合肥工业大学校徽

合肥工业大学学报自科版

合肥工业大学学报(自然科学版)

JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY (NATURAL SCIENCE)

主管：中华人民共和国教育部

主办：合肥工业大学

ISSN 1003-5060, CN 34-1083/N

基于改进 GM $ (1,1) $ 模型的生活用水量预测

Prediction of domestic water consumption based on improved GM $ (1,1) $ model

期刊信息

合肥工业大学（自然科学版），2024年3月，第47卷第3期：387-391，416

DOI: 10.3969/j.issn.1003-5060.2024.03.016

作者信息

高华昆，陶月赞，杨杰

（合肥工业大学土木与水利工程学院，安徽合肥230009）

摘要和关键词

摘要: 生活用水量预测是城市给水规划的关键,其核心是提高预测的精准度。由于传统$\mathrm{GM}(1,1)$模型误差主要来源于背景值和初始值,文章采取引入幂函数改进背景值和初始值2种改进方法。引入幂函数改进背景值权重构造,使新数据占改进模型主导地位;引入幂函数减少原始数据振荡,优化原始序列。将改进后的2种模型应用于河南省生活用水量预测中,并与传统$\mathrm{GM}(1,1)$模型进行比较。结果表明改进模型各个检验均满足要求,可进行中长期用水量预测,预测可得2025年河南省生活用水量为$48.31\times10^{8}\mathrm{~m}^{3}$。优化原始值改进的$\mathrm{GM}(1,1)$模型预测效果好、精度高,可为当地水资源保护、管理提供参考。

关键词: 优化原始值；优化背景值；改进 GM(1,1) 模型；用水量预测

Authors

GAO Huakun, TAO Yuezan, YANG Jie

(School of Civil and Hydraulic Engineering, Hefei University of Technology, Hefei 230009, China)

Abstract and Keywords

Abstract: The prediction of domestic water consumption is the key to the urban water supply planning, and the core of it is to improve the accuracy of prediction. Since the error of the traditional GM(1,1) model mainly comes from the background value and the initial value, two improvement methods are adopted: introducing the power function to improve the background value weight structure, making the improved model dominated by new data; introducing the power function to reduce the oscillation of the original data, enhancing the adaptability of the original sequence. The two models are applied to the prediction of domestic water consumption in Henan Province and compared with the traditional GM(1,1) model. The results show that all tests of the improved model satisfy the requirements, and the medium-and long-term water consumption can be predicted. The domestic water consumption of Henan Province in 2025 is predicted to be $ 48.31 \times 10^{8} $ m $ ^{3} $. The improved GM(1,1) model with optimized original value has good prediction effect and high accuracy, and can provide reference for local water resources protection and management.

Keywords: optimized original value; optimized background value; improved GM(1,1) model; water consumption prediction

基金信息

安徽高校自然科学研究重点资助项目（KJ2017A409）

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