three-dimensional short-term prediction model of dissolved oxygen content based on pso-bpann algorithm coupled with kriging interpolation

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ID: 176993
2016
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Abstract
Dissolved oxygen (DO) content is a significant aspect of water quality in aquaculture. Prediction of dissolved oxygen may timely avoid the financial loss caused by inappropriate dissolved oxygen content and three-dimensional prediction can achieve more accurate and overall guidance. Therefore, this study presents a three-dimensional short-term prediction model of dissolved oxygen in crab aquaculture ponds based on back propagation artificial neural network (BPANN) optimized by particle swarm optimization (PSO), which coupled with Kriging method. In this model, wavelet analysis is adopted for denoising, BPANN optimized by PSO is utilized for data analysis and one-dimensional prediction, and Kriging method is used for three-dimensional prediction. Compared with traditional one-dimensional prediction model, three-dimensional model has more real reaction of dissolved oxygen content in crab growth environment. In particular, the merits of PSO are evaluated against genetic algorithm (GA). The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for PSO model are 0.136445, 0.90534, and 0.15384, respectively, while for the GA model the values are 2.04184, 1.18316, and 0.21014, respectively. Furthermore, results of cross validation experiment show that the average error of this model is 0.0705 (mg/L). Consequently, this study suggests that the prediction model operates in a satisfactory manner.
Reference Key
chen2016mathematicalthree-dimensional Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Yingyi Chen;Jing Xu;Huihui Yu;Zhumi Zhen;Daoliang Li
Journal journal of power sources
Year 2016
DOI
10.1155/2016/6564202
URL
http://dx.doi.org/10.1155/2016/6564202
Keywords
civil engineering (general)mathematics engineering (general)

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