Water Quality Monitoring Using Remote Sensing and an Artificial Neural Network

Chebud, Yirgalem; Naja, Ghinwa; Rivero, Rosanna G.; Melesse, Assefa M.

doi:10.1007/s11270-012-1243-0

Cited by 116 publications

(80 citation statements)

References 39 publications

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“…Basically, the turbidity simulation model establishment was based on the quantitative relation between telemetry image and turbidity. Those estimation methods can be divided into linear relation, multiple linear regression (MLR) relation, logarithmic relation, exponential relation, Gordan relation, Water 2019, 11 The in-situ turbidity measure frequency in Tsing-Wen and Nan-Hwa reservoirs are approximately once per season; however, the observation date and depth of each station are not identical. Therefore, in this study, turbidity data were selected with a water depth of~1 m to establish the turbidity simulation model.…”

Section: Introductionmentioning

confidence: 99%

Modelling Reservoir Turbidity Using Landsat 8 Satellite Imagery by Gene Expression Programming

Liu

Wang

2019

Water

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This study aimed to develop a reliable turbidity model to assess reservoir turbidity based on Landsat-8 satellite imagery. Models were established by multiple linear regression (MLR) and gene-expression programming (GEP) algorithms. Totally 55 and 18 measured turbidity data from Tseng-Wen and Nan-Hwa reservoir paired and screened with satellite imagery. Finally, MLR and GEP were applied to simulated 13 turbid water data for critical turbidity assessment. The coefficient of determination (R 2 ), root mean squared error (RMSE), and relative RMSE (R-RMSE) calculated for model performance evaluation. The result show that, in model development, MLR and GEP shows a similar consequent. However, in model testing, the R 2 , RMSE, and R-RMSE of MLR and GEP are 0.7277 and 0.8278, 0.7248 NTU and 0.5815 NTU, 22.26% and 17.86%, respectively. Accuracy assessment result shows that GEP is more reasonable than MLR, even in critical turbidity situation, GEP is more convincible. In the model performance evaluation, MLR and GEP are normal and good level, in critical turbidity condition, GEP even belongs to outstanding level. These results exhibit GEP denotes rationality and with relatively good applicability for turbidity simulation. From this study, one can conclude that GEP is suitable for turbidity modeling and is accurate enough for reservoir turbidity estimation. artificial neural networks, supper vector machine (SVM), random forest (RF), partial least squares (PLS), and principal components regression (PCR) . However, the empirical relation and observation method used to test different data with various temporal and spatial backgrounds or different measuring instruments, the results of reservoir turbidity estimation are considerably different from those of the original research area [35]. The accuracy of the simulated turbidity by the empirical equation is also influenced by the algorithms. Chang et al., using the high-resolution spectral image obtained from Formosat-2 satellite imagery have a resolution of 8 m and the SSC data of 53 mud samples, the empirical relation that is established using the stepwise regression method exhibit the R 2 of 0.84 and the relative-RMSE (R-RMSE) of 31%, however, in the case of data validation and testing, R-RMSE improved to 43%, indicating that the empirical relation between the remote sensing imagery and the water quality observations exhibited a considerable degree of error. Therefore, the rationality and applicability of the integrated assessment for relevant research related to the water quality estimation of remote sensing imagery can be improved if the simulated turbidity accuracy is improved and the difference is reduced [36]. Based on the large-area and high-repetition remote sensing imagery and the measured turbidity, this study would like to provide a simulation algorithm for the one who cannot obtain an ideal result in typical algorithms for reservoir turbidity estimation. Materials and MethodsThis study refers to Quang, N. H. et al., adopted the high-resolution multi-spectrum satell...

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Section: Introductionmentioning

confidence: 99%

Modelling Reservoir Turbidity Using Landsat 8 Satellite Imagery by Gene Expression Programming

Liu

Wang

2019

Water

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“…Neural networks have also been used to predict other water quality parameters, including nutrient loading, direct runoff volumes and overall water quality (Khuan et al, 2002;Chebud et al, 2012;Kim et al, 2012). Recent water quality prediction work with ANN's has been extended to include the prediction of groundwater contamination due to highway construction, as well as land use type based on location-specific runoff water quality observations from areas such as highways (Ha and Stenstrom, 2003;El Tabach et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Highway runoff quality models for the protection of environmentally sensitive areas

Trenouth

Gharabaghi

2016

Journal of Hydrology

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“…Many scientists (Table 1) have made continuous efforts to answer the first question, even though correlating remote sensing spectral features directly to nitrogen and phosphorus concentrations of water bodies in theory is difficult. Several studies have used indirect retrieval methods to estimate TN and TP concentrations based on the study that they are closely correlated with other water quality parameters, such as TSS, chlorophyll-a (Chl-a), or CDOM [13,18]. As an inference, this study assumes that TP and TN concentration has an indirect correlation with the optical properties of water, which can be retrieved by satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

“…Pan et al [16] established an inversion model of TN by multivariable regression Kriging to analyze the close association of TN and Chl-a or TSS by HJ-1A/HSI (hyperspectral image) satellite imagery. Some other studies have achieved good retrieval accuracy by directly exploring the relationship between TP and TN concentrations, and satellite image data by establishing empirical models [14,18,19]. According to the literature (Table 1), the methods could generally be divided into two categories: traditional linear regression model and intelligent algorithms.…”

Section: Introductionmentioning

confidence: 99%

Empirical Estimation of Total Nitrogen and Total Phosphorus Concentration of Urban Water Bodies in China Using High Resolution IKONOS Multispectral Imagery

Liu

Zhang

Yuan

et al. 2015

Water

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Abstract:Measuring total nitrogen (TN) and total phosphorus (TP) is important in managing heavy polluted urban waters in China. This study uses high spatial resolution IKONOS imagery with four multispectral bands, which roughly correspond to Landsat/TM bands 1-4, to determine TN and TP in small urban rivers and lakes in China. By using Lake Cihu and the lower reaches of Wen-Rui Tang (WRT) River as examples, this paper develops both multiple linear regressions (MLR) and artificial neural network (ANN) models to estimate TN and TP concentrations from high spatial resolution remote sensing imagery and in situ water samples collected concurrently with overpassing satellite. The measured and estimated values of both MLR and ANN models are in good agreement (R 2 > 0.85 and RMSE < 2.50). The empirical equations selected by MLR are more straightforward, whereas the estimated accuracy using ANN model is better (R 2 > 0.86 and RMSE < 0.89). Results validate the potential of using high resolution IKONOS multispectral imagery to study the chemical states of small-sized urban water bodies. The spatial distribution maps of TN and TP concentrations generated by the ANN model can inform the decision makers of variations in water quality in Lake Cihu and lower reaches of WRT River. The approaches and equations developed in this study could be applied to other urban water bodies for water quality monitoring. OPEN ACCESSWater 2015, 7 6552

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Water Quality Monitoring Using Remote Sensing and an Artificial Neural Network

Cited by 116 publications

References 39 publications

Modelling Reservoir Turbidity Using Landsat 8 Satellite Imagery by Gene Expression Programming

Modelling Reservoir Turbidity Using Landsat 8 Satellite Imagery by Gene Expression Programming

Highway runoff quality models for the protection of environmentally sensitive areas

Empirical Estimation of Total Nitrogen and Total Phosphorus Concentration of Urban Water Bodies in China Using High Resolution IKONOS Multispectral Imagery

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