气候变化领域集成服务门户(CCPortal): Analysing the spatiotemporal variation and influencing factors of Lake Chaohu's CDOM over the past 40 years using machine learning

CCPortal

DOI	10.1002/eco.2639
	Analysing the spatiotemporal variation and influencing factors of Lake Chaohu's CDOM over the past 40 years using machine learning
	Zhang, Zijie; Zhang, Han; Jin, Yifan; Guo, Hongwei; Tian, Shang; Huang, Jinhui Jeanne; Zhu, Xiaotong
发表日期	2024
ISSN	1936-0584
EISSN	1936-0592
起始页码	17
结束页码	3
卷号	17 期号:3
英文摘要	Chromophoric dissolved organic matter (CDOM) in aquatic environments is an important component of the biogeochemical cycle and carbon cycle. The aim of this study is to investigate the long-term changes in CDOM in shallow and eutrophic Chaohu Lake, as well as its relationship with climate, environment and social factors. Using long time series Landsat image data and machine learning technology, the spatiotemporal evolution of Chaohu CDOM since 1987 was reconstructed. A total of 180 samples were collected, which were divided into three parts based on regional and hydrological characteristics. The results show that the water quality in different regions were significantly different, and TN may be the key factor driving the change of CDOM in Chaohu Lake. Machine learning algorithms including random forest (RF), support vector regression (SVR), neural network (NN), multimodal deep learning (MDL) model and Extreme Gradient Boosting (XGBoost) were used, among which XGBoost model performed best (R-2 = 0.955, mean absolute error [MAE] = 0.024 mg/L, root mean square error [RMSE] = 0.036 mg/L, bias = 1.005) and was used for CDOM spatiotemporal variation retrieval. The change of CDOM was seasonal, highest in August (0.67 m(-1)) and lowest in December (0.48 m(-1)), and the western lake is the main source of CDOM. Annual variability of the CDOM indicates that it began to decline after the completion of water pollution control in 2000. Temperature changes were closely related to CDOM (P < 0.01) and agricultural non-point source pollution plays an important role in Chaohu Lake. This study will provide feasible methods and scientific basis for the long-term remote sensing supervision of CDOM.
英文关键词	chromophoric dissolved organic matter; climate change; environmental management; Lake Chaohu; machine learning; remote sensing
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
WOS类目	Ecology ; Environmental Sciences ; Water Resources
WOS记录号	WOS:001178358400001
来源期刊	ECOHYDROLOGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/299120
作者单位	Nankai University; Nankai University
推荐引用方式 GB/T 7714	Zhang, Zijie,Zhang, Han,Jin, Yifan,et al. Analysing the spatiotemporal variation and influencing factors of Lake Chaohu's CDOM over the past 40 years using machine learning[J],2024,17(3).
APA	Zhang, Zijie.,Zhang, Han.,Jin, Yifan.,Guo, Hongwei.,Tian, Shang.,...&Zhu, Xiaotong.(2024).Analysing the spatiotemporal variation and influencing factors of Lake Chaohu's CDOM over the past 40 years using machine learning.ECOHYDROLOGY,17(3).
MLA	Zhang, Zijie,et al."Analysing the spatiotemporal variation and influencing factors of Lake Chaohu's CDOM over the past 40 years using machine learning".ECOHYDROLOGY 17.3(2024).