Estimation of water quality parameters based on time series hydrometeorological data in Miaowan Island

doi:10.1016/j.ecolind.2024.111693

IOCAS-IR

	Estimation of water quality parameters based on time series hydrometeorological data in Miaowan Island
	Zheng, Yuanning 1,2; Li, Cai 1; Zhang, Xianqing 1,2; Zhao, Wei 3; Yang, Zeming 1; Cao, Wenxi 1
	2024-02-01
发表期刊	ECOLOGICAL INDICATORS
ISSN	1470-160X
卷号	159 页码:11
通讯作者	Li, Cai(liclaire@scsio.ac.cn)
摘要	Water quality parameters (WQPs), such as dissolved oxygen (DO), chemical oxygen demand (COD) and chlorophyll (Chl), are important indicators of ecosystem system. The easy availability of hydro-meteorological parameters (HMPs) provides an important tool for estimating WQPs. In this study, using three empirical machine learning (ML) algorithms, namely Multi-Layer Perceptron (MLP), Random Forest (RF), and M5 Model Tree (M5T), and based on a large amount of time series in situ monitoring of HMPs and WQPs data over a six-month period in Miaowan Island, a new ML model was developed to estimate DO, COD, and Chl in a simple and costeffective manner. Through feature selection, the input HMPs for ML the ML models include temperature, salinity, depth, air pressure and relative humidity. The results of the accuracy evaluation showed that the RF-based model was the optimal model for estimating DO, COD, and Chl with R2 values of 0.987, 0.992, and 0.965 on the testing set, respectively. With the RF-based model, the WQPs at two sites of Miaowan Island were estimated over a temporal sequence, and the estimated results are highly consistent with the measurements obtained from IEEIoTS. Furthermore, we extended the application of the RF-based model to estimate DO in Zhanjiang Bay throughout August 2023. This extension was based on in situ monitoring of HMPs obtained from WQMS, and comparison with the measured DO. They have corresponding temporal trends but with variations in values, potentially attributable to the inherent normality of the model. The results suggest that the RF-based model based on HMPs information provides a practical approach for estimating WQPs.
关键词	Water quality parameter Hydrometeorological Island Machine learning
DOI	10.1016/j.ecolind.2024.111693
收录类别	SCI
语种	英语
资助项目	Science and Technology Planning Project of Guangzhou Nansha District Guangzhou City China[2022ZD001] ; National Key Research and Development Program of China[2016YFC1400603] ; National Key Research and Development Program of China[2017YFC0506305] ; Ministry of Science and Technology of the People's Republic of China (MOST) ; Guangdong Basic and Applied Basic Research Foundation[2021A1515110639] ; Hainan Provincial Natural Science Foundation of China[422QN441] ; Open Project Program of the State Key Laboratory of Tropical Oceanography[LTOZZ2003]
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
WOS类目	Biodiversity Conservation ; Environmental Sciences
WOS记录号	WOS:001186911300001
出版者	ELSEVIER
WOS关键词	SEA-SURFACE TEMPERATURE ; PEARL RIVER ESTUARY ; SOUTH CHINA SEA ; FEATURE-SELECTION ; CHLOROPHYLL-A ; LEVEL RISE ; PHYTOPLANKTON ; BIODIVERSITY ; MODEL ; CONSERVATION
引用统计
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/184853
专题	中国科学院海洋研究所
通讯作者	Li, Cai
作者单位	1.Chinese Acad Sci, South China Sea Inst Oceanol, State Key Lab Trop Oceanog, Guangzhou 510300, Peoples R China 2.Univ Chinese Acad Sci, Inst Oceanol, Beijing 100049, Peoples R China 3.State Ocean Adm, Natl Satellite Ocean Applicat Serv, Beijing 100081, Peoples R China
第一作者单位	中国科学院海洋研究所
通讯作者单位	中国科学院海洋研究所
推荐引用方式 GB/T 7714	Zheng, Yuanning,Li, Cai,Zhang, Xianqing,et al. Estimation of water quality parameters based on time series hydrometeorological data in Miaowan Island[J]. ECOLOGICAL INDICATORS,2024,159:11.
APA	Zheng, Yuanning,Li, Cai,Zhang, Xianqing,Zhao, Wei,Yang, Zeming,&Cao, Wenxi.(2024).Estimation of water quality parameters based on time series hydrometeorological data in Miaowan Island.ECOLOGICAL INDICATORS,159,11.
MLA	Zheng, Yuanning,et al."Estimation of water quality parameters based on time series hydrometeorological data in Miaowan Island".ECOLOGICAL INDICATORS 159(2024):11.