BP神经网络模型作为一种常用的机器学习方法，被广泛应用于物种分布模型，以解析生物分布与环境因子的关系。与传统回归模型相比，该模型可以灵活处理变量间的非线性关系，但其结构复杂，在参数设置方面存在不确定性，从而影响模型的预测与应用。根据2016—2017年山东近海口虾蛄渔业资源调查与环境数据，利用BP神经网络模型构建口虾蛄资源分布模型，同时利用数据分组处理算法(group method of data handling, GMDH)、遗传算法(genetic algorithm, GA)和自适应算法(adaptive algorithm)分别对模型输入变量、初始权值和隐节点数目3方面进行优化，构建7种不同组合优化模型。结果显示，7种模型的优化效果存在明显差异，单方面和两方面组合优化模型预测性能基本保持一致；而三方面共同优化其均方根误差与残差平方和分别为0.35和1.94，较初始模型的0.52和2.40更小，且相关系数最大为0.45，表明模型优化效果最好。对比优化前后发现，口虾蛄资源密度随纬度和底层盐度变化趋势基本保持一致，而随底层温度的升高，口虾蛄资源密度存在较大差异。此外，最优模型较初始模型增加水深为关键环境因子，对口虾蛄的资源密度具有重要影响。本研究进一步开发了BP神经网络模型参数优化的方法，证明了参数优化对BP模型的预测性能具有重要影响，模型优化对于分析口虾蛄资源密度与环境因子的关系具有重要意义。

As a common machine learning method, BP neural network model is widely used in species distribution models to analyze the relationship between biological distribution and environmental factors. Compared with the traditional regression models, this model can flexibly deal with the nonlinear relationship between variables. However, there are substantial uncertainties in parameter setting as a result of its complex structure, which may affect the prediction and application of this model. This study considered approaches to optimize the model parameters, including the group method of data handling, genetic algorithm and adaptive algorithm, to improve initial weights and the number of hidden nodes of the model, respectively. Seven combinations of optimized BP models were implemented based on the survey data obtained from fishery resources and environment in Shandong offshore between 2016 and 2017. Our results showed that there were significant differences in the predictive performance of the seven optimization models. The predictive performance of the one-way and two-way optimization models was approximately the same. The root mean square error and the square of residual error were 0.35 and 1.94 respectively, which were smaller than the initial model's 0.52 and 2.40, and the maximum correlation coefficient was 0.45, indicating that the optimization effect of the model was the best. After the comparison and optimization, it was found that the resource density of Oratosquilla oratoria was basically different with the increase of bottom salinity while the resource density of O. oratoria was significantly different with the increase of bottom temperature. In addition, the increase of water depth in the optimal model compared with the initial model was a key environmental factor,which had an important effect on the resource density of O. oratoria. In this study, the parameter optimization method of the BP neural network model was further developed, which proved that the parameter optimization had important effect on the prediction performance of the BP model, and the model optimization was of great significance for the analysis of the relationship between resource density and environmental factors.

S932.5⁺1；TP183

国家重点研发计划(2018YFD0900906)；国家自然科学基金(31802301)