Prediction of water temperature in prawn cultures based on a mechanism model optimized by an improved artificial bee colony

摘要

To reduce aquaculture risk and optimize water quality management in prawn culture ponds, this paper uses mechanistic and statistical analytic methods to propose a hybrid water temperature forecasting model based on the water temperature mechanism model (WTMM) with optimal parameters selected by an improved artificial bee colony (IABC) algorithm. Because of existing problems with using an artificial bee colony algorithm in modeling, an improved ABC with a dynamically adjusted inertia weight based on the fitness function value was implemented to improve local and global search abilities. Then, IABC was employed to adaptively search for the optimal combinatorial parameters needed in the WTMM model, which overcomes the blindness of and limits to parameter selection for the traditional WTMM model. We adopted an IABC-WTMM algorithm to construct a non-linear mechanical prediction model. The IABC-WTMM was tested and compared to other algorithms by applying it to the prediction of water temperature in prawn culture ponds. Experimental results show that the proposed IABC-WTMM could increase prediction accuracy and execute generalization performance better than the original water temperature mechanism model (O-WTMM) and back-propagation neural network (BP-NN), but was inferior to the standard LSSVR model. Overall, it is a suitable and effective method for predicting water temperature in intensive aquacultures. (C) 2017 Elsevier B.V. All rights reserved.