A Method for Water Quality Remote Retrieva Based on Support Vector Regression with Parameters Optimized by Genetic Algorithm

He, Tong; Li, Jian Wei

doi:10.4028/www.scientific.net/amr.383-390.3593

Cited by 3 publications

(3 citation statements)

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“…Zhang et al [58] applied PSO and genetic algorithm (GA) to optimize the parameters of SVM, and they found that PSO showed a better learning ability and generalization in wood drying process modeling. A study presented by He et al [59] indicated that PSO-SVM model is more accurate than the routine linear regression model for the water quality retrievals of Weihe River in Shanxi province. Their results demonstrated the improvement of SVM models through the optimization of PSO.…”

Section: Discussionmentioning

confidence: 99%

Visible-Near Infrared Spectroscopy and Chemometric Methods for Wood Density Prediction and Origin/Species Identification

Via

Young

et al. 2019

Forests

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This study aimed to rapidly and accurately identify geographical origin, tree species, and model wood density using visible and near infrared (Vis-NIR) spectroscopy coupled with chemometric methods. A total of 280 samples with two origins (Jilin and Heilongjiang province, China), and three species, Dahurian larch (Larix gmelinii (Rupr.) Rupr.), Japanese elm (Ulmus davidiana Planch. var. japonica Nakai), and Chinese white poplar (Populus tomentosa carriere), were collected for classification and prediction analysis. The spectral data were de-noised using lifting wavelet transform (LWT) and linear and nonlinear models were built from the de-noised spectra using partial least squares (PLS) and particle swarm optimization (PSO)-support vector machine (SVM) methods, respectively. The response surface methodology (RSM) was applied to analyze the best combined parameters of PSO-SVM. The PSO-SVM model was employed for discrimination of origin and species. The identification accuracy for tree species using wavelet coefficients were better than models developed using raw spectra, and the accuracy of geographical origin and species was greater than 98% for the prediction dataset. The prediction accuracy of density using wavelet coefficients was better than that of constructed spectra. The PSO-SVM models optimized by RSM obtained the best results with coefficients of determination of the calibration set of 0.953, 0.974, 0.959, and 0.837 for Dahurian larch, Japanese elm, Chinese white poplar (Jilin), and Chinese white poplar (Heilongjiang), respectively. The results showed the feasibility of Vis-NIR spectroscopy coupled with chemometric methods for determining wood property and geographical origin with simple, rapid, and non-destructive advantages.

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Section: Discussionmentioning

confidence: 99%

Visible-Near Infrared Spectroscopy and Chemometric Methods for Wood Density Prediction and Origin/Species Identification

Via

Young

et al. 2019

Forests

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“…The current retrieval models of nitrogen and phosphorus concentrations are mainly constructed directly from remotesensing images or through media water-quality parameters such as chlorophyll a and suspended solids, and their applicability has been proved, but their models are only applicable to single water bodies (He and Li, 2011;Huang et al, 2021;Isenstein and Park, 2014;Qun'ou et al, 2021;Wang et al, 2018). It is clear that narrowing the application region of the model when constructing the retrieval model for the total phosphorus concentration in Chaohu Lake can significantly improve the retrieval accuracy (Gao et al, 2015).…”

Section: Effect Of Geographical Division On the Retrieval Accuracymentioning

confidence: 99%

Remote sensing monitoring of total nitrogen and total phosphorus concentrations in the water around Chaohu Lake based on geographical division

Wang²,

Wu³

et al. 2022

Front. Environ. Sci.

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Remote sensing is useful for quantifying water-quality parameters for managing inland water systems. However, the single water-quality retrieval model usually has poor applicability in large regions. To solve the issue of low retrieval accuracy of water-quality parameters in inland water, the study area herein is geographically divided into rural water and urban water according to the proportion of land-use types in the riparian zones. Furthermore, the machine-learning regression algorithms are used to construct the retrieval models suitable for the total nitrogen (TN) and total phosphorus (TP) concentrations based on the measured water-quality data and the simultaneous Sentinel-2 Multispectral Imager (MSI) images. Additionally, the optical retrieval models are applied to the MSI images acquired on different dates to analyze the variations of TN and TP concentrations in the water around Chaohu Lake of China. The results show that the three accuracy indices of determination coefficient (R2), mean square error (MSE), and mean absolute percentage error (MAPE) of the TN concentration retrieval models for rural water and urban water were 0.67, 0.37 mg/L, and 36.81%, and 0.78, 0.34 mg/L, and 8.34%, respectively, while those of the TP concentration retrieval model for rural water and urban water reached 0.46, 0.0034 mg/L, and 38.60%, and 0.58, 0.018 mg/L, and 37.57%, respectively. The accuracy of the TN and TP concentration retrieval model constructed using geographical division is significantly better than that which does not use geographical division. According to the retrieval results from MSI images, the TN and TP concentrations in urban water are higher than those in rural water. TN and TP concentrations in urban water are stable throughout the year and peak in December, while those of rural water are highest in March and lowest in November. The method proposed in this study can provide a new idea for improving the retrieval accuracy of water-quality parameters in different water bodies in a large-scale region, and the relevant conclusion can provide a theoretical basis for water pollution control and prevention strategies in agricultural basins.

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“…Water quality remote sensing inversion algorithm can be roughly divided into empirical algorithm and model-based algorithm. The empirical algorithm mostly adopts a ratio method, such as hyperbolic exponential algorithm [27], binary quadratic polynomial algorithm [28], SeaWiFS algorithm [29] as well as the semi-empirical Carder model [30]. However, with the development of the bio-optical model, the quantitative inversion algorithm based on model has become the mainstream of retrieving the correlation between the water body components and the spectral radiation characteristics of water body.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Inversion of Inland Aquaculture Water Quality Based on UAVs-WSN Spectral Analysis

et al. 2020

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The inland aquaculture environment is an artificial ecosystem, where the water quality is a key factor which is closely related to the economic benefits of inland aquaculture and the quality of aquatic products. Compared with marine aquaculture, inland aquaculture is normally smaller and susceptible to pollution, with poor self-purification capacity. Considering its low cost and large-scale monitoring ability, many researches have developed spectrum sensor on-board satellite platforms to allow remote monitoring of inland water surface. However, there remain many problems, such as low image resolution, poor flexible data acquisition, and anti-interference. Apart from that, the conventional forecasting model is of weak generalization ability and low accuracy. In our study, we combine unmanned aerial vehicles system (UAVs) with the wireless sensor network (WSN) to design a new ground water quality parameter and drone spectrum information acquisition approach, and to propose a novel dynamic network surgery-deep neural networks (DNS-DNNs) model based on multi-source feature fusion to forecast the distribution of dissolved oxygen (DO) and turbidity (TUB) in inland aquaculture areas. The result of using fused features, including characteristic spectrum, Gray-level co-occurrence matrix (GLCM) texture feature, and convolutional neural network (CNN) texture feature to build a model is that the characteristic spectrum+ CNN texture fusion features were the best input items for DNS-DNNs when forecasting DO, with the determination coefficient R 2 of the vertical set arriving at 0.8741, while the characteristic spectrum+ GLCM texture+ CNN texture fusion features were the best for TUB, with the R 2 reaching 0.8531. Compared with a variety of conventional models, our model had a better performance in the inversion of DO and TUB, and there was a strong correlation between predicted and real values: R 2 reached 0.8042 and 0.8346, whereas the root mean square error (RMSE) were only 0.1907 and 0.1794, separately. Our study provides a new insight about using remote sensing to rapidly monitor water quality in inland aquaculture regions.

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A Method for Water Quality Remote Retrieva Based on Support Vector Regression with Parameters Optimized by Genetic Algorithm

Cited by 3 publications

References 1 publication

Visible-Near Infrared Spectroscopy and Chemometric Methods for Wood Density Prediction and Origin/Species Identification

Visible-Near Infrared Spectroscopy and Chemometric Methods for Wood Density Prediction and Origin/Species Identification

Remote sensing monitoring of total nitrogen and total phosphorus concentrations in the water around Chaohu Lake based on geographical division

Dynamic Inversion of Inland Aquaculture Water Quality Based on UAVs-WSN Spectral Analysis

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