Developing Decision Tree Models to Create a Predictive Blockage Likelihood Model for Real-World Wastewater Networks

被引:9
|
作者
Bailey, James [1 ,2 ]
Harris, Emma [1 ]
Keedwell, Edward [2 ]
Djordjevic, Slobodan [2 ]
Kapelan, Zoran [2 ]
机构
[1] DCWW, Pentwyn Rd, Treharris CF46 6LY, Mid Glamorgan, Wales
[2] Univ Exeter, Coll Engn Math & Phys Sci, Exeter, Devon, England
关键词
Sewer; Wastewater; Blockage; Likelihood; Model; Decision Trees; Ensemble;
D O I
10.1016/j.proeng.2016.07.433
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
To reduce the blockages occurring on wastewater networks, reducing costs, customer and environmental impact, greater levels of proactive maintenance are being conducted by water and sewerage companies. For effective prioritisation of this maintenance, an accurate model of blockage likelihood is required. This paper presents the development of a model, for provision of a blockage likelihood level and verification using unseen data, based on previous decision tree models constructed using the asset and historical incident data from the wastewater network of Dwr Cymru Welsh Water. The model has been developed here using the geographical grouping of sewers and the application of ensemble techniques, with the results illustrating the potential benefits which can be derived from these techniques. (C) 2016 Published by Elsevier Ltd.
引用
收藏
页码:1209 / 1216
页数:8
相关论文
共 50 条
  • [1] Predictive risk modelling of real-world wastewater network incidents
    Bailey, James
    Keedwell, Edward
    Djordjevic, Slobodan
    Kapelan, Zoran
    Burton, Chris
    Harris, Emma
    [J]. COMPUTING AND CONTROL FOR THE WATER INDUSTRY (CCWI2015): SHARING THE BEST PRACTICE IN WATER MANAGEMENT, 2015, 119 : 1288 - 1298
  • [2] Tree decompositions of real-world networks from simulated annealing
    Klemm, Konstantin
    [J]. JOURNAL OF PHYSICS-COMPLEXITY, 2020, 1 (03):
  • [3] Developing predictive precision medicine models by exploiting real-world data using machine learning methods
    Theocharopoulos, Panagiotis C.
    Bersimis, Sotiris
    Georgakopoulos, Spiros V.
    Karaminas, Antonis
    Tasoulis, Sotiris K.
    Plagianakos, Vassilis P.
    [J]. JOURNAL OF APPLIED STATISTICS, 2024,
  • [4] Complex systems: Analysis and models of real-world networks
    Latora, V
    Crucitti, P
    Marchiori, M
    Rapisarda, A
    [J]. ENERGY AND INFORMATION TRANSFER BIOLOGICAL SYSTEMS, PROCEEDINGS: HOW PHYSICS COULD ENRICH BIOLOGICAL UNDERSTANDING, 2003, : 188 - 204
  • [5] Multiplicative Attribute Graph Model of Real-World Networks
    Kim, Myunghwan
    Leskovec, Jure
    [J]. ALGORITHMS AND MODELS FOR THE WEB GRAPH, 2010, 6516 : 62 - 73
  • [6] Metric Tree-Like Structures in Real-World Networks: An Empirical Study
    Abu-Ata, Muad
    Dragan, Feodor F.
    [J]. NETWORKS, 2016, 67 (01) : 49 - 68
  • [7] Advanced Predictive Model and Real-World Results for Medium Concentration CPV
    Karney, Bruce
    Finot, Marc
    [J]. 7TH INTERNATIONAL CONFERENCE ON CONCENTRATING PHOTOVOLTAIC SYSTEMS (CPV-7), 2011, 1407
  • [8] Efficiency of attack strategies on complex model and real-world networks
    Bellingeri, Michele
    Cassi, Davide
    Vincenzi, Simone
    [J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2014, 414 : 174 - 180
  • [10] Temporal Networks Based on Human Mobility Models: A Comparative Analysis With Real-World Networks
    Mboup, Djibril
    Diallo, Cherif
    Cherifi, Hocine
    [J]. IEEE ACCESS, 2022, 10 : 5912 - 5935