Journal
COMPUTERS & ELECTRICAL ENGINEERING
Volume 49, Issue -, Pages 1-8Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compeleceng.2015.10.003
Keywords
Empirical mode decomposition; Back-propagation neural network; Water temperature; Multi-scale prediction
Categories
Funding
- Special Fund for Agro-scientific Research in the Public Interest [201203017]
- National Natural Science Foundation [61471133]
- National Science and Technology Supporting Plan Project [2012BAD35B07]
- Guangdong Science and Technology Plan Project [2013B090500127, 2013B021600014, 2015A070709015, 2015A020209171]
- Guangdong Natural Science Foundation [S2013010014629]
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In order to reduce aquaculture risks and optimize the operation of water quality management in prawn engineering culture ponds, this paper proposes a novel water temperature forecasting model based on empirical mode decomposition (EMD) and back-propagation neural network (BPNN). First, the original water temperature datasets are decomposed into a collection of intrinsic mode functions (IMFs) and a residue by EMD yields relatively stationary sub-series that can be readily modeled by BPNN. Second, both IMF components and residue is applied to establish the corresponding BPNN models. Then, each sub-series is predicted using the corresponding BPNN. Finally, the prediction values of the original water temperature datasets are calculated by the sum of the forecasting values of every sub-series. The proposed hybrid model was applied to predict water temperature in prawn culture ponds. Compared with traditional models, the simulation results of the hybrid EMD-BPNN model demonstrate that de-noising and capturing non-stationary characteristics of water temperature signals after EMD comprise a very powerful and reliable method for predicting water temperature in intensive aquaculture accurately and quickly. (C) 2015 Elsevier Ltd. All rights reserved.
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