IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v467y2022ics0304380022000370.html
   My bibliography  Save this article

Optimization of deep learning model for coastal chlorophyll a dynamic forecast

Author

Listed:
  • Wenxiang, Ding
  • Caiyun, Zhang
  • Shaoping, Shang
  • Xueding, Li

Abstract

Chlorophyll a is an important factor in characterizing algal biomass. Its dynamic forecast model is considered to be one of the best early warning methods to prevent or alleviate the occurrence of algal blooms. In this study, the absolute concentration of Chlorophyll (Chl), the change rate of Chl (ΔChl), and the relative change rate of Chl (ΔRChl) were used as the output of a long short-term memory (LSTM) model. The model was used to carry out Chl dynamic forecasts for different seasons in Xiamen Bay. The results show that the Chl forecast result obtained using ΔChl and ΔRChl is much better than the forecast using Chl. Combining the Chl forecast results obtained using ΔChl and ΔRChl can solve the problem of overestimating the Chl high value, thereby improving the forecasting accuracy. Effectively applying our understanding of the mechanisms of deep learning forecasting models can improve forecasting capabilities.

Suggested Citation

  • Wenxiang, Ding & Caiyun, Zhang & Shaoping, Shang & Xueding, Li, 2022. "Optimization of deep learning model for coastal chlorophyll a dynamic forecast," Ecological Modelling, Elsevier, vol. 467(C).
    • Handle: RePEc:eee:ecomod:v:467:y:2022:i:c:s0304380022000370
    DOI: 10.1016/j.ecolmodel.2022.109913
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380022000370
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2022.109913?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kim, Hyo Gyeom & Hong, Sungwon & Jeong, Kwang-Seuk & Kim, Dong-Kyun & Joo, Gea-Jae, 2019. "Determination of sensitive variables regardless of hydrological alteration in artificial neural network model of chlorophyll a: Case study of Nakdong River," Ecological Modelling, Elsevier, vol. 398(C), pages 67-76.
    2. Tian, Wenchong & Liao, Zhenliang & Zhang, Jin, 2017. "An optimization of artificial neural network model for predicting chlorophyll dynamics," Ecological Modelling, Elsevier, vol. 364(C), pages 42-52.
    3. Banerjee, Arnab & Scharler, Ursula M. & Fath, Brian D. & Ray, Santanu, 2017. "Temporal variation of keystone species and their impact on system performance in a South African estuarine ecosystem," Ecological Modelling, Elsevier, vol. 363(C), pages 207-220.
    4. Yoo-Geun Ham & Jeong-Hwan Kim & Jing-Jia Luo, 2019. "Deep learning for multi-year ENSO forecasts," Nature, Nature, vol. 573(7775), pages 568-572, September.
    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. Yuquan Qu & Diego G. Miralles & Sander Veraverbeke & Harry Vereecken & Carsten Montzka, 2023. "Wildfire precursors show complementary predictability in different timescales," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    2. Xuan Wang & Wenchong Tian & Zhenliang Liao, 2022. "Framework for Hyperparameter Impact Analysis and Selection for Water Resources Feedforward Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4201-4217, September.
    3. Weston Anderson & Shraddhanand Shukla & Jim Verdin & Andrew Hoell & Christina Justice & Brian Barker & Kimberly Slinski & Nathan Lenssen & Jiale Lou & Benjamin I. Cook & Amy McNally, 2024. "Preseason maize and wheat yield forecasts for early warning of crop failure," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    4. Jiulin Li & Jiangang Xu & Jinlong Chu, 2019. "The Construction of a Regional Ecological Security Pattern Based on Circuit Theory," Sustainability, MDPI, vol. 11(22), pages 1-17, November.
    5. Coulibaly, Saliya & Bessin, Florent & Clerc, Marcel G. & Mussot, Arnaud, 2022. "Precursors-driven machine learning prediction of chaotic extreme pulses in Kerr resonators," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    6. Xin Wei & Lulu Zhang & Junyao Luo & Dongsheng Liu, 2021. "A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(1), pages 471-497, October.
    7. Yumin Liu & Kate Duffy & Jennifer G. Dy & Auroop R. Ganguly, 2023. "Explainable deep learning for insights in El Niño and river flows," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    8. Vinay Kumar Gautam & Chaitanya B. Pande & Kanak N. Moharir & Abhay M. Varade & Nitin Liladhar Rane & Johnbosco C. Egbueri & Fahad Alshehri, 2023. "Prediction of Sodium Hazard of Irrigation Purpose using Artificial Neural Network Modelling," Sustainability, MDPI, vol. 15(9), pages 1-17, May.
    9. Li, Zhuo-Lin & Yu, Jie & Zhang, Xiao-Lin & Xu, Ling-Yu & Jin, Bao-Gang, 2022. "A Multi-Hierarchical attention-based prediction method on Time Series with spatio-temporal context among variables," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).
    10. Cheng, Wei & Wang, Yan & Peng, Zheng & Ren, Xiaodong & Shuai, Yubei & Zang, Shengyin & Liu, Hao & Cheng, Hao & Wu, Jiagui, 2021. "High-efficiency chaotic time series prediction based on time convolution neural network," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    11. Arturs Kempelis & Inese Polaka & Andrejs Romanovs & Antons Patlins, 2024. "Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems," Future Internet, MDPI, vol. 16(2), pages 1-14, January.
    12. Lu, Na & Niu, Jun & Kang, Shaozhong & Singh, Shailesh Kumar & Du, Taisheng, 2021. "A hybrid PCA-SEM-ANN model for the prediction of water use efficiency," Ecological Modelling, Elsevier, vol. 460(C).
    13. Fenghua Ling & Jing-Jia Luo & Yue Li & Tao Tang & Lei Bai & Wanli Ouyang & Toshio Yamagata, 2022. "Multi-task machine learning improves multi-seasonal prediction of the Indian Ocean Dipole," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    14. Yan, Qing-dong & Chen, Xiu-qi & Jian, Hong-chao & Wei, Wei & Wang, Wei-da & Wang, Heng, 2022. "Design of a deep inference framework for required power forecasting and predictive control on a hybrid electric mining truck," Energy, Elsevier, vol. 238(PC).
    15. Wei Fang & Yu Sha & Victor S. Sheng, 2022. "Survey on the Application of Artificial Intelligence in ENSO Forecasting," Mathematics, MDPI, vol. 10(20), pages 1-22, October.
    16. Xia, Rui & Zou, Lei & Zhang, Yuan & Zhang, Yongyong & Chen, Yan & Liu, Chengjian & Yang, Zhongwen & Ma, Shuqin, 2022. "Algal bloom prediction influenced by the Water Transfer Project in the Middle-lower Hanjiang River," Ecological Modelling, Elsevier, vol. 463(C).
    17. Prashant K. Srivastava & Manika Gupta & Ujjwal Singh & Rajendra Prasad & Prem Chandra Pandey & A. S. Raghubanshi & George P. Petropoulos, 2021. "Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 5504-5519, April.
    18. Hye-Suk Yi & Sangyoung Park & Kwang-Guk An & Keun-Chang Kwak, 2018. "Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea," IJERPH, MDPI, vol. 15(10), pages 1-20, September.

    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:eee:ecomod:v:467:y:2022:i:c:s0304380022000370. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    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.