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
相关论文
共 50 条
  • [41] Learning deep autoregressive models for hierarchical data
    Andersson, Carl R.
    Wahlstrom, Niklas
    Schon, Thomas B.
    IFAC PAPERSONLINE, 2021, 54 (07): : 529 - 534
  • [42] MULTILEVEL AUTOREGRESSIVE MODELS FOR LONGITUDINAL DYADIC DATA
    Gistelinck, Fien
    Loeys, Tom
    TPM-TESTING PSYCHOMETRICS METHODOLOGY IN APPLIED PSYCHOLOGY, 2020, 27 (03) : 433 - 452
  • [43] Data Poisoning Attacks against Autoregressive Models
    Alfeld, Scott
    Zhu, Xiaojin
    Barford, Paul
    THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2016, : 1452 - 1458
  • [44] Periodic autoregressive models in flow regulating reservoirs design
    Coser, M. C.
    Mendes, M. S.
    Reis, J. A. T.
    Mendonca, A. S. F.
    JOURNAL OF APPLIED WATER ENGINEERING AND RESEARCH, 2022, 10 (04): : 278 - 295
  • [45] Asymptotic normality of error density estimator in stationary and explosive autoregressive models
    Wu, Shi-peng
    Yang, Wen-zhi
    Gao, Min
    Hu, Shu-he
    APPLIED MATHEMATICS-A JOURNAL OF CHINESE UNIVERSITIES SERIES B, 2024, 39 (01) : 140 - 158
  • [46] Censored Nonparametric Time-Series Analysis with Autoregressive Error Models
    Dursun Aydin
    Ersin Yilmaz
    Computational Economics, 2021, 58 : 169 - 202
  • [47] Law of the iterated logarithm for error density estimators in nonlinear autoregressive models
    Liu, Tianze
    Zhang, Yong
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2020, 49 (05) : 1082 - 1098
  • [48] Monitoring changes in the error distribution of autoregressive models based on Fourier methods
    Zdeněk Hlávka
    Marie Hušková
    Claudia Kirch
    Simos G. Meintanis
    TEST, 2012, 21 : 605 - 634
  • [49] Asymptotic normality of error density estimator in stationary and explosive autoregressive models
    WU Shi-peng
    YANG Wen-zhi
    GAO Min
    HU Shu-he
    Applied Mathematics:A Journal of Chinese Universities, 2024, 39 (01) : 140 - 158
  • [50] A double bootstrap method to analyze linear models with autoregressive error terms
    McKnight, SD
    McKean, JW
    Huitema, BE
    PSYCHOLOGICAL METHODS, 2000, 5 (01) : 87 - 101