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基于GRA&BPNN的广西粮食产量预测研究 预览 被引量:4

PREDICTING GRAIN YIELD OF GUANGXI PROVINCE BASED ON GRA&BPNN
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摘要 [目的]研究粮食产量的影响因素并以其相关性为基础预测粮食产量对实现广西粮食产业的“做强做优”具有重大意义。通常情况下粮食产量与种植技术发展水平、农田耕地面积、土地肥力、气候等诸多因素相关,但是在样本数据不足、数据间关联度不明显的情况下则无法采用回归分析、灰系统等常用预测方法。[方法]文章应用灰色关联分析方法得到水库水量、农田有效灌溉面积、第一产业从业人口、播种面积、除涝面积等5个与广西粮食产量关系最为密切的因子变量,同时取2004~2012年的数据作为学习、训练样本,以2013~2014年的数据为试报样本,并以此建立BP神经网络粮食预测模型。[结果]检验结果表明运用本模型预测粮食产量具有较高的精度和良好的泛化性。[结论]根据模型结果,该文提出提升广西粮食产业发展的可行性建议,即加强水库的管理、引导与粮食产业相关的产业、稳定粮食种植面积、加强洪涝灾害的防御和治理能力、推进农业信息化。 It plays a great significance for Guangxi grain industry to be ying the relevant factors of the grain yield and predicting yield based on is relevant to the development level of planting technology, the amount getting stronger and more excellent by stud- its correlation. Generally, grain production of agricultural cultivated land acreage, soil fertility, climate and other factors, the general methods of regression analysis and grey system can hardly be used for the prediction at the background of limitations of sample size and correlational data. This paper got 5 variables which were the most close related to the Guangxi grain yield, including the amount of reservoir water, effective irri- gation area, working population of the first industry, planting area, and drainage area of waterlogged elimination u- sing grey relational analysis (GRA) method, and established BP neural networks (BPNN) prediction model of grain yield based onthe data in 2004 -2012 as training samples and the data in 2013 ~2014 as test report samples. The results showed that the model using in the prediction of grain yield had high precision an last, it put forward the feasible proposals in the interest of promotion and development of d good generalization. At grain industry in Guangxi based on the model results, including strengthening the management of the reservoir, guiding the relevant industries of the grain industry, keeping the amount of grain acreage, improving the defensing and processing abilities of flood disaster, and promoting agricultural information technology.
作者 戎陆庆 陈飞 欧阳浩 Rong Luqing1 , Chen Fei1 ,Ouyang Hao2 ( 1. Management School, Guangxi University of Science and Technology, Liuzhou 545006, China; 2. Computer School, Guangxi University of Science and Technology, Liuzhou 545006, China)
出处 《中国农业资源与区划》 CSCD 北大核心 2017年第2期105-111,共7页 Journal of China Agricultural Resources and Regional Planning
基金 国家社科基金项目“新型城镇化背景下西部地区城市少数民族民生问题研究”(15XMZ078) 广西哲社规划课题项目“广西先进制造业服务化技术支持体系研究”(15DGL001)
关键词 粮食产量 预测 灰色关联分析 BP神经网络 可行性建议 广西 grain yield prediction GRA BPNN feasible proposals Guangxi province
作者简介 戎陆庆(1982-),男,重庆人,硕士、实验师。研究方向:系统优化与决策分析、农产品物流。Email:rongluqing@tom.com
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