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An optimization of artificial neural network model for predicting chlorophyll dynamics

Author

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  • Tian, Wenchong
  • Liao, Zhenliang
  • Zhang, Jin

Abstract

As one of the factors to represent some species of algae, chlorophyll dynamics model has been regarded as one of the early-warning proactive approaches to prevent or mitigate the occurrence of some algal blooms. To decrease the cost of aquatic environmental in-situ monitoring and increase the accuracy of bloom forecasting, a traditional artificial neural network (ANN) based chlorophyll dynamics prediction model had been optimized. This optimization approach was conducted by presenting the change of chlorophyll value rather than the base value of chlorophyll as the output variable of the network. Both of the optimized and traditional networks had been applied to a case study. The results of model performance indices show that the optimized network predicts better than the traditional network. Furthermore, the non-stationary time series was employed to explain this phenomenon from a theoretical aspect. The proposed approach for chlorophyll dynamics ANN model optimization could assist the essential proactive strategy for algal bloom control.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ecomod:v:364:y:2017:i:c:p:42-52
    DOI: 10.1016/j.ecolmodel.2017.09.013
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    References listed on IDEAS

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    1. Oh, Hee-Mock & Ahn, Chi-Yong & Lee, Jae-Won & Chon, Tae-Soo & Choi, Kyung Hee & Park, Young-Seuk, 2007. "Community patterning and identification of predominant factors in algal bloom in Daechung Reservoir (Korea) using artificial neural networks," Ecological Modelling, Elsevier, vol. 203(1), pages 109-118.
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    Cited by:

    1. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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.

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