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Chlorophyll Prediction Using Ensemble Deep Learning Technique

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

Abstract

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.

Keywords

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

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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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