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Study on Inversion of Suspended Matter in Wuliangsu Lake Based on M-GA-BP
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Chen-hao WU, Xue-liang FU, Hong-hui LI, Hua HU
Water Resources and Power | 2023, 41(12) : 49 - 52
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Water Resources and Power | 2023, 41(12): 49-52
• HYDROLOGY, WATER RESOURCES AND ENVIRONMENT •
Study on Inversion of Suspended Matter in Wuliangsu Lake Based on M-GA-BP
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Chen-hao WU, Xue-liang FU, Hong-hui LI, Hua HU
Affiliations
  • College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Published: 2023-12-25 doi: 10.20040/j.cnki.1000-7709.2023.20230103
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In order to understand the water quality of Wuliangsu Lake, an inversion method of total suspended matter concentration based on M-GA-BP was proposed. Using Sentinel-2 remote sensing satellite images as the data source and considering the spatial and temporal characteristics existing in the study area, the monthly data was considered as a feature for the inversion of TSM concentration. The GA-BP model was built by optimizing the weights and thresholds of the BP neural network using genetic algorithm (GA), and comparing with the traditional BP neural network model. The results show that the introduction of the monthly feature model effectively reduces the model complexity and improves the model inversion accuracy, among which the M-GA-BP model has the highest inversion accuracy with the coefficients of determination of 0.916 and 0.903 for the training and test sets, respectively, and the root mean square errors of 0.049 μg/L and 0.057 μg/L for the training and test sets, respectively. The study can provide a new idea for the inversion of TSM concentration in the Wuliangsu Lake.

month feature  /  genetic algorithm  /  BP neural network  /  suspended solid  /  Sentinel-2  /  Wuliangsu Lake
Chen-hao WU, Xue-liang FU, Hong-hui LI, Hua HU. Study on Inversion of Suspended Matter in Wuliangsu Lake Based on M-GA-BP[J]. Water Resources and Power, 2023 , 41 (12) : 49 -52 . DOI: 10.20040/j.cnki.1000-7709.2023.20230103
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Year 2023 volume 41 Issue 12
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20230103
  • Receive Date：2023-01-30
  • Online Date：2026-01-28
  • Published：2023-12-25
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  • Received：2023-01-30
  • Revised：2023-04-17
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    College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20230103
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
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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