SSA-BP Neural Network Model for Predicting Rice-Fish Production in China

IF 0.7 4区 农林科学 Q4 FISHERIES
Junlei Wang, Guorui Zeng, Maosen Xu, Xuanchen Wan, Keke Wang, Jiegang Mou, Chenchen Hua, Chuanhao Fan, Pengfei Han
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Abstract

The rice-fish system has gained significant interest in recent years because of its effective usage of limited land and freshwater resources. To scientifically guide the improvement of rice field fishery production, the data in this study were selected from the latest China Fishery Statistical Yearbook, and therefore the development of paddy aquaculture was investigated. In order to more precisely predict the production of rice-fish in China, this paper introduces an artificial neural network with the SSA-BP model, which solves the drawbacks of the BP neural network such as easy to fall into local optimum and slow convergence speed when it is used for prediction. Firstly, the SSA-BP model incorporates the aquaculture area (split by water area), the national freshwater fish seedling output, the national end-of-year ownership of inland fishing vessels, the number of freshwater fisheries practitioners as input variables, and the production of rice-fish as an output variable; secondly, the SSA optimization algorithm was used to find the optimal initial thresholds and weights for the BP neural network, and finally the SSA-BP prediction model was constructed. The results revealed that the overall expansion of the rice field fishery was swift in the last five years, and the output of cultivated fish in China’s rice fields rose by nearly 20% yearly in the past five years. Compared with the BP neural network and GA-BP models, the accuracy of the SSA-BP prediction model was enhanced by 61.01% and 16.15%, respectively, which was more suited for predicting the production of rice-fish.

Abstract Image

预测中国稻鱼产量的 SSA-BP 神经网络模型
近年来,稻田养鱼因其能有效利用有限的土地和淡水资源而备受关注。为了科学地指导稻田渔业生产的改进,本研究的数据选自最新的《中国渔业统计年鉴》,因此调查了稻田水产养殖的发展情况。为了更准确地预测我国稻田养鱼产量,本文引入了 SSA-BP 模型的人工神经网络,解决了 BP 神经网络在预测时容易陷入局部最优、收敛速度慢等缺点。首先,SSA-BP 模型将水产养殖面积(按水域面积划分)、全国淡水鱼苗种产量、全国内陆渔船年末拥有量、淡水渔业从业人员数量作为输入变量,将稻田养鱼产量作为输出变量;其次,利用 SSA 优化算法为 BP 神经网络寻找最佳初始阈值和权重,最后构建了 SSA-BP 预测模型。结果表明,近五年来稻田渔业总体发展迅速,我国稻田养鱼产量近五年年均增长近 20%。与 BP 神经网络和 GA-BP 模型相比,SSA-BP 预测模型的准确率分别提高了 61.01% 和 16.15%,更适合预测稻田养鱼的产量。
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来源期刊
Journal of Applied Ichthyology
Journal of Applied Ichthyology 生物-海洋与淡水生物学
CiteScore
2.30
自引率
11.10%
发文量
73
审稿时长
3-8 weeks
期刊介绍: The Journal of Applied Ichthyology publishes articles of international repute on ichthyology, aquaculture, and marine fisheries; ichthyopathology and ichthyoimmunology; environmental toxicology using fishes as test organisms; basic research on fishery management; and aspects of integrated coastal zone management in relation to fisheries and aquaculture. Emphasis is placed on the application of scientific research findings, while special consideration is given to ichthyological problems occurring in developing countries. Article formats include original articles, review articles, short communications and technical reports.
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