A soft sensor for simulating algal cell density based on dynamic response to environmental changes in a eutrophic shallow lake
Open accessAbstract
There is a great need for timely monitoring and rapid water quality assessment to control the algal blooms that often occur in eutrophic lakes. While algal cell density (ACD) is a critical indicator of algal growth, field monitoring is laborious and time-consuming, and rapid assessment of algal blooms based on ACD is often not possible. To address the limitations of conventional ACD detection, we proposed a soft sensor approach that uses surrogate indicators to simulate ACD in machine learning models. We conducted a case study using monitoring data from Chaohu Lake collected between 2016 and 2019. We found that ensemble learning models, especially extreme gradient boosting (XGBoost), outperformed traditional machine learning algorithms by comparing various machine learning algorithms. Also, considering the influence of input variable selection on model performance, we combined the results of different filter methods in the multi-stage variable selection process. Finally, we screened out seven key variables out of the 43 initial candidate variables, including dissolved oxygen (DO), chlorophyll-a (Chl-a), Secchi disk depth (SD), pH, permanganate index (COD Mn ), week of the year (WOY), and wind velocity (WV). Their inclusion substantially improved data accessibility and supported the development of a rapid simulation model. The final model was capable of reliable spatiotemporal generalization, with an overall R 2 value of 0.761. On the theoretical side, our study makes a new attempt to simulate ACD values in a eutrophic lake. For practical purposes, the soft sensor can facilitate the rapid assessment of bloom conditions, which helps the local administration with emergency prevention and control.
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Sensors frequently generate dirty data that include inaccurate, aberrant, or missing values, necessitating data cleaning (outlier identification and missing value imputation).
“Sensors frequently generate dirty data that include inaccurate, aberrant, or missing values, necessitating data cleaning (outlier identification and missing value imputation) [68].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [68]
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- Authors
- Rao, W., Qian, X., Fan, Y., & Liu, T.
- Affiliations
- State Key Laboratory of Pollution Control and Resource Reuse (CN)
- Nanjing University of Information Science and Technology (CN)
- Hokkaido University (JP)
- Corresponding author
- Qian, Xin
- Venue
- Science of The Total Environment
- Publisher
- Elsevier BV
- Published
- 2023-04
- Volume
- 868
- Pages
- 161543
- ISSN
- 0048-9697 (ISSN-L)
- OpenAlex
- W4315645287
- Cited by
- 15
- References
- 77
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 9
- FWCI
- 1.06
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- The Science of The Total Environment (publishedVersion)
- SSRN Electronic Journal (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (41907388)
- Natural Science Foundation of Jiangsu Province (BK20211155)
- Topics
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
- Physical Sciences › Environmental Science › Waste Management and Disposal › Water Quality Monitoring and Analysis
- Keywords
- algal density, soft sensors, algal bloom monitoring, eutrophic shallow lakes, Chaohu Lake, surrogate markers, XGBoost, ensemble learning, feature selection, dissolved oxygen, chlorophyll a, Secchi depth, permanganate index, wind speed, water quality assessment
- MeSH terms
- Chlorophyll A, China, Environmental Monitoring, Eutrophication, Lakes, Water Quality
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