Flow data, inflow/infiltration ratio, and autoregressive error models

被引:16
|
作者
Zhang, Z [1 ]
机构
[1] Univ N Carolina, Dept Math & Stat, Charlotte, NC 28223 USA
关键词
Auto-regressive models; Errors; Infiltration; Inflow; Sanitary sewers; Statistical models;
D O I
10.1061/(ASCE)0733-9372(2005)131:3(343)
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Sanitary sewer overflows (SSOs) are a major environmental issue. One of the major factors causing SSOs is the rain-derived inflow and infiltration (RDII) to a separate sanitary sewer system. If a wastewater collection system is not well maintained, cumulative system-wide RDII could easily cause the wastewater conveyance and treatment capacity to be overwhelmed, and thus lead to SSOs. Monitoring system condition is a key component in system management. The industry's standard approaches to system monitoring include the practice of collecting and analyzing continuous rainfall and flow data at certain key locations in the system to estimate the level of RDII. However, the writer is of the opinion that the current standard analytical methodologies of the industry can be significantly improved. This paper introduces a basic regression approach with autoregressive errors to support statistical inferences with respect to the level of RDII.
引用
收藏
页码:343 / 349
页数:7
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