CAJ | 학술논문

针对用传统机理建模不能满足水体中亚硝酸盐浓度变化预测的问题，引用非线性自结合的时间序列网络，建立了基于NAR神经网络的养殖水体亚硝酸盐预测模型。采用2014年6—10月养殖塘口检测的亚硝酸盐的数据建模，建立了用于养殖水体亚硝酸盐模拟的NAR神经网络，并利用2014年11月的观测数据对模型的模拟能力进行了检验。结果显示，建立的养殖水体亚硝酸盐预测模型，可以很好地模拟水体中亚硝酸盐浓度的变化趋势，模拟的绝对误差平均值为0．0016 mg／L，纳什效率系数为0．72。研究表明，基于NAR神经网络建立的预测模型，在养殖水体亚硝酸盐含量变化预测中具有很强的非线性动态描述能力，对养殖水体中亚硝酸盐的预测有较好的适应性和预测精度。
침대용전통궤리건모불능만족수체중아초산염농도변화예측적문제，인용비선성자결합적시간서렬망락，건립료기우NAR신경망락적양식수체아초산염예측모형。채용2014년6—10월양식당구검측적아초산염적수거건모，건립료용우양식수체아초산염모의적NAR신경망락，병이용2014년11월적관측수거대모형적모의능력진행료검험。결과현시，건립적양식수체아초산염예측모형，가이흔호지모의수체중아초산염농도적변화추세，모의적절대오차평균치위0．0016 mg／L，납십효솔계수위0．72。연구표명，기우NAR신경망락건립적예측모형，재양식수체아초산염함량변화예측중구유흔강적비선성동태묘술능력，대양식수체중아초산염적예측유교호적괄응성화예측정도。
In view that the traditional model cannot meet with the prediction of nitrite concentration variations in the water.In the present study, NAR artificial neural network model was developed to predict the variations of water nitrite in aquaculture pond.The nitrite content in the corresponding pond was chosen as output variable.The above data were collected everyday from June to October in 2014 which were used to develop model in this study, and the data collected in November of 2014 were chosen to evaluate the model.The results showed that the changing trend of water nitrite in aquaculture pond could be simulated well by the model, the predictive absolute error mean was 0.001 6 mg/L, and the efficiency of Nash-Sutcliffe coefficient was 0 .72 .The prediction model based on NAR neural network had a strong ability to describe the nonlinear dynamic variations of nitrite content in aquaculture water, and it showed the good adaptability and accuracy in practical application.