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Time-Series Forecasting of Chlorophyll-a in Coastal Areas Using LSTM, GRU and Attention-Based RNN Models

S. S. Wu1, Z. H. Du1,2 *, F. Zhang1,2, Y. Zhou3,4, and R. Y. Liu1,2

  1. School of Earth Sciences, Zhejiang University, Hangzhou, Zhejiang 310027, China
  2. Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, Zhejiang 310028, China
  3. Zhejiang Fisheries Technical Extension Center, Hangzhou, Zhejiang 310013, China
  4. Zhejiang Province Academy of Marine Science, Hangzhou, Zhejiang 310012, China

*Corresponding author. Tel.: +0571-8827-3287. E-mail address: (Z. H. Du).


The chlorophyll-a (Chl-a) concentration is commonly considered as the main indicator of phytoplankton biomass in coastal waters. Forecasting and understanding the status of Chl-a is beneficial to coastal ecosystem management and is an important emergency management measure for algae blooms. To obtain accurate predictions, the long short-term memory neural network (LSTM) and gated recurrent unit neural network (GRU) were implemented for Chl-a forecasting, and based on the LSTM and GRU units, two simplified attention-based encoder-decoder recurrent neural network (AEDRNN) models were also developed for time series predictions. The performance of the proposed models was compared with that of the auto-regressive integrated moving average (ARIMA), multilayer perceptron (MLP) and Elman recurrent neural network (ERNN) models by experimentally generating multi-step-ahead predictions using a dataset in the Zhejiang coastal areas of China. The results demonstrated that the LSTM, GRU and AEDRNN models significantly outperformed the ARIMA, MLP and ERNN models according to multiple statistical indicators. Moreover, the AEDRNN models were superior to the LSTM and GRU models, especially for middle-term predictions. In addition, the AEDRNN model with LSTM units was more robust than the AEDRNN model with GRU units in terms of accuracy and stability; therefore, it was considered to be the best model for Chl-a forecasting.

Keywords: attention-based encoder-decoder recurrent neural network, algal blooms, chlorophyll-a, gated recurrent unit neural network, long short-term memory neural network, time-series forecasting

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