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Water Quality Prediction of Small-Micro Water Body Based on the Intelligent-Algorithm-Optimized Support Vector Machine Regression Method and Unmanned Aerial Vehicles Multispectral Data

Author

Listed:
  • Ke Yao

    (School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China)

  • Yujie Chen

    (School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China)

  • Yucheng Li

    (School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China)

  • Xuesheng Zhang

    (School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China)

  • Beibei Zhu

    (School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China)

  • Zihao Gao

    (School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China)

  • Fei Lin

    (Hefei Intelligent Agriculture Collaborative Innovation Research Institute, Hefei 230031, China
    Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China)

  • Yimin Hu

    (Hefei Intelligent Agriculture Collaborative Innovation Research Institute, Hefei 230031, China
    Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China)

Abstract

Accurate prediction of spatial variation in water quality in small microwaters remains a challenging task due to the complexity and inherent limitations of the optical properties of small microwaters. In this paper, based on unmanned aerial vehicles (UAV) multispectral images and a small amount of measured water quality data, the performance of seven intelligent algorithm-optimized SVR models in predicting the concentration of chlorophyll (Chla), total phosphorus (TP), ammonia nitrogen (NH 3 -N), and turbidity (TUB) in small and micro water bodies were compared and analyzed. The results show that the Gray Wolf optimized SVR model (GWO-SVR) has the highest comprehensive performance, with R 2 of 0.915, 0.827, 0.838, and 0.800, respectively. In addition, even when dealing with limited training samples and different data in different periods, the GWO-SVR model also shows remarkable stability and portability. Finally, according to the forecast results, the influencing factors of water pollution were discussed. This method has practical significance in improving the intelligence level of small and micro water body monitoring.

Suggested Citation

  • Ke Yao & Yujie Chen & Yucheng Li & Xuesheng Zhang & Beibei Zhu & Zihao Gao & Fei Lin & Yimin Hu, 2024. "Water Quality Prediction of Small-Micro Water Body Based on the Intelligent-Algorithm-Optimized Support Vector Machine Regression Method and Unmanned Aerial Vehicles Multispectral Data," Sustainability, MDPI, vol. 16(2), pages 1-19, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:559-:d:1315792
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    References listed on IDEAS

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    1. Chengtian Ouyang & Donglin Zhu & Yaxian Qiu, 2021. "Lens Learning Sparrow Search Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-17, May.
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