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    Flow Data, Inflow/Infiltration Ratio, and Autoregressive Error Models

    Source: Journal of Environmental Engineering:;2005:;Volume ( 131 ):;issue: 003
    Author:
    Z. Zhang
    DOI: 10.1061/(ASCE)0733-9372(2005)131:3(343)
    Publisher: American Society of Civil Engineers
    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.
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      Flow Data, Inflow/Infiltration Ratio, and Autoregressive Error Models

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    • Journal of Environmental Engineering

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    contributor authorZ. Zhang
    date accessioned2017-05-08T21:48:19Z
    date available2017-05-08T21:48:19Z
    date copyrightMarch 2005
    date issued2005
    identifier other%28asce%290733-9372%282005%29131%3A3%28343%29.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/62853
    description abstractSanitary 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.
    publisherAmerican Society of Civil Engineers
    titleFlow Data, Inflow/Infiltration Ratio, and Autoregressive Error Models
    typeJournal Paper
    journal volume131
    journal issue3
    journal titleJournal of Environmental Engineering
    identifier doi10.1061/(ASCE)0733-9372(2005)131:3(343)
    treeJournal of Environmental Engineering:;2005:;Volume ( 131 ):;issue: 003
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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