Evaluation of surface water quality of Ukkadam lake in Coimbatore using UAV and Sentinel-2 multispectral data

Rahul, T. S.; Brema, J.; Wessley, G. Jims John

doi:10.1007/s13762-022-04029-7

Cited by 8 publications

(7 citation statements)

References 41 publications

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“…However, there is also a notable correlation between spectral bands and SC. This > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < is because the concentration of nOWQPs, such as SC, is linked to OWQPs (Figure 3c), such as Turb, which can influence the color of water and can be detected by satellite sensors [15,22,48].…”

Section: Performance Evaluation Of the Proposed Methodologymentioning

confidence: 99%

“…S2 data consists of 13 spectral bands, three of which have been designed for atmospheric purposes with a spatial resolution of 60 meters and were not used in this study (B1, B9, and B10 in Table II). The remaining 10 bands were II, which were all resampled to a uniform resolution of 10 meters using the nearest neighbor method [15,48]. The MSI offers a short revisit period of 5 days, allowing for frequent monitoring of water dynamics.…”

Section: Satellite Datamentioning

confidence: 99%

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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: Performance Evaluation Of the Proposed Methodologymentioning

confidence: 99%

Section: Satellite Datamentioning

confidence: 99%

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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“…Furthermore, despite the instability of the inversion results from the DTR model, it had the potential to provide superior accuracy exploration over other methods. Lastly, the revelation of sensitive spectral bands for different WQPs will contribute to combining drone hyperspectral data and satellite images for WQP monitoring [54,55]. UAV hyperspectral images will facilitate the rapid acquisition of water quality in urban rivers, supporting pollution source tracing and health protection [6,22].…”

Section: Implications and Limitationsmentioning

confidence: 99%

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

Liu,

2024

Remote Sensing

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Efficient monitoring of water quality parameters (WQPs) is crucial for environmental health. Drone hyperspectral images have offered the potential for the flexible and accurate retrieval of WQPs. However, a machine learning (ML)-based multi-process strategy for WQP inversion has yet to be established. Taking a typical urban river in Guangzhou city, China, as the study area, this paper proposes a machine learning-based strategy combining spectral preprocessing and ML regression models with ground truth WQP data. Fractional order derivation (FOD) and discrete wavelet transform (DWT) methods were used to explore potential spectral information. Then, multiple methods were applied to select sensitive features. Three modeling strategies were constructed for retrieving four WQPs, including the Secchi depth (SD), turbidity (TUB), total phosphorus (TP), and permanganate index (CODMn). The highest R2s were 0.68, 0.90, 0.70, and 0.96, respectively, with corresponding RMSEs of 13.73 cm, 6.50 NTU, 0.06 mg/L, and 0.20 mg/L. Decision tree regression (DTR) was found to have the potential with the best performance for the first three WQPs, and eXtreme Gradient Boosting Regression (XGBR) for the CODMn. Moreover, tailored feature selection methods emphasize the importance of fitting processing strategies for specific parameters. This study provides an effective framework for WQP inversion that combines spectra mining and extraction based on drone hyperspectral images, supporting water quality monitoring and management in urban rivers.

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“…Taking the estimation of total phosphorus (TP) as an example, Wu et al [17] constructed a linear regression equation for natural logarithmic TP concentration, and Gao et al [18] constructed a combination of linear regression models adapted to the different regions. This kind of method is easy to establish and has achieved high accuracy in several studies [19][20][21][22][23][24]. It may be because most of these studies focused on small areas and using only a small amount of data for model building, so the water type was relatively homogeneous.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Non-Optically Active Water Quality Parameters in Zhejiang Province Based on Machine Learning

Gao,

Shangguan,

Sun

et al. 2024

Remote Sensing

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Water parameter estimation based on remote sensing is one of the common water quality evaluation methods. However, it is difficult to describe the relationship between the reflectance and the concentration of non-optically active substances due to their weak optical characteristics, and machine learning has become a viable solution for this problem. Therefore, based on machine learning methods, this study estimated four non-optically active water quality parameters including the permanganate index (CODMn), dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Specifically, four machine learning models including Support Vector Machine Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) were constructed for each parameter and their performances were assessed. The results showed that the optimal models of CODMn, DO, TN, and TP were RF (R2 = 0.52), SVR (R2 = 0.36), XGBoost (R2 = 0.45), and RF (R2 = 0.39), respectively. The seasonal 10 m water quality over the Zhejiang Province was measured using these optimal models based on Sentinel-2 images, and the spatiotemporal distribution was analyzed. The results indicated that the annual mean values of CODMn, DO, TN, and TP in 2022 were 2.3 mg/L, 6.6 mg/L, 1.85 mg/L, and 0.063 mg/L, respectively, and the water quality in the western Zhejiang region was better than that in the northeastern Zhejiang region. The seasonal variations in water quality and possible causes were further discussed with some regions as examples. It was found that DO would decrease and CODMn would increase in summer due to the higher temperature and other factors. The results of this study helped understand the water quality in Zhejiang Province and can also be applied to the integrated management of the water environment. The models constructed in this study can also provide references for related research.

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Evaluation of surface water quality of Ukkadam lake in Coimbatore using UAV and Sentinel-2 multispectral data

Cited by 8 publications

References 41 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 Machine-Learning-Based Framework for Retrieving Water Quality Parameters in Urban Rivers Using UAV Hyperspectral Images

Estimation of Non-Optically Active Water Quality Parameters in Zhejiang Province Based on Machine Learning

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