海洋科学2024，Vol.48Issue(3)：50-63,14.DOI:10.11759/hykx20231016004
基于灰色多元变权组合预测模型对山东省海水养殖产量预测
Prediction of mariculture production in Shandong Province using a gray multiple variable weight combination prediction model
摘要
Abstract
To further improve the accuracy of seawater aquaculture yield prediction and consider the impact of multiple factors on seawater aquaculture yield,this study tested a variable weight combination prediction model by fully combining the advantages of the conventional statistical prediction models,such as the Long Short-Term Memory(LSTM)neural network prediction model,the GM(1,N)prediction model,and the partial least squares regression prediction model,and by constructing a gray multiple variable weight combination prediction model.As a result,the total and classified yields of seawater aquaculture in the Shandong province were predicted.The empirical results indicated that the prediction accuracy of the gray multiple variable weight combination prediction model for seawater aquaculture production in the Shandong province is as high as 99.13%,which is remarkably higher than those of various single-item models,such as the LSTM neural network.This model also integrated the advantages of the LSTM neural network,GM(1,N)prediction model,and partial least squares regression prediction model,thereby making up for the shortcomings of the individual single-item models and improving the prediction accuracy and reliability.Based on the predicted results,total production of marine aquaculture products in the Shandong province will maintain good development at least until 2025,reaching 5.7928 million tons at an average growth rate of 3.11%.The production of fish,crustaceans,shellfish,algae,and sea cucumber in marine aquaculture is estimated to reach 62 700 tons,262 700 tons,4.458 3 million tons,686 500 tons,and 95 700 tons,respectively.关键词
海水养殖/LSTM神经网络/变权组合预测Key words
mariculture/LSTM neural network/variable weight combination prediction分类
农业科技引用本文复制引用
王艳明,郭云水,王锐..基于灰色多元变权组合预测模型对山东省海水养殖产量预测[J].海洋科学,2024,48(3):50-63,14.基金项目
国家社会科学基金重点项目(21ATJ006) National Social Science Found Key Project,No.21ATJ006 （21ATJ006）
