Temporal continuous monitoring of cyanobacterial blooms in Lake Taihu at an hourly scale using machine learning

Siqi Wang; Xiang Zhang; Chao Wang; Nengcheng Chen

doi:10.1016/j.scitotenv.2022.159480

Temporal continuous monitoring of cyanobacterial blooms in Lake Taihu at an hourly scale using machine learning

Sci Total Environ. 2023 Jan 20;857(Pt 2):159480. doi: 10.1016/j.scitotenv.2022.159480. Epub 2022 Oct 18.

Authors

Siqi Wang¹, Xiang Zhang², Chao Wang³, Nengcheng Chen⁴

Affiliations

¹ State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China; Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China. Electronic address: wangsiqiwhu@whu.edu.cn.
² Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China; National Engineering Research Centre of Geographic Information System, China University of Geosciences, Wuhan 430074, China.
³ State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China; Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China.
⁴ State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China; Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China; National Engineering Research Centre of Geographic Information System, China University of Geosciences, Wuhan 430074, China. Electronic address: cnc@whu.edu.cn.

PMID: 36265631
DOI: 10.1016/j.scitotenv.2022.159480

Abstract

Cyanobacterial blooms in most lakes exhibit extraordinary changes in time and space. Herein, a cyanobacterial prediction model was designed for Lake Taihu based on a machine learning method. This method can generate temporally continuous (24 moments throughout the day) cyanobacterial data at a fine spatial scale of 9 km. The hourly meteorological data for 24 moments of the day were obtained from ERA5-Land data. Areal coverage of cyanobacterial blooms was derived from the hourly Geostationary Ocean Color Imager reflectance data observed only eight times a day (from ~8:00 to ~15:00, UTC+8). The cyanobacterial and meteorological data of eight moments in Lake Taihu from 2011 to 2020 were used to design the prediction model. The results were compared and validated employing nine training strategies to determine the best cyanobacterial prediction model for Lake Taihu (R = 0.42; root mean square error = 0.10). With the best-fitted model utilizing meteorological data (2011-2020), the area coverage of cyanobacterial blooms at the other 16 moments during a day were estimated. Based on this, the regional and temporal characteristics of diurnal bloom variation were evaluated at an hourly scale. The results indicated that the hourly variations in the areal coverage of cyanobacterial blooms at 24 moments of the day had similar patterns in each subregion of Lake Taihu with minor seasonal variations. The six meteorological variables adopted to construct the model had similar diurnal changes but with diverse value ranges among the seasons. Further analysis revealed that three meteorological variables (temperature, surface pressure, and evaporation) were positively related to diurnal bloom variations at an hourly scale. Overall, these results illustrate that meteorological conditions can affect the occurrence of cyanobacterial blooms at multiple time scales (e.g., hourly, daily, or monthly). The developed cyanobacterial prediction model can provide cyanobacterial data when cyanobacterial data is unavailable for the target waterbody.

Keywords: Cyanobacterial blooms; Hourly prediction; LightGBM; Machine learning; Spatially continuous.

MeSH terms

China
Cyanobacteria*
Environmental Monitoring
Eutrophication
Lakes* / microbiology
Machine Learning
Seasons