[1]蔡惠民*,曹扬,陶政坪,等.污水流量与天气数据融合的贝叶斯服务人口预测[J].计算机技术与发展,2024,34(08):181-188.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0111]
　CAI Hui-min*,CAO Yang,TAO Zheng-ping,et al.Bayesian Service Population Prediction Based on Sewage Flow and Weather Data Fusion[J].,2024,34(08):181-188.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0111]
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污水流量与天气数据融合的贝叶斯服务人口预测

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年08期
页码:
181-188
栏目:
新型计算应用系统
出版日期:
2024-08-10

文章信息/Info

Title:
Bayesian Service Population Prediction Based on Sewage Flow and Weather Data Fusion
文章编号:
1673-629X(2024)08-0181-08
作者:
蔡惠民1; 2*; 曹扬1; 2; 陶政坪1; 2; 谢真强1; 2; 3
1. 中电科大数据研究院有限公司,贵州 贵阳 550022; 2. 提升政府治理能力大数据应用技术国家工程研究中心,贵州 贵阳 550022; 3. 天津大学 智能与计算学部,天津 300354
Author(s):
CAI Hui-min1; 2*; CAO Yang1; 2; TAO Zheng-ping1; 2; XIE Zhen-qiang1; 2; 3
1. CETC Big Data Research Institute Co. ,Ltd. ,Guizhou 550022,China; 2. National Engineering Research Center of Big Data Application to Improvement of Governance Capacity,Guizhou 550022,China; 3. School of Intelligence and Computing,Tianjin University,Tia
关键词:
污水监测; 多源数据融合; 服务人口预测; 贝叶斯分析; 随机变分推理
Keywords:
sewage monitoring; multi-source data fusion; service population prediction; Bayesian analysis; stochastic variational inference
分类号:
TP391
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0111
摘要:
传统基于污水日均流量及人均用水量的人口预测模型缺乏对天气因素的考虑,存在人口数量测算偏大等问题。 为了综合考虑天气因素对污水日均流量的影响,提出了一种基于污水监测数据与天气数据融合的贝叶斯服务人口预测模型。 通过引入天气影响因子,同质化、异质化天气影响因子转化率,天气因素对污水日均流量的贡献量等,构建基于贝叶斯方法的污水日均流量生成模型。 基于随机变分推理,获得生成模型参数的后验分布,进而实现各污水处理厂服务区域的服务人口预测模型。 该模型能抵消区域天气因素的综合影响水平,能更合理地实现污水厂服务区域的人口数量预测。同时,通过统计分析对比了同质化、异质化天气影响因子转化率估计,天气因素对污水日均流量的影响等。 该服务人口预 测模型能进一步支撑城市人口的态势感知,对提升社会治理能力有重要意义。
Abstract:
The traditional population prediction model based on the daily average sewage flow and per capita water consumption lacks consideration of weather factors,and there are problems such as overestimation of population size. In order to comprehensively consider the influence of weather factors on the daily average sewage flow, a Bayesian service population prediction model based on sewage monitoring data and weather data fusion is proposed. By introducing weather influence factors, the homogeneous and heterogeneous conversion rates of weather influence factors,and the contribution of weather factors to the daily average sewage flow,a Bayesian method based generative model is constructed. Based on stochastic variational inference,posterior distributions of the generative model parameters are obtained,and a service population prediction model for the service area of each sewage treatment plant is implemented. This model can offset the comprehensive influence level of regional weather factors and more reasonably predict the population of the sewage treatment plant service area. At the same time, statistical analysis was conducted to compare the estimation of homogeneous and heterogeneous conversion rates of weather impact factors,as well as the influence of weather factors on the daily average sewage flow rate. This service population prediction model can further support the perception of urban population trends and is of great significance for improving social governance capabilities.

相似文献/References:

[1]齐华[],李晓[],刘军[],等. 面向污水监控系统的自适应加权数据融合算法[J].计算机技术与发展,2015,25(04):221.
　QI Hua[],LI Xiao[],LIU Jun[],et al. Adaptive Weighted Data Fusion Algorithm Faced to Wastewater Monitoring System[J].,2015,25(08):221.

更新日期/Last Update: 2024-08-10