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Remote Sensing Retrieval of Coastal Water Quality Parameters Based on ISSA-SVR Method
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Yuanjie LIU1, Jianyong CUI1, Wen DONG2, Jianhua WAN1, Jie ZHANG1
Journal of Telemetry, Tracking and Command | 2024, 45(3) : 81 - 90
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Journal of Telemetry, Tracking and Command | 2024, 45(3): 81-90
Radar and Countermeasures
Remote Sensing Retrieval of Coastal Water Quality Parameters Based on ISSA-SVR Method
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Yuanjie LIU1, Jianyong CUI1, Wen DONG2, Jianhua WAN1, Jie ZHANG1
Affiliations
  • 1.College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China
  • 2.Academy of Aerospace Information Innovation, Chinese Academy of Sciences, Beijing 100094, China
Published: 2024-05-15 doi: 10.12347/j.ycyk.20240120001
Outline
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The chemical oxygen demand (COD) and chlorophyll a concentration, which are typical water quality parameters re-lated to the spectrum, serve as important indicators for reflecting the degree of water pollution and eutrophication. Support Vector Regression (SVR) is suitable for small sample sizes and widely utilized in remote sensing retrieval of typical offshore water quality parameters; however, it faces challenges in model parameter selection and may easily fall into local optimal solutions. To address this issue, an Improved Sparrow Search Algorithm (ISSA) is developed by integrating reverse learning and simulated annealing. An enhanced support vector regression model (ISA-SVR) is proposed by refining the Sparrow algorithm to optimize the penalty coeffi-cient and kernel parameters of the SVR model. Inversion models for COD and Chl-a concentrations are established using measured water spectra and data on water quality parameters. The accuracy of the model is validated using Sentinel-2 satellite remote sensing spectral data, yielding inversion accuracies for each water quality parameter concentration. The mean relative error (MRE) of the COD concentration prediction model and Chl-a concentration prediction model based on ISSA algorithm optimized SVR are 20.02%and 30.17%, respectively, outperforming other models such as linear regression, SVR, and SSA-SVR models. Experimental results demonstrate that ISA-SVR algorithm represents an effective approach for remotely sensed retrieval of COD and Chl-a concentrations while offering valuable insights for subsequent scientific management of offshore water quality.

COD  /  Chl-a  /  SVR  /  Sparrow search algorithm
Yuanjie LIU, Jianyong CUI, Wen DONG, Jianhua WAN, Jie ZHANG. Remote Sensing Retrieval of Coastal Water Quality Parameters Based on ISSA-SVR Method[J]. Journal of Telemetry, Tracking and Command, 2024 , 45 (3) : 81 -90 . DOI: 10.12347/j.ycyk.20240120001
Year 2024 volume 45 Issue 3
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Article Info
doi: 10.12347/j.ycyk.20240120001
  • Receive Date:2024-01-20
  • Online Date:2026-03-18
  • Published:2024-05-15
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  • Received:2024-01-20
  • Revised:2024-02-24
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Affiliations
    1.College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China
    2.Academy of Aerospace Information Innovation, Chinese Academy of Sciences, Beijing 100094, China
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表12种不同金属材料的力学参数

Family
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Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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