A novel method for modeling effluent quality index using Bayesian belief network

被引:2
|
作者
Nezhad, M. Falah [1 ]
Abbasi, M. [2 ]
Markarian, S. [2 ]
机构
[1] Univ Tehran, Fac Environm, Tehran, Iran
[2] Shahid Beheshti Univ, Fac Civil Water & Environm Engn, Tehran, Iran
关键词
Bayesian networks; Effluent quality index; Reuse; Water quality; WASTE-WATER; SELECTION;
D O I
10.1007/s13762-018-2121-0
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Reliable estimation of the effluent quality from a municipal wastewater treatment plant is important for safe discharge and reuse of the treated stream as well as control and monitoring of treatment processes. The quality index is a summative index that can be used for a rapid assessment of water and treated wastewater to rank the quality level. Since there is no quality index for different reuse options of reclaimed wastewater, this study aims to propose a quality index for the treated wastewater focusing on reusing purpose. The significant quality parameters associated with EQI were found using the Delphi method and weighted by analytic hierarchy process decision-making tool. Finally, the Bayesian network analysis was employed to estimate the probability of meeting legal reuse and disposal requirements for EQI based on data collected from south wastewater treatment plant in Tehran city, Iran. The results of Bayesian network analysis were compared with the aggregation method as a widely used method for estimating quality indices. Results revealed Bayesian model had great potential for effluent quality index modeling and significantly increased the precision and the accuracy of estimating the EQI formula. The suggested methodology can provide valuable support also to such practice.
引用
收藏
页码:7071 / 7080
页数:10
相关论文
共 50 条
  • [21] Artificial neural network modeling of the effluent quality index for municipal wastewater treatment plants using quality variables: south of Tehran wastewater treatment plant
    Nezhad, Maliheh Falah
    Mehrdadi, Naser
    Torabian, Ali
    Behboudian, Sadegh
    JOURNAL OF WATER SUPPLY RESEARCH AND TECHNOLOGY-AQUA, 2016, 65 (01): : 18 - 27
  • [22] Fusing multimodal biometrics with quality estimates via a Bayesian belief network
    Maurer, Donald E.
    Baker, John P.
    PATTERN RECOGNITION, 2008, 41 (03) : 821 - 832
  • [23] A Bayesian belief network for quality assessment: application to employment officer support
    Wooff, DA
    Schneider, JM
    JOURNAL OF INTELLECTUAL DISABILITY RESEARCH, 2006, 50 : 109 - 126
  • [24] Semantic modeling of cyber threats in the energy sector using Dynamic Cognitive Maps and Bayesian Belief Network
    Gaskova, Daria A.
    Massel, Aleksei G.
    PROCEEDINGS OF THE 7TH SCIENTIFIC CONFERENCE ON INFORMATION TECHNOLOGIES FOR INTELLIGENT DECISION MAKING SUPPORT (ITIDS 2019), 2019, 166 : 326 - 329
  • [25] Modeling Attribution of Cyber Attacks Using Bayesian Belief Networks
    Sharma, Munish
    STRATEGIC ANALYSIS, 2021, 45 (01) : 18 - 37
  • [26] A Software Risk Analysis Model Using Bayesian Belief Network
    Yong Hu Juhua Chen Mei Liu Yang Yun Junbiao Tang Sun Yatsen UniversityGuangzhou ChinaUniversity of Kansas LawrenceUSAShunde Polytechnicfoshan China
    南昌工程学院学报, 2006, (02) : 102 - 106
  • [27] Performance evaluation of employees using Bayesian belief network model
    Kabir, Golam
    Sumi, Razia Sultana
    Sadiq, Rehan
    Tesfamariam, Solomon
    INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT, 2018, 13 (02) : 91 - 99
  • [28] Slip and fall event detection using Bayesian Belief Network
    Liao, Yi Ting
    Huang, Chung-Lin
    Hsu, Shih-Chung
    PATTERN RECOGNITION, 2012, 45 (01) : 24 - 32
  • [29] OPTIMIZING SURVIVAL AND QUALITY OF LIFE USING BAYESIAN NETWORK MODELING IN KIDNEY TRANSPLANTATION
    Zia, A.
    Jones, C. A.
    Weimersheimer, P.
    Mesa, O. A.
    VALUE IN HEALTH, 2017, 20 (05) : A307 - A308
  • [30] Diagnosing schizophrenia in the presence of uncertainty using a Bayesian belief network
    Dudgeon, P
    Mackinnon, AJ
    McGorry, PD
    SCHIZOPHRENIA RESEARCH, 1999, 36 (1-3) : 7 - 7