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Optimization of deep learning model for coastal chlorophyll a dynamic forecast

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
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