[1]龙志伟.肖松毅,王晖,周新宇,等.基于粒子群算法的水资源需求预测[J].郑州大学学报(工学版),2019,40(04):32-35.[doi:10.13705/j.issn.1671-6833.2019.04.005]
　Long Zhiwei,Xiao Songyi,Wang Hui,et al.Water resource demand forecasting based on particle swarm optimization[J].Journal of Zhengzhou University (Engineering Science),2019,40(04):32-35.[doi:10.13705/j.issn.1671-6833.2019.04.005]
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基于粒子群算法的水资源需求预测()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40
期数:
2019年04期
页码:
32-35
栏目:
出版日期:
2019-07-10

文章信息/Info

Title:
Water resource demand forecasting based on particle swarm optimization
作者:
龙志伟.肖松毅; 王晖; 周新宇;李 伟
1. 南昌工程学院瑶湖学院;2. 南昌工程学院江西省水信息协同感知与智能处理重点实验室;3. 江西师范大学计算机信息工程学院;4. 江西理工大学信息工程学院
Author(s):
Long Zhiwei 1; Xiao Songyi 2; Wang Hui 2; Zhou Xinyu 3; Li Wei 4
1. Yaohu College, Nanchang Institute of Technology; 2. Jiangxi Provincial Key Laboratory of Water Information Collaborative Perception and Intelligent Processing, Nanchang Institute of Technology; 3. School of Computer Information Engineering, Jiangxi Normal University; 4. School of Information Engineering, Jiangxi University of Science and Technology
关键词:
群智能" 粒子群算法" 水资源需求" 预测" 优化
Keywords:
swarm intelligence; Particle swarm algorithm; water needs; predict; optimization
分类号:
TP18
DOI:
10.13705/j.issn.1671-6833.2019.04.005
文献标志码:
A
摘要:
针对南昌市未来水资源需求预测问题!提出了基于粒子群算法的水资源需求预测方法+以南昌市历史人口&经济和水量需求数据为基础!构造了线性&指数和混合预测模型!利用粒子群算法对预测模型进行优化以确定模型参数+仿真实验结果表明!种模型都能获得较好的预测精度!其中混合预测模型效果最好!预测精度达到97.71%
Abstract:
Aiming at the problem of predicting the future demand of water resources in Nanchang, a water resource demand forecasting method based on particle swarm optimization algorithm is proposed. Based on the historical population, economy and water demand data of Nanchang City, linear, exponential and mixed forecasting models are constructed. The algorithm optimizes the prediction model to determine the model parameters. The simulation experiment results show that all three models can obtain good prediction accuracy, and the hybrid prediction model is the best, with a prediction accuracy of 97.71%.
更新日期/Last Update: 2019-07-29