Ensemble learning chlorophyll-<italic style="font-style: italic">a</italic> concentration prediction in lakes based on multisource heterogeneous data

Haibo LONG; Yuan ZHANG; Xu ZHANG; Zhenzhen LIU; Liqiong CHEN; Jianzhong LU

doi:10.1007/s00343-026-5531-8

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Ensemble learning chlorophyll-a concentration prediction in lakes based on multisource heterogeneous data

Inland water color remote sensing algorithms | Updated：2026-09-10

- Ensemble learning chlorophyll-a concentration prediction in lakes based on multisource heterogeneous data
- Journal of Oceanology and Limnology Vol. 44, Pages: 2063-2080(2026)
- Affiliations：
  
  1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  2.Central Station of Ecology and Environment Monitoring of Hubei Province, Wuhan 430070, China
- Author bio：
  
  lujzhong@whu.edu.cn
- Funds：
- DOI：10.1007/s00343-026-5531-8
  CLC：
- Received：12 January 2026，
  
  Online First：10 September 2026，
  
  Published：01 September 2026
- Accepted：
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LONG Haibo,ZHANG Yuan,ZHANG Xu,et al.Ensemble learning chlorophyll-,a concentration prediction in lakes based on multisource heterogeneous data[J].Journal of Oceanology and Limnology,
DOI：

LONG Haibo,ZHANG Yuan,ZHANG Xu,et al.Ensemble learning chlorophyll-,a concentration prediction in lakes based on multisource heterogeneous data[J].Journal of Oceanology and Limnology, DOI：.

摘要

Abstract

Chlorophyll

(Chl

) in lakes serves as a pivotal indicator for evaluating algal blooms

rendering its short-term prediction crucial for lake ecological management. Traditional physical models

encounter limitations due to their intricate parameters and suboptimal predictive capabilities. Meanwhile

data-driven models rely primarily on data from a single source. To address these challenges

this study introduces a Chl-

prediction method grounded in multisource heterogeneous data and ensemble learning. This method amalgamates two types data: satellite images and water quality parameters. It uses gray relation analysis (GRA) and variance inflation factor (VIF) test to filter the water quality parameters

which together with the spectral features are used as input variables. Furthermore

the satellite-field matched dataset is constructed. Then a Stacking ensemble learning model is formulated

integrating support vector machine (SVM)

random forest (RF) and long short-term memory (LSTM) with temporal attention mechanism. The results demonstrate that the ensemble model achieves

of 0.624

MAE of 2.653 μg/L

MAPE of 0.756 and RMSE of 3.733 μg/L. These results significantly surpass the individual LSTM

SVM

and RF method. The temporal attention mechanism enhances model accuracy by focusing on crucial time steps and considers early water quality parameters which have time lags. Additionally

prediction accuracy continues to escalate as the predicted time period increases (

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=117593781&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=117593779&type=

1.60866666

2.28600001

7 d). This study emphasizes the effectiveness of multisource heterogeneous data and ensemble learning in predicting Chl

. It also offers novel insights for the development of early warning systems for lake’s water quality.

关键词

Keywords

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Ensemble learning chlorophyll-a concentration prediction in lakes based on multisource heterogeneous data

DOI：10.1007/s00343-026-5531-8

摘要

Abstract

关键词

Keywords

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