Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir

He, Yanhu; Gong, Zhenjie; Zheng, Yayuan; Yuanbo, Zhang

doi:10.3390/w13202844

Cited by 40 publications

(20 citation statements)

References 71 publications

(94 reference statements)

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“…As Liu et al used High-Resolution IKONOS Multispectral Imagery model by artificial neural network to estimated water quality of urban water bodies, the rRMSE are 10.4% for TN and 13.4% for TP [14]. Moreover, He et al report that BP neural network modeling produces 17.9% and 39.8% relative error for TN and TP respectively [15]. Generally, the accuracy of remote sensing estimation of constructed wetland water quality is lower than that of lake water bodies.…”

Section: Performance Of Modelsmentioning

confidence: 99%

Comparing the machine learning and empirical algorithms on remote sensing estimates water quality parameters of constructed wetland

Qian

Zhao²,

Wang³

et al. 2023

International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022)

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The water quality parameters (WQPs) at the constructed wetland of the lakeside zone are fluctuating. In order to accurately estimate the water purification efficiency of the lakeside belt, the remote sensing technology by satellite has a great advantage to complete such a task. In this study, back-propagation neural network and polynomial regression are compared in remote sensing estimate Total Nitrogen (TN), Total Phosphorus (TP), Nitrate Nitrogen (NN), and Ammonia Nitrogen (AN) concentrations in constructed wetland water quality. The result shows that the BP neural network algorithms outperformed the polynomial regression algorithms in the estimate AN and TN. However, the polynomial regression algorithms have achieved better performance in the estimate NN and TP. Moreover, the best algorithms produce about 60% of rRMSE in all WQPs in this study. As to mean normalized bias (MNB) result, the overall estimate by the BP neural network algorithms is lower than the measured data. In addition to TP, the empirical model is the opposite. This study could provide some reference for the remote sensing estimate of the water purification efficiency in constructed wetlands. Furthermore, BP neural network performance is more stable than the polynomial regression.

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Section: Performance Of Modelsmentioning

confidence: 99%

Comparing the machine learning and empirical algorithms on remote sensing estimates water quality parameters of constructed wetland

Qian

Zhao²,

Wang³

et al. 2023

International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022)

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“…Table 14 shows that pH, DO, Chl-a concentration, WT, TN, and COD Mn dominated PC1, which explained 35.57% of the total variance, and conductivity, algal density, and WT dominated PC2, which accounted for 14.91%, indicating the importance of pH, DO, Chl-a concentration, WT, TN, COD Mn , and conductivity in estimating water quality in the study area. At present, the retrieval of the COD Mn mainly uses conventional satellite remote sensing (such as GF series satellites, Landsat series satellites) [42,43], while the retrieval of DO and TN using hyperspectral remote sensing has higher accuracy [44][45][46]. Combined with the results of the above-related studies, it can be considered that the water quality of Shanmei Reservoir can be better evaluated by measuring pH, conductivity, and WT at the monitoring station, or by establishing the regression fitting equations between Chl-a, algae density, and turbidity and DO, COD Mn , and TN.…”

Section: Analysis Of Current Water Quality Status and Relationship Be...mentioning

confidence: 99%

Retrieving Inland Reservoir Water Quality Parameters Using Landsat 8-9 OLI and Sentinel-2 MSI Sensors with Empirical Multivariate Regression

Meng

Zhang

Zhang³

2022

IJERPH

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Improving water quality is one of the top priorities in the global agenda endorsed by the United Nations. To ensure the achievement of this goal, governments have developed plans to continuously monitor the status of inland waters. Remote sensing provides a low-cost, high-frequency, and practical complement to monitoring systems that can cover a large area. However, it is crucial to evaluate the suitability of sensors for retrieving water quality parameters (WQPs), owing to differences in spatial and spectral sampling from different satellites. Taking Shanmei Reservoir in Fuzhou City, Fujian Province as a case study, this study collected and sorted the water quality data measured at the site in 2020 to 2022 and Landsat 8-9 OLI and Sentinel-2 MSI images, simulated the chlorophyll-a (Chl-a) concentration, algae density, and turbidity using empirical multivariate regression, and explored the relationship between different WQPs using correlation analysis and principal component analysis (PCA). The results showed that the fitting effect of Landsat OLI data was better than that of the Sentinel-2 MSI data. The coefficient of determination (R2) values of Chl-a, algal density, and turbidity simulated by Landsat OLI data were 0.70, 0.81, and 0.80, respectively. Furthermore, the parameters of its validation equation were also smaller than those of Sentinel MSI data. The spatial distribution of three key WQPs retrieved from Landsat OLI data shows their values were generally low, with the mean values of the Chl-a concentration, algal density, and turbidity being 4.25 μg/L, 4.11 × 106 cells/L, and 1.86 NTU, respectively. However, from the end of February 2022, the values of the Chl-a concentration and algae density in the reservoir gradually increase, and the risk of water eutrophication also increases. Therefore, it is still necessary to pay continuous attention and formulate corresponding water quality management measures. The correlation analysis shows that the three key WQPs in this study have a high correlation with pH, water temperature (WT), and dissolved oxygen (DO). The results of PCA showed that pH, DO, Chl-a concentration, WT, TN, and CODMn were dominant in PC1, explaining 35.57% of the total variation, and conductivity, algal density, and WT were dominant in PC2, explaining 13.34% of the total variation. Therefore, the water quality of the Shanmei Reservoir can be better evaluated by measuring pH, conductivity, and WT at the monitoring station, or by establishing the regression fitting equations between DO, CODMn, and TN. The regression algorithm used in this study can identify the most important water quality features in the Shanmei Reservoir, which can be used to monitor the nutritional status of the reservoir and provide a reference for other similar inland water bodies.

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“…These networks are one of the most widely used neural network models. The development of the BP neural network is relatively mature and is easy to implement [26]. The method begins by computing the ANN output with the current weight values.…”

Section: Development Of a Back Propagation Annmentioning

confidence: 99%

Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network

2021

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Recently, biodigesters have attracted much attention as an efficient alternative for energy generation and organic waste treatment. The final performance of a biodigester depends heavily on the quality of its building process and the selection of its raw material: the geomembrane. The geomembrane is the coat that covers the biodigester used to control the migration of fluids. Therefore, the selection of the proper geomembrane, in terms of thickness, resistance, flexibility, etc., is fundamental. Unfortunately, there are no studies for the selection of geomembranes, and usually, it is an empirical process performed by workers based on their own experience. Such empirical selection might be inaccurate, limited, inconvenient, and even dangerous. In order to assist workers during the building process of a biodigester, this study proposes the use of an Artificial Neural Network (ANN) to classify a geomembrane as appropriate or not appropriate for the manufacture of a biodigester. The ANN is trained with a database built from qualitative and quantitative evaluations of different characteristics of geomembranes. The results indicate that the proposed ANN classifies the most suitable geomembranes with a 99.9% success rate. The proposed ANN becomes a reliable tool that contributes to the quality and safety of a biodigester.

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Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir

Cited by 40 publications

References 71 publications

Comparing the machine learning and empirical algorithms on remote sensing estimates water quality parameters of constructed wetland

Comparing the machine learning and empirical algorithms on remote sensing estimates water quality parameters of constructed wetland

Retrieving Inland Reservoir Water Quality Parameters Using Landsat 8-9 OLI and Sentinel-2 MSI Sensors with Empirical Multivariate Regression

Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network

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