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Flow Data, Inflow/Infiltration Ratio, and Autoregressive Error Models
Authors:Z Zhang
Affiliation:Associate Professor, Dept. of Mathematics and Statistics, Univ. of North Carolina at Charlotte, Charlotte, NC 28223. E-mail: zzhang@uncc.edu
Abstract: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.
Keywords:Inflow  Infiltration  Sanitary sewers  Statistical models  Auto-regressive models  Errors  
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