Home Journals Articles Updates About
CN
image
收藏切换
Machine learning-based identification and contribution analysis of dominant factors driving algal blooms in the Xiangxi Bay
收藏切换
Jing YANG1, Zhong-yong YANG1, 2, *, Yu-fang HE3, Li-wei XIONG1, Yi-ling HU1, Dao-bin JI1, 2
China Environmental Science | 2026, 46(3) : 1486 - 1498
Less
收藏切换
China Environmental Science | 2026, 46(3): 1486-1498
• Environmental Ecology •
Machine learning-based identification and contribution analysis of dominant factors driving algal blooms in the Xiangxi Bay
Full
Jing YANG1, Zhong-yong YANG1, 2, *, Yu-fang HE3, Li-wei XIONG1, Yi-ling HU1, Dao-bin JI1, 2
Affiliations
  • 1.College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang 443002, China
  • 2.Hubei Provincial Field Observation and Research Station for the Three Gorges Reservoir Ecosystem, China Three Gorges University, Yichang 443002, China
  • 3.Hebei Water Conservancy Planning and Design Institute Co., Ltd., Shijiazhuang 050021, China
Published: 2026-03-20
Outline
收藏切换

Algal blooms are a major ecological and environmental issue in the tributary bays of the Three Gorges Reservoir. To identify the dominant environmental factors driving algal blooms in the Xiangxi Bay, this study utilized high-frequency monitoring data collected from August 9 to August 30, 2024, with chlorophyll-a (Chl.a) concentration as the bloom indicator. Three machine learning models—Random Forest (RF), Support Vector Regression (SVR), and Partial Least Squares Regression (PLS)—were applied to identify key driving factors. The results showed that algal blooms in Xiangxi Bay exhibited pronounced diurnal variation, with nighttime stable stratification and low disturbance favoring bloom formation, while daytime disturbances and high temperatures inhibited algal accumulation. Water velocity (WV) and air temperature (AT) were identified as immediate driving factors, with contribution rates of 27.1% and 18.1%, respectively. specific conductivity (SP) served as an important regulating factor (17.1%), while dissolved oxygen (DO), pH, and water temperature (WT) were secondary feedback factors. Moreover, the impacts of these factors on Algal blooms exhibited significant nonlinearity and lagged response characteristics.

algal blooms  /  Xiangxi River  /  machine learning models  /  environmental drivers  /  contribution analysis
杨京, 杨忠勇, 和玉芳, 熊励伟, 胡怡玲, 纪道斌. 基于机器学习的香溪河库湾水华暴发主控因子识别及贡献. 中国环境科学, 2026 , 46 (3) : 1486 -1498 .
Jing YANG, Zhong-yong YANG, Yu-fang HE, Li-wei XIONG, Yi-ling HU, Dao-bin JI. Machine learning-based identification and contribution analysis of dominant factors driving algal blooms in the Xiangxi Bay[J]. China Environmental Science, 2026 , 46 (3) : 1486 -1498 .
Appendix
Less
Year 2026 volume 46 Issue 3
71
14
Cite this Article
BibTeX
Article Info
  • Receive Date：2025-07-22
  • Online Date：2026-10-08
  • Published：2026-03-20
Article Data
Affiliations
History
  • Received：2025-07-22
Funding
Affiliations
    1.College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang 443002, China
    2.Hubei Provincial Field Observation and Research Station for the Three Gorges Reservoir Ecosystem, China Three Gorges University, Yichang 443002, China
    3.Hebei Water Conservancy Planning and Design Institute Co., Ltd., Shijiazhuang 050021, China
References
Share
https://castjournals.cast.org.cn/joweb/zghjkx/EN/
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
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
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House