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Forecasting chlorophyll‐a concentration using empirical wavelet transform and support vector regression

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  • Jin‐Won Yu
  • Ju‐Song Kim
  • Yun‐Chol Jong
  • Xia Li
  • Gwang‐Il Ryang

Abstract

Accurate forecast of chlorophyll‐a concentration of water bodies is important for aquatic management because it can support management decisions with future information. In this regard, this paper proposes a new chlorophyll‐a forecast method that combines empirical wavelet transform, support vector regression, and sine cosine algorithm. Chlorophyll‐a concentration data are decomposed by empirical wavelet transform, and then, support vector regression is employed to predict decomposed components, and finally, forecast value is obtained by reconstructing the predicted values for the decomposed components. Hyperparameters of support vector regression models are optimized by sine cosine algorithm. Our model is evaluated by chlorophyll‐a concentration data of Lake Kasumigaura, Japan. For the purpose of comparison, several other models are also developed. Result indicates that our method shows better forecast performance than other competitor models. This study demonstrates that data processing by empirical wavelet transform can significantly improve forecast accuracy and our method is a promising new forecast method for lake chlorophyll‐a concentration.

Suggested Citation

  • Jin‐Won Yu & Ju‐Song Kim & Yun‐Chol Jong & Xia Li & Gwang‐Il Ryang, 2022. "Forecasting chlorophyll‐a concentration using empirical wavelet transform and support vector regression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1691-1700, December.
    • Handle: RePEc:wly:jforec:v:41:y:2022:i:8:p:1691-1700
    DOI: 10.1002/for.2890
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    1. Patricia Jimeno-Sáez & Javier Senent-Aparicio & José M. Cecilia & Julio Pérez-Sánchez, 2020. "Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)," IJERPH, MDPI, vol. 17(4), pages 1-14, February.
    2. García Nieto, P.J. & García-Gonzalo, E. & Alonso Fernández, J.R. & Díaz Muñiz, C., 2019. "Water eutrophication assessment relied on various machine learning techniques: A case study in the Englishmen Lake (Northern Spain)," Ecological Modelling, Elsevier, vol. 404(C), pages 91-102.
    3. Shen, Jian & Qin, Qubin & Wang, Ya & Sisson, Mac, 2019. "A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading," Ecological Modelling, Elsevier, vol. 398(C), pages 44-54.
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    1. Wang, Jianguo & Han, Lincheng & Zhang, Xiuyu & Wang, Yingzhou & Zhang, Shude, 2023. "Electrical load forecasting based on variable T-distribution and dual attention mechanism," Energy, Elsevier, vol. 283(C).
    2. Anurag Kulshrestha & Abhishek Yadav & Himanshu Sharma & Shikha Suman, 2024. "A deep learning‐based multivariate decomposition and ensemble framework for container throughput forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2685-2704, November.

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