Multi-scale prediction of water temperature using empirical mode decomposition with back-propagation neural networks. (January 2016) - Liu, Shuangyin; Xu, Longqin; Li, Daoliang
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.