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Estimation of the Fe and Cu Contents of the Surface Water in the Ebinur Lake Basin Based on LIBS and a Machine Learning Algorithm

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  • Xianlong Zhang

    (Key Laboratory of Smart City and Environmental of Higher Education Institute, College of Resources and Environment Sciences, Xinjiang University, Urumqi 830046, China
    Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China
    Engineering Research Center of Central Asia Geoinformation Development and Utilization, National Administration of Surveying, Mapping and Geoinformation, Urumqi 830002, China)

  • Fei Zhang

    (Key Laboratory of Smart City and Environmental of Higher Education Institute, College of Resources and Environment Sciences, Xinjiang University, Urumqi 830046, China
    Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China
    Engineering Research Center of Central Asia Geoinformation Development and Utilization, National Administration of Surveying, Mapping and Geoinformation, Urumqi 830002, China)

  • Hsiang-te Kung

    (Department of Earth Sciences, The University of Memphis, Memphis, TN 38152, USA)

  • Ping Shi

    (School of Foreign Language, Jining Medical University, Jining 272067, China)

  • Ayinuer Yushanjiang

    (Key Laboratory of Smart City and Environmental of Higher Education Institute, College of Resources and Environment Sciences, Xinjiang University, Urumqi 830046, China
    Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China
    Engineering Research Center of Central Asia Geoinformation Development and Utilization, National Administration of Surveying, Mapping and Geoinformation, Urumqi 830002, China)

  • Shidan Zhu

    (Key Laboratory of Smart City and Environmental of Higher Education Institute, College of Resources and Environment Sciences, Xinjiang University, Urumqi 830046, China
    Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China
    Engineering Research Center of Central Asia Geoinformation Development and Utilization, National Administration of Surveying, Mapping and Geoinformation, Urumqi 830002, China)

Abstract

Traditional technology for detecting heavy metals in water is time consuming and difficult and thus is not suitable for quantitative detection of large samples. Laser-induced breakdown spectroscopy (LIBS) can identify multi-state (such as solid, liquid, and gas) substances simultaneously, rapidly and remotely. In this study, water samples were collected from the Ebinur Lake Basin. The water samples were subjected to LIBS to extract the characteristic peaks of iron (Fe) and copper (Cu). Most of the quantitative analysis of LIBS rarely models and estimates the heavy metal contents in natural environments and cannot quickly determine the heavy metals in field water samples. This study creatively uses the Fe and Cu contents in water samples and the characteristics of their spectral curves in LIBS for regression modelling analysis and estimates their contents in an unknown water body by using LIBS technology and a machine learning algorithm, thus improving the detection rate. The results are as follows: (1) The Cu content of the Ebinur Lake Basin is generally higher than the Fe content, the highest Fe and Cu contents found within the basin are in the Ebinur Lake watershed, and the lowest are in the Jing River. (2) A number of peaks from each sample were found of the LIBS curve. The characteristic analysis lines of Fe and Cu were finally determined according to the intensities of the Fe and Cu characteristic lines, transition probabilities and high signal-to-background ratio (S/B). Their wavelengths were 396.3 and 324.7 nm, respectively. (3) The relative percent deviation (RPD) of the Fe content back-propagation (BP) network estimation model is 0.23, and the prediction ability is poor, so it is impossible to accurately predict the Fe content of samples. In the estimation model of BP network of Cu, the coefficient of determination (R 2 ) is 0.8, the root mean squared error (RMSE) is 0.1, and the RPD is 1.79. This result indicates that the BP estimation model of Cu content has good accuracy and strong predictive ability and can accurately predict the Cu content in a sample. In summary, estimation based on LIBS improved the accuracy and efficiency of Fe and Cu content detection in water and provided new ideas and methods for the accurate estimation of Fe and Cu contents in water.

Suggested Citation

  • Xianlong Zhang & Fei Zhang & Hsiang-te Kung & Ping Shi & Ayinuer Yushanjiang & Shidan Zhu, 2018. "Estimation of the Fe and Cu Contents of the Surface Water in the Ebinur Lake Basin Based on LIBS and a Machine Learning Algorithm," IJERPH, MDPI, vol. 15(11), pages 1-20, October.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:11:p:2390-:d:178871
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    References listed on IDEAS

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    1. Ruiliang Jia & Jinlong Zhou & Yinzhu Zhou & Qiao Li & Yexin Gao, 2014. "A Vulnerability Evaluation of the Phreatic Water in the Plain Area of the Junggar Basin, Xinjiang Based on the VDEAL Model," Sustainability, MDPI, vol. 6(12), pages 1-14, November.
    2. Dernoncourt, David & Hanczar, Blaise & Zucker, Jean-Daniel, 2014. "Analysis of feature selection stability on high dimension and small sample data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 681-693.
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    Cited by:

    1. Jinfeng Guo & Dong Cui & Jinxing Guo & Umut Hasan & Fengqi Lv & Zixing Li, 2025. "Hyperspectral Estimation of Tea Leaf Chlorophyll Content Based on Stacking Models," Agriculture, MDPI, vol. 15(10), pages 1-23, May.

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