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Transfer learning for neural network model in chlorophyll-a dynamics prediction

  • Wenchong Tian
  • Zhenliang LiaoEmail author
  • Xuan Wang
Research Article
  • 39 Downloads

Abstract

Neural network models have been used to predict chlorophyll-a concentration dynamics. However, as model generalization ability decreases, (i) the performance of the models gradually decreases over time; (ii) the accuracy and performance of the models need to be improved. In this study, Transfer learning (TL) is employed to optimize neural network models (including feedforward neural networks (FNN), recurrent neural networks (RNN) and long short-term memory (LTSM)) and overcome these problems. Models using TL are able to reduce the influence of mutable data distribution and enhance generalization ability. Thus, it can improve the accuracy of prediction and maintain high performance in long-term applications. Also, TL is compared with parameter norm penalties (PNP) and dropout—two other methods used to improve model generalization ability. In general, TL has a better prediction effect than PNP and dropout. All the models, including FNN with different architectures, RNN and LSTM, as well as models optimized by PNP, dropout, and TL, are applied to an estuary reservoir in eastern China to predict chlorophyll-a dynamics at 5-min intervals. According to the results of this study, (i) models with TL produce the best prediction results; (ii) the original models and the models with PNP and dropout lose their ability to predict within 3 months, while TL models retain a high prediction accuracy.

Keywords

Transfer learning Chlorophyll-a dynamics Feedforward neural networks Recurrent neural network Long short-term memory 

Notes

Funding information

This study was financially supported by The National Key Research and Development Program of China (Grant No.2016YFE0123300 and 2017YFC0405406) and the National Natural Science Foundation of China (Grant No. 51578396).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.UNEP-Tongji Institute of Environment for Sustainable Development, College of Environmental Science and EngineeringTongji UniversityShanghaiPeople’s Republic of China
  2. 2.Shanghai Institute of Pollution Control and Ecological SecurityShanghaiPeople’s Republic of China
  3. 3.Key Laboratory of Yangtze River Water Environment (Ministry of Education),Tongji UniversityShanghaiPeople’s Republic of China
  4. 4.College of Civil Engineering and ArchitectureXinjiang UniversityUrumqiPeople’s Republic of China

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