基于LSSA-XGBOOST改进算法的高体鰤鱼类体质量预测模型

Body mass of Seriola dumerili prediction model based on LSSA-XGBOOST improved algorithm

为构建利用体质量判断的精准投喂模型,需实时获取鱼群体质量状态,基于LSSA-XGBOOST算法,通过对工船养殖实测的高体鰤(Seriola dumerili)体长、体宽和体质量数据进行分析,构建以体长、体宽两项体态特征数据为输入、体质量数据为输出的高体鰤体质量预测模型。结果显示,与常规数学模型拟合相比,LSSA-XGBOOST模型拟合的相关性系数R^(2)提高约10%;与传统BP神经网络和粒子群优化BP相比,LSSA-XGBOOST模型误差平方和R^(2)提升约3%,这为构建基于体质量判断的高体鰤精准投喂模型提供了理论依据。

In order to build an accurate feeding model using mass judgment and obtain the body mass state of S.dumerili in real time,this study built a body mass prediction model based on LSSA-XGBOOST algorithm.Firstly,the data of body length,body width and body mass measured by the culture experiment ship were detected by extreme studentized deviate(ESD)method,and the abnormal points were removed.Secondly,the chaotic random number generator was used to complete the initial population optimization of the SSA algorithm to improve its searching ability.The optimized SSA algorithm was used to optimize three parameters of the optimal tree depth,the optimal learning rate and the optimal number of iterations of the XGBOOST model.Finally,the LSSA-XGBOOST model with body length and body width as the input and body mass data as the output was constructed.The experimental results showed that,compared with the conventional mathematical model fitting,the LSSA-XGBOOST model fitting correlation coefficient R^(2)increased by about 10%.Compared with the traditional BP neural network and PSO particle swarm optimization BP,the error square and R^(2)were improved by about 3%,and the mean absolute error(MAE),mean square error(MSE)as well as the root mean square error(RMSE)were significantly reduced.It could be seen that LSSA-XGBOOST model was more accurate for predicting the body mass of small samples of S.dumerili,and the construction of LSSA-XGBOOST model was greatly significant for users to grasp the growth state of S.dumerili and build accurate feeding model for mass judgment.