Abstract
The forecasting methods of water bloom in existence are hard to reflect nonlinear dynamic change in algal bloom formation mechanism, leading to poor forecasting accuracy of bloom. To solve this problem, this paper deeply analyzes the generation process of algal bloom, introduces the recursive time series algorithm into the deep belief network model and improves the model structure and training algorithm, and proposes a forecasting method based on the recursive timed deep belief network model. The model introduces the current moments and historical time values of the characterization factors and influencing factors at the input layer, and increases the connection between the input layer and the hidden layer of the deep belief network. A recursive algorithm is used to establish the relationship between the current time value of the characterization factor and the historical time value of the characterization factor, and the connection between the current time value of the hidden layer and the influencing factor is increased. By re-extracting the characteristics of the hidden layer at each moment, and then fine tuning the network parameters by the BP neural network, a recursive timing deep belief network model is finally constructed. The results show that compared with the existing forecasting methods, this method can extract the characteristics of time series data more accurately and completely to deal with the dynamic nonlinear process and can further improve the forecast accuracy of algal blooms.
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Acknowledgements
This work was financially supported by National Natural Science Foundation of China (61703008), Support Project of High-level Teachers in Beijing Municipal Universities in the Period of 13th Five-year Plan (CIT&TCD201804014), and Major Project of Beijing Municipal Education Commission science and technology development plans (KZ201510011011). Those supports are gratefully acknowledged.
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Wang, L., Zhang, T., Jin, X. et al. An approach of recursive timing deep belief network for algal bloom forecasting. Neural Comput & Applic 32, 163–171 (2020). https://doi.org/10.1007/s00521-018-3790-9
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DOI: https://doi.org/10.1007/s00521-018-3790-9