned Aerial Vehicle

Zheng,

Jiang,

Zhang

et al. 2024

Water

Self Cite

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The timely monitoring of urban water bodies using unmanned aerial vehicle (UAV)-mounted remote sensing technology is crucial for urban water resource protection and management. Addressing the limitations of the use of satellite data in inferring the water quality parameters of small-scale water bodies due to their spatial resolution constraints and limited input features, this study focuses on the Zao River in Xi’an City. Leveraging UAV multispectral imagery, a feature selection method based on Relief Feature Ranking with Recursive Feature Elimination (Relief F-RFE) is proposed to determine the quality parameters of the typical urban pollution in water (dissolved oxygen (DO), total nitrogen (TN), turbidity, and chemical oxygen demand (COD). By constructing a potential feature set and utilizing optimal feature combinations, inversion models are developed for the four water quality parameters using three machine learning (ML) algorithms (Random Forest (RF), Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM). The inversion accuracies of the different models are compared, and the spatial distribution of the four water quality parameters is analyzed. The results show that the models constructed based on UAV-based multispectral remote sensing imagery perform well in inferring the water quality parameters of the Zao River. The SVR algorithm, based on Relief F-RFE feature selection, achieves a higher accuracy, with RMSE values of 7.19 mg/L, 1.14 mg/L, 3.15 NTU, and 4.28 mg/L, respectively. The methods and conclusions of this study serve as a reference for research on the inversion of water quality parameters in urban rivers.

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Machine Learning Based Inversion of Water Quality Parameters in Typical Reach of Rural Wetland by Unmanned Aerial Vehicle Images

Zeng,

Ma,

Zheng

et al. 2024

Water

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Rural wetlands are complex landscapes where rivers, croplands, and villages coexist, making water quality monitoring crucial for the well-being of nearby residents. UAV-based imagery has proven effective in capturing detailed features of water bodies, making it a popular tool for water quality assessments. However, few studies have specifically focused on drone-based water quality monitoring in rural wetlands and their seasonal variations. In this study, Xiangfudang Rural Wetland Park, Jiaxin City, Zhejiang Province, China, was taken as the study area to evaluate water quality parameters, including total nitrogen (TN), total phosphors (TP), chemical oxygen demand (COD), and turbidity degree (TUB). We assessed these parameters across summer and winter seasons using UAV multispectral imagery and field sample data. Four machine learning algorithms were evaluated and compared for the inversion of the water quality parameters, based on the situ sample survey data and UAV multispectral images. The results show that ANN algorithm yielded the best results for estimating TN, COD, and TUB, with validation R2 of 0.78, 0.76, and 0.57, respectively; CatBoost performed best in TP estimation, with validation R2 and RMSE values of 0.72 and 0.05 mg/L. Based on spatial estimation results, the average COD concentration in the water body was 16.05 ± 9.87 mg/L in summer, higher than it was in winter (13.02 ± 8.22 mg/L). Additionally, mean TUB values were 18.39 Nephelometric Turbidity Units (NTU) in summer and 20.03 NTU in winter. This study demonstrates the novelty and effectiveness of using UAV multispectral imagery for water quality monitoring in rural wetlands, providing critical insights into seasonal water quality variations in these areas.

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The optimal method for water quality parameters retrieval of urban river based on machine learning algorithms using remote sensing images

Cited by 3 publications

References 45 publications

Fuzzy Similarity Analysis of Effective Training Samples to Improve Machine Learning Estimations of Water Quality Parameters Using Sentinel-2 Remote Sensing Data

Fuzzy Similarity Analysis of Effective Training Samples to Improve Machine Learning Estimations of Water Quality Parameters Using Sentinel-2 Remote Sensing Data

A Feature Selection Method Based on Relief Feature Ranking with Recursive Feature Elimination for the Inversion of Urban River Water Quality Parameters Using Multispectral Imagery from an Unmanned Aerial Vehicle

Machine Learning Based Inversion of Water Quality Parameters in Typical Reach of Rural Wetland by Unmanned Aerial Vehicle Images

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