摘要
棉花产量预测研究对提升农业生产效率、保障农民经济利益以及促进纺织行业和国家粮棉安全具有重要意义。本文提出了一种基于鲸鱼优化算法(WOA)和双向长短期记忆网络(BiLSTM)的棉花产量预测模型,旨在优化BiLSTM模型的关键参数,以提高预测的准确性和稳定性。WOA算法通过模拟鲸鱼觅食行为,能够有效地搜索参数空间,优化BiLSTM的隐藏层单元数和学习率等超参数,从而提升预测性能。本文利用1980—2024年全国棉花产量数据进行实证分析,结果表明,WOA-BiLSTM模型相比传统的主观参数选择BiLSTM模型,具有更小的预测误差和更高的预测精度。该结果不仅验证了WOA算法在BiLSTM参数优化中的优势,也表明该模型能够准确反映棉花产量的变化规律,对棉花生产和相关行业的决策提供了有力支持。通过该模型的应用,能够更有效地应对棉花生产中的不确定性,帮助政府、农民和相关企业在棉花产量预测、政策制定和市场调控中作出更加科学和合理的决策。
Cotton yield prediction research is of great significance to improve agricultural production efficiency,protect farmers’economic interests as well as promote the textile industry and national grain and cotton security.In this paper,a cotton yield prediction model based on Whale Optimization Algorithm and Bidirectional Long Short-Term Memory Network is proposed,aiming to optimize the key parameters of the BiLSTM model in order to improve the accuracy and stability of the prediction.By simulating the whale’s foraging behaviour,the WOA algorithm is able to search the parameter space efficiently,and to optimize the number of hidden-layer units of the BiLSTM and the learning rate of the superparameters,thus improving the prediction performance.In this paper,using the national cotton yield data from 1980 to 2024 for empirical analysis,the results show that the WOA-BiLSTM model has a smaller prediction error and higher prediction accuracy than the traditional subjective parameter selection BiLSTM model.The results not only verify the advantages of the WOA algorithm in BiLSTM parameter optimisation,but also show that the model can accurately reflect the changing law of cotton production,which provides a strong support for decisionmaking in cotton production and related industries.Through the application of the model,it can more effectively deal with the uncertainty in cotton production,and help the government,farmers and related enterprises to make more scientific and reasonable decisions in cotton yield prediction,policy making and market regulation.
作者
黄胜龙
袁宏俊
胡凌云
HUANG Shenglong;YUAN Hongjun;HU Lingyun(College of Statistics and Applied Mathematics,Anhui University of Finance and Economics,Bengbu,Anhui 233030,China;College of Management Science and Engineering,Anhui University of Finance and Economics,Bengbu,Anhui 233030,China)
出处
《中国纤检》
2025年第8期101-106,共6页
China Fiber Inspection
基金
安徽省哲学社会科学规划项目(AHSKY2020D42)
安徽省高校自然科学重点项目(2024AH050003,2022AH050602)
安徽财经大学教学研究重点项目(acjyzd2023023)。
关键词
棉花产量
鲸鱼优化算法
双向长短期记忆网络
预测
cotton yield
whale optimisation algorithm
bi-directional long and short-term memory networks
prediction
