基于被动水声信号的淡水鱼混合数量预测

Mixed quantities prediction of freshwater fish based on passive underwater acoustic signals

针对淡水鱼数量评估问题,通过水听器和声学记录仪采集鳊(Parabramis pekinensis)和鲫(Carassius auratus)在相同比例、不同混合数量下的水声信号,提取54个特征参数,进行相关性分析,挑选与淡水鱼混合数量显著相关的特征参数,采用Rank-RS法进行样本划分,建立多元线性回归模型,并与偏最小二乘回归模型的预测效果进行比较。结果显示,平均Mel频率倒谱系数与淡水鱼混合数量的相关性整体上最显著,多元线性回归模型的拟合效果较好,预测模型R^2为0.950,RPD为4.492,说明所建立的模型适用于淡水鱼混合数量预测,将被动水声技术应用于淡水鱼数量研究具有一定的可行性。

The quantities prediction is an important part of fishery resource assessment and aquaculture.Traditional active sonar and large-scale fishing gear trials and other methods have certain defects.For the quantitative assessment of freshwater fish,the hydroacoustic signals of bream and crucian carp in the same proportion and different mixed quantities were collected by hydrophone and acoustic recorder.54 characteristic parameters were extracted and used for correlation analysis.The characteristic parameters significantly correlated with the mixed quantities of freshwater fish were selected.The Rank-RS method was used to divide the samples.The multiple linear regression model was established and compared with the prediction effect of the partial least squares regression model.The results showed that the correlation between the average Mel frequency cepstrum coefficient and mixed quantity of freshwater fish was the most significant on the whole.The fitting effect of the multiple linear regression model was better.The prediction model R^2 and the RPD was 0.950 and 4.492,indicating that the established model was suitable for predicting the mixed numbers of freshwater fish.It is feasible to apply passive underwater acoustic technology in studying quantities of freshwater fish.