IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12784-d935615.html
   My bibliography  Save this article

A Method of Cyanobacterial Concentrations Prediction Using Multispectral Images

Author

Listed:
  • Xiyong Zhao

    (College of Mechanical Engineering, Guangxi University, Nanning 530004, China
    Guangxi Bossco Environmental Protection Technology Co., Ltd., Nanning 530007, China
    Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China)

  • Yanzhou Li

    (College of Mechanical Engineering, Guangxi University, Nanning 530004, China)

  • Yongli Chen

    (Guangxi Bossco Environmental Protection Technology Co., Ltd., Nanning 530007, China)

  • Xi Qiao

    (College of Mechanical Engineering, Guangxi University, Nanning 530004, China
    Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China)

Abstract

With the increasingly serious eutrophication of inland water, the frequency and scope of harmful cyanobacteria blooms are increasing, which affects the ecological balance and endangers human health. The aim of this study was to propose an alternative method for the quantification of cyanobacterial concentrations in water by correlating multispectral data. The research object was the cyanobacteria in Erhai Lake, Dali, China. Ten monitoring sites were selected, and multispectral images and cyanobacterial concentrations were measured in Erhai Lake from September to November 2021. In this study, multispectral data were used as independent variables, and cyanobacterial concentrations as dependent variables. We performed curve estimation, and significance analysis for the independent variables, and compared them with the original variable model. Here, we chose about four algorithms to establish models and compare their applicability, including Multivariable Linear Regression (MLR), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Extreme Learning Machine (ELM). The prediction performance was evaluated by the coefficient of determination (R 2 ), Root-Mean-Square Error (RMSE), and Mean Relative Error (MRE). The results showed that the variable analysis model outperformed the original variable model, the ELM was superior to other algorithms, and the variable analysis model based on the ELM algorithm achieved the best results (R 2 = 0.7609, RMSE = 4197 cells/mL, MRE = 0.044). This study confirmed the applicability of cyanobacterial concentrations prediction using multispectral data, which can be characterized as a quick and easy methodology, and the deep neural network has great potential to predict the concentration of cyanobacteria.

Suggested Citation

  • Xiyong Zhao & Yanzhou Li & Yongli Chen & Xi Qiao, 2022. "A Method of Cyanobacterial Concentrations Prediction Using Multispectral Images," Sustainability, MDPI, vol. 14(19), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12784-:d:935615
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12784/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/12784/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yan Wei & Haocai Huang & Bin Chen & Bofu Zheng & Yihong Wang, 2019. "Application of Extreme Learning Machine for Predicting Chlorophyll-a Concentration Inartificial Upwelling Processes," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-11, May.
    2. Fatin Nadiah Yussof & Normah Maan & Mohd Nadzri Md Reba, 2021. "LSTM Networks to Improve the Prediction of Harmful Algal Blooms in the West Coast of Sabah," IJERPH, MDPI, vol. 18(14), pages 1-14, July.
    3. David C. Hoaglin, 2016. "Regressions are commonly misinterpreted," Stata Journal, StataCorp LP, vol. 16(1), pages 5-22, March.
    4. Manuel Viso-Vázquez & Carolina Acuña-Alonso & Juan Luis Rodríguez & Xana Álvarez, 2021. "Remote Detection of Cyanobacterial Blooms and Chlorophyll-a Analysis in a Eutrophic Reservoir Using Sentinel-2," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
    5. Xiaofan Wang & Lingyu Xu, 2020. "Unsteady Multi-Element Time Series Analysis and Prediction Based on Spatial-Temporal Attention and Error Forecast Fusion," Future Internet, MDPI, vol. 12(2), pages 1-13, February.
    6. David C. Hoaglin, 2016. "Regressions are commonly misinterpreted: A rejoinder," Stata Journal, StataCorp LP, vol. 16(1), pages 30-36, March.
    7. Ming-Wei Li & Jing Geng & Wei-Chiang Hong & Yang Zhang, 2018. "Hybridizing Chaotic and Quantum Mechanisms and Fruit Fly Optimization Algorithm with Least Squares Support Vector Regression Model in Electric Load Forecasting," Energies, MDPI, vol. 11(9), pages 1-22, August.
    8. Shaoqian Pei & Hui Qin & Liqiang Yao & Yongqi Liu & Chao Wang & Jianzhong Zhou, 2020. "Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network," Energies, MDPI, vol. 13(16), pages 1-23, August.
    9. Li Mao & Lidong Zhang & Xingyang Liu & Chaofeng Li & Hong Yang, 2014. "Improved Extreme Learning Machine and Its Application in Image Quality Assessment," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-7, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pillai N., Vijayamohanan, 2016. "How Do You Interpret Your Regression Coefficients?," MPRA Paper 76867, University Library of Munich, Germany.
    2. Bornmann, Lutz & Adams, Jonathan & Leydesdorff, Loet, 2018. "The negative effects of citing with a national orientation in terms of recognition: National and international citations in natural-sciences papers from Germany, the Netherlands, and the UK," Journal of Informetrics, Elsevier, vol. 12(3), pages 931-949.
    3. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
    4. José García & José V. Martí & Víctor Yepes, 2020. "The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm," Mathematics, MDPI, vol. 8(6), pages 1-22, May.
    5. Yuanyuan Zhou & Min Zhou & Qing Xia & Wei-Chiang Hong, 2019. "Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory," Mathematics, MDPI, vol. 7(12), pages 1-23, December.
    6. Guo-Feng Fan & Yan-Hui Guo & Jia-Mei Zheng & Wei-Chiang Hong, 2019. "Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 12(5), pages 1-19, March.
    7. Sana Mujeeb & Nadeem Javaid & Manzoor Ilahi & Zahid Wadud & Farruh Ishmanov & Muhammad Khalil Afzal, 2019. "Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities," Sustainability, MDPI, vol. 11(4), pages 1-29, February.
    8. Yamin Shen & Yuxuan Ma & Simin Deng & Chiou-Jye Huang & Ping-Huan Kuo, 2021. "An Ensemble Model based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    9. Mehmet Türker Takcı & Tuba Gözel, 2022. "Effects of Predictors on Power Consumption Estimation for IT Rack in a Data Center: An Experimental Analysis," Sustainability, MDPI, vol. 14(21), pages 1-19, November.
    10. Guo‐Feng Fan & Yan‐Hui Guo & Jia‐Mei Zheng & Wei‐Chiang Hong, 2020. "A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 737-756, August.
    11. Xiaocong Xiao & Jianxun Liu & Deshun Liu & Yufei Tang & Shigang Qin & Fan Zhang, 2022. "A Normal Behavior-Based Condition Monitoring Method for Wind Turbine Main Bearing Using Dual Attention Mechanism and Bi-LSTM," Energies, MDPI, vol. 15(22), pages 1-17, November.
    12. Jicheng Liu & Yu Yin, 2022. "Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in China," Energies, MDPI, vol. 15(3), pages 1-23, February.
    13. Hao Ma & Peng Yang & Fei Wang & Xiaotian Wang & Di Yang & Bo Feng, 2023. "Short-Term Heavy Overload Forecasting of Public Transformers Based on Combined LSTM-XGBoost Model," Energies, MDPI, vol. 16(3), pages 1-16, February.
    14. Ankit Kumar Srivastava & Ajay Shekhar Pandey & Mohamad Abou Houran & Varun Kumar & Dinesh Kumar & Saurabh Mani Tripathi & Sivasankar Gangatharan & Rajvikram Madurai Elavarasan, 2023. "A Day-Ahead Short-Term Load Forecasting Using M5P Machine Learning Algorithm along with Elitist Genetic Algorithm (EGA) and Random Forest-Based Hybrid Feature Selection," Energies, MDPI, vol. 16(2), pages 1-23, January.
    15. Seon Hyeog Kim & Gyul Lee & Gu-Young Kwon & Do-In Kim & Yong-June Shin, 2018. "Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting," Energies, MDPI, vol. 11(12), pages 1-17, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12784-:d:935615. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.