Multi-scale prediction of water temperature using empirical mode decomposition with back-propagation neural networks. (January 2016)

• Record Type: Journal Article
• Authors: Liu, Shuangyin; Xu, Longqin; Li, Daoliang
• Is Part Of: Computers & electrical engineering. Volume 49(2016)
• Page Start: 1
• Page End: 8
• Publication Date: 2016-01
• Subjects: Empirical mode decomposition -- Back-propagation neural network -- Water temperature -- Multi-scale prediction; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.10.003
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: The novel model which combines EMD and BPNN algorithm is presented to predict water temperature in intensive aquaculture.. Using EMD technology adaptively decomposed the original water temperature data into a finite set of IMFs and a residue. EMD-BPNN has higher prediction accuracy and better generalization performance than standard BPNN and standard SVR. EMD-BPNN can be used as a suitable and effective modeling tool for predicting water temperature in intensive aquaculture. Abstract: 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.