Chlorophyll Prediction Using Ensemble Deep Learning Technique

  • Ashapurna MarndiEmail author
  • G. K. Patra
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1119)


Chlorophyll is an essential component of phytoplankton and plays an important role in food chain and nutrient cycle required for survival of marine creatures. Getting suitable fishing zone is one of the commercial usages of liveliness measurement of marine ecosystem. Optimal sustainability of marine ecosystems needs an accurate prediction of chlorophyll. Dynamical models to predict the chlorophyll are challenged by the complex physical, chemical, and biological processes. Numerous researchers have attempted to address this problem using various computationally intelligent methods such as neural networks. However, normal neural networks have failed to provide a reliable forecast. This paper proposes a novel ensemble forecasting using Long Short Term Memory (LSTM) and a deep learning (DL) approach for time series data analysis. The methodology was tested to predict chlorophyll in Arabian Sea and found satisfactory result. Improved capabilities of the proposed method are also been demonstrated through various statistical analyses.


Ensemble forecasting Long short-term memory Artificial intelligence Deep neural networks Chlorophyll prediction 


  1. 1.
    Das, H., Naik, B., Behera, H.S.: Classification of Diabetes Mellitus Disease (DMD): A Data Mining (DM) approach. In: Progress in Computing, Analytics and Networking, pp. 539–549. Springer, Singapore (2018)Google Scholar
  2. 2.
    Sahoo, A.K., Mallik, S., Pradhan, C., Mishra, B.S.P., Barik, R.K., Das, H.: Intelligence-based health recommendation system using big data analytics. In: Big Data Analytics for Intelligent Healthcare Management, pp. 227–246. Academic (2019)Google Scholar
  3. 3.
    Li, X., Sha, J., Wang, Z.-L.: Application of feature selection and regression models for chlorophyll-a prediction in a shallow lake. Environ. Sci. Pollut. Res. 1–11 (2018)Google Scholar
  4. 4.
    Yajima, H., Derot, J.: Application of the Random Forest model for chlorophyll-a forecasts in fresh and brackish water bodies in Japan, using multivariate long-term databases. J. Hydro Inf. 20, 206–220 (2018)CrossRefGoogle Scholar
  5. 5.
    Cho, H.: Deep: Learning Application to Time Series Prediction of Daily Chlorophyll-a Concentration (2018)Google Scholar
  6. 6.
    X. et al.: Long short-term memory neural network for air pollutant concentration predictions: method development and evaluation. Environ. Pollut. 231, pp. 997–1004 (2017)Google Scholar
  7. 7.
    Lee, G., Bae, J., Lee, S., Jang, M., Park, H.: Monthly chlorophyll-a prediction using neuro-genetic algorithm for water quality management in Lakes. Desalin. Water Treat. 57, 26783–26791 (2016)Google Scholar
  8. 8.
    Lee, G., Othman, F., Ibrahim, S., Jang, M.: Determination of the forecasting-model parameters by statistical analysis for development of algae warning system. Desalin. Water Treat. 57, 26773–26782 (2016)CrossRefGoogle Scholar
  9. 9.
    Cho, K.H., Kang, J.-H., Ki, S.J., Park, Y., Cha, S.M., Kim, J.H.: Determination of the optimal parameters in regression models for the prediction of chlorophyll-a: a case study of the Yeongsan reservoir. Korea. Sci. Total Environ. 407, 2536–2545 (2009)CrossRefGoogle Scholar
  10. 10.
    Krasnopolsky, V., Nadiga, S., Mehra, A., Bayler, E., Behringer, D.: Neural networks technique for filling gaps in satellite measurements: application to ocean color observations. Comput. Intell. Neurosci. 2016, 9. Article ID 6156513.
  11. 11.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)CrossRefGoogle Scholar
  12. 12.
    ESSO—Indian National Centre for Ocean Information Services.
  13. 13.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Academy of Scientific and Innovative ResearchGhaziabadIndia
  2. 2.Council of Scientific and Industrial Research, Fourth Paradigm InstituteBengaluruIndia

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