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作者针对远洋渔场渔情预报精度偏低的问题,提出一种基于空间自回归和空间聚类的渔情预报模型。该模型利用空间自回归对收集到的渔业历史数据进行预处理,然后通过空间聚类将所有数据样本根据地理位置分划成若干个区域,最后研究每个区域中环境数据与渔获数据之间的数学关系,各自建立栖息地适宜性指数模型(Habitat Suitability Index,HSI),并以印度洋大眼金枪鱼(Thunnus obesus)为例进行验证。结果表明,本模型的均方差为0.1742,与传统线性回归方法的均方差0.2363相比,能更好地表达海洋环境数据与渔获量之间的关系,预测精度显著提高。 相似文献
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In order to improve the forecasting ability of the fishery forecast model for the longline bigeye tuna (Thunnus obesus), we proposed a marine environment feature extraction method based on deep convolutional embedded clustering (DCEC), combined with generalized additive model (GAM) for forecasting the longline bigeye tuna fishing grounds in the Southwest Indian Ocean. We used the MODIS-Aqua and MODIS-Terra sea surface temperature (SST) three-level inversion image data (in days) from January to December in 2018 at 0.041 6°×0.041 6° to construct a DCEC model, determined the optimal number of clusters based on the Davies-Bouldi index (DBI), and extracted the category feature value (FM) of each month’s sea surface temperature (SST); we used monthly 1°×1° bigeye tuna longline fishery data from January to December in 2018 generated from the Indian Ocean Tuna Commission (IOTC), and calculated the catch per unit effort (CPUE); we matched the monthly category feature value FM and the monthly average value of Chl a concentration with the CPUE data to construct an improved GAM; we matched the monthly average SST, the monthly average Chl a concentration and CPUE data to build a basic GAM; we used the joint hypothesis test (F test) to verify the influence of model explanatory variables; we used akaike information criterion (AIC), mean square error (MSE), and draw the frequency distribution diagrams and box diagrams of measured and predicted values, etc., to analysis the improvement effect of the improved GAM compared to the basic GAM. The results showed that: (1) the category feature value (FM) extracted based on the DCEC model could better reflect the temporal and spatial dynamic characteristics of SST in the Southwest Indian Ocean, and was related with the climatic conditions, monsoon conditions, and hydrological characteristics in the Southwest Indian Ocean; (2) the factor interpretation of FM was higher than that of the monthly average SST in GAM, which means FM had more significant impact on the CPUE of bigeye tuna. The high catch rate was concentrated in the areas where the FM category was 2, 10, 24 with intersections between the warm and cold currents; (3) the AIC of the improved GAM was reduced by 9.17% than that of the basic GAM and MSE of the improved GAM was reduced by 26.7% than that of the basic GAM; the frequency distribution of the CPUE logarithmic value predicted by the improved GAM was closer to the normal distribution, and the high frequency distribution interval was closer to that of the measured value; the scatter plot showed that the CPUE predicted by the improved GAM had a significant correlation with the measured CPUE, with r equaled to 0.60. This study proves the effectiveness of the DCEC model in extracting marine environmental features, and can provide a reference for the further study on the bigeye tuna fishery forecast. 相似文献
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西北太平洋柔鱼中长期预测方法研究 总被引：3，自引：0，他引：3
为了能更好预测西北太平洋柔鱼的资源量, 选择合适的预测方法及开发相应的预测系统颇为重要。利用相关性分析, 筛选出在产卵区显著影响西北太平洋柔鱼资源量的关键网格点, 并采用这些网格点的海表温度、产卵区适宜温度所占面积的比例和单位努力捕获量等数据组织样本, 然后利用线性回归、BP 神经网络、RBF 神经网络和支持向量机等预测方法进行实验。结果表明: 在西北太平洋柔鱼中长期预测中, BP 神经网络要优于其他方法。以相关性分析和BP 神经网络为基础建立的西北太平洋柔鱼资源量预测系统是有效可行的。 相似文献
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为了解决远洋渔业中过度依赖经验而产生的盲目捕捞问题,结合海洋环境数据和历史产量数据对渔场进行有效分析,提出了一种基于径向基函数神经网络(Radial basis function neural network,RBFNN)的栖息地指数(HSI)预测方法,并将其应用于印度洋海域大眼金枪鱼(Thunnus obesus)栖息地指数的预测。在RBFNN训练过程中使用模糊C均值(Fuzzy c-means,FCM)聚类算法,在基于神经网络的规则提取过程中首次采用了和声搜索(Harmony search,HS)算法。实验研究表明,利用FCM改进后的RBFNN,均方误差(Mean square error,MSE)达到0.021 6。和声搜索由于算法简单,易于实现,能够应用于训练后的FCM-RBFNN提取分类规则,提取出的规则能够反映该渔业现状。 相似文献
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在海洋生态环境中, 食物链上层捕食者对下层鱼群的捕猎行为可以被普遍地观察到, 捕食者攻击的策略以及鱼群的防御机制是海洋捕猎行为的关键。本文构建了鱼群行为模型并对捕食者目标选择策略进行模拟, 3种策略分别是选择“最近”“最中心”和“最外围”的个体。在虚拟环境中模拟单个捕食者攻击鱼群这一过程, 对3种目标选择策略做比较分析, 以鱼群结构变化程度的3个关键参数做评判标准。首先发现采用选择“最外围”个体的策略对于中等规模鱼群造成的结构变化程度最大, 其次拓展到不同大小规模的鱼群, 此策略仍是最有效的, 其中小规模鱼群因应对风险能力低, 无法体现三种策略的区别。 相似文献
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