Remote Sensing Technology and Application, Volume. 39, Issue 1, 149(2024)
The Hyperspectral Inversion Method of Main Ionic Compounds Content in Groundwater based on BP Neural Network
[1] ZHOU Zhifang, ZHENG Hu, ZHUANG Chao. Study on the unrecoverable depletion of groundwater resource. School of Earth Science and Engineering, 45, 1458-1463(2014).
[3] ZHOU Yangxiao, LI Wenpeng. Groundwater quality monitoring and assessment. China Institute for Geo-environmental Monitoring, 1-11(2008).
[4] SUN Bin. Application of multivariate statistics method into research on spatial distribution law of hydrochemistry in Dusituhe groundwater systerm in Ordos Cretaceous Basin[D](2007).
[5] PENG Ling, Mei Junjun, WANG Na et al. Quantitative inversion of water quality parameters in industrial and mining cities from hyperspectral remote sensing[J]. Spectroscopy and Spectral Analysis, 39, 2922-2928(2019).
[6] GUO Y, LIU C, YE R et al. Advances on water quality detection by UV-Vis spectroscopy[J]. Applied Sciences, 10, 6874(2020).
[7] HOSSAIN S, COOK D, CHOW CWK et al. Development of an optical method to monitor nitrification in drinking water[J]. Sensors, 21, 7525(2021).
[8] TAHRAOUI H, BELHADJ A, HAMITOUCHE A et al. Predicting the concentration of sulfate (SO42-) in drinking water using artificial neural networks: A case study: Médéa-Algeria. Desalination and Water Treatment, 217, 181-194(2021).
[9] YESILNACAR M I, SAHINKAYA E et al. Neural network prediction of nitrate in groundwater of Harran Plain,Turkey. Economic and Environmental Geology, 56, 19-25(2008).
[10] LEI Huiping, HU Bingliang, YU Tao et al. Research on the quantitative analysis method of nitrate in complex water by full scale spectrum with GS-SVR. Spectroscopy and Spectral Analysis, 41, 372-378(2021).
[11] CHEN Jie. Study on hyperspectral inversion model and online monitoring technology of nitrogen and phosphorus water quality parameters[D](2021).
[12] MAESSCHALCK R, DE D J, DESIRE L M. The mahalanobis distance. Chemometrics and Intelligent Laboratory Systems, 50, 1-18(2000).
[13] MASSAOUDI M S, REFAAT S, ABU-RUB H et al. PLS-CNN-BiLSTM: An end-to-end algorithm-based savitzky-golay smoothing and evolution strategy for load forecasting. Energies, 13, 5464(2020).
[14] CHEN C, JIANG Q, ZHANG Z et al. Hyperspectral inversion of petroleum hydrocarbon contents in soil based on continuum removal and wavelet packet decomposition. Sustainability, 12, 4218(2020).
[15] CHEN Yao, HUANG Changping, ZHANG Lifu et al. Spectral characteristics analysis and remote sensing retrieval of COD concentration. Institute of Remote Sensing and Digital Earth, 40, 824-830(2020).
[16] YANG Z, XIAO H, ZHANG L et al. Fast determination of oxides content in cement raw meal using NIR spectroscopy with SPXY algorithm. Analytical Methods, 10, 1039(2019).
[17] BEATTIE J R, ESMONDE-WHITE F W L. Exploration of principal component analysis: Deriving principal component analysis visually using spectra. Applied Spectroscopy, 75, 361-375(2021).
[18] ZHAO X, ZHAO X, HUANG M et al. An uncertainty sampling strategy based model updating method for soluble solid content and firmness prediction of apples from different years. Chemometrics and Intelligent Laboratory Systems, 217, 104426-(2021).
[19] HU F, ZHOU M, YAN P et al. Selection of characteristic wavelengths using SPA for laser induced fluorescence spectroscopy of mine water inrush. Spectrochimica Acta. Part A, 219, 367-374(2019).
[20] SHU Xiaohui, LIU Jianping. Some issues in dealing with multicollinearity using principal component regression. Statistics and Decision, 25-26(2004).
[21] HELLAND I S, SOLVE S, TRYGVE A et al. Model and estimators for partial least squares regression. Journal of Chemometrics, 13(2018).
[22] DENG Y, ZHOU X, SHEN J et al. New methods based on back propagation (BP) and radial basis function (RBF) artificial neural networks (ANNs) for predicting the occurrence of haloketones in tap water. Science of The Total Environment, 772, 145534(2021).
[23] LIU L. Research on water environment monitoring based on the internet of things combined with neural network. Optical Memory and Neural Networks, 30, 206-213(2021).
[24] LI H, LI G, HUANG Z et al. Application of BP neural network based on genetic algorithm optimization. Association for Computing Machinery, 40, 160-165(2019).
[25] BO J, LIU H, XING Q et al. Evaluating traditional empirical models and BPNN models in monitoring the concentrations of chlorophyll-a and total suspended particulate of eutrophic and turbid waters. Water, 13, 650(2021).
[26] WANG M, ZHANG J, ZHANG Z et al. Simultaneous ultraviolet spectrophotometric determination of sodium benzoate and potassium sorbate by BP-neural network algorithm and partial least squares. Optik-International Journal for Light and Electron Optics, 201, 163529(2019).
[27] WANG Yu, GUO Qiyi, LI Weigang. Predictive model based on improved bp neural networks and it's application. Computer Automated Measurement & Control, 39-42(2005).
[30] WENG Shifu[M]. Fourier transform infrared spectroscopy(2010).
[31] CHEN Jianhong, ZHU Lingjian, HUA Dengxin. Determination of sodium by near infrared spectroscopy. Spectroscopy and Spectral Analysis, 32, 949-952(2012).
[32] ZHANG Bin, CHEN Jianhong, JIAO Mingxing. Determination of chloride salt solution by NIR spectroscopy. Spectroscopy and Spectral Analysis, 35, 1840-1843(2015).
[33] BUIJS K, CHOPPIN G R. Near-infrared studies of the structure of water. I. Pure Water. Journal of Chemical Physics, 39, 2035-2041(1963).
Get Citation
Copy Citation Text
Qing GUO, Lifu ZHANG, Wenchao Qi, Linshan ZHANG. The Hyperspectral Inversion Method of Main Ionic Compounds Content in Groundwater based on BP Neural Network[J]. Remote Sensing Technology and Application, 2024, 39(1): 149
Category: Research Articles
Received: Jul. 15, 2022
Accepted: --
Published Online: Jul. 22, 2024
The Author Email: Qing GUO (1079695784@qq.com)