文章摘要
杜迎欣, 曹小兵, 李 琛, 王玉恒, 马廉洁.基于遗传算法的城市用水量灰色预测模型研究——以秦皇岛市引青济秦工程为例Journal of Water Resources and Water Engineering[J].,2014,25(3):129-132
基于遗传算法的城市用水量灰色预测模型研究——以秦皇岛市引青济秦工程为例
Research on model of gray prediction of urban water demand based on genetic algorithm:taking project of water diversion from Qinglong River to Qinhuangdao for example
  
DOI:10.11705/j.issn.1672-643X.2014.03.026
中文关键词: 城市用水  灰色系统  遗传算法  用水量预测  引青济秦
英文关键词: urban water demand  gray system  genetic algorithm  prediction of water demand  water diversion from Qinglong River to Qinhuangdao
基金项目:河北省高校科技项目(Z2013017)
Author NameAffiliation
DU Yingxin1, CAO Xiaobing2, LI Chen2, WANG Yuheng2, MA Lianjie2 (1.秦皇岛市引青管理局, 秦皇岛 066305
2.东北大学秦皇岛分校 控制工程学院, 秦皇岛 066004) 
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中文摘要:
      通过数据分析,结合传统灰色GM(1,1)模型的特点,基于遗传算法与新陈代谢思想提出了改进的GM(1,1,λ)模型。结果表明:GM(1,1)模型对分散数据预测精度较低,其精度等级为四级以下,最大相对误差大于45%,预测值逐年上升,与实际情况不符。而改进的GM(1,1,λ)模型的精度等级为三级,最大相对误差为18.716%,更好地反映了城市用水量的变化趋势,与观测数据最为接近,预测精度较高。
英文摘要:
      Through data analysis and combined with the characteristics of traditional gray GM(1,1) model, the paper presented improved GM(1,1,λ) model based on genetic algorithm and metabolism. The results indicated that prediction accuracy of GM(1,1) model is lower for scattered data, its accuracy level is under the forth and the maximum relative error is 45%. The predictive value is increased year by year,which is inconsistent with the actual situation. The prediction accuracy level of improved GM(1,1,λ) model is the third and the maximum relative error is 18.716%,which better reflects the change trend of urban water demand and is most close to the observation data,and the prediction accuracy is higher.
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