Pipeline failure prediction in water distribution networks using weather conditions as explanatory factors
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @Article{kakoudakis:2018:hydro,
-
author = "Konstantinos Kakoudakis and Raziyeh Farmani and
David Butler",
-
title = "Pipeline failure prediction in water distribution
networks using weather conditions as explanatory
factors",
-
journal = "Journal of Hydroinformatics",
-
year = "2018",
-
volume = "20",
-
number = "5",
-
pages = "1191--1200",
-
month = "1 " # sep,
-
keywords = "genetic algorithms, genetic programming, ANN, XAI,
EPR, artificial neural network, evolutionary polynomial
regression, pipe failure predictions,rehabilitation,
water distribution networks, weather factors",
-
ISSN = "1464-7141",
-
eprint = "https://iwaponline.com/jh/article-pdf/20/5/1191/657152/jh0201191.pdf",
-
URL = "https://doi.org/10.2166/hydro.2018.152",
-
DOI = "doi:10.2166/hydro.2018.152",
-
size = "10 pages",
-
abstract = "This paper examines the impact of weather conditions
on pipe failure in water distribution networks using
artificial neural network (ANN) and evolutionary
polynomial regression (EPR). A number of
weather-related factors over 4 consecutive days are the
input of the binary ANN model while the output is the
occurrence or not of at least a failure during the
following 2 days. The model is able to correctly
distinguish the majority 87 percent of the days with
failure(s). The EPR is employed to predict the annual
number of failures. Initially, the network is divided
into six clusters based on pipe diameter and age. The
last year of the monitoring period is used for testing
while the remaining years since the beginning are
retained for model development. An EPR model is
developed for each cluster based on the relevant
training data. The results indicate a strong
relationship between the annual number of failures and
frequency and intensity of low temperatures. The
outputs from the EPR models are used to calculate the
failures of the homogeneous groups within each cluster
proportionally to their length.",
- }
Genetic Programming entries for
Konstantinos Kakoudakis
Raziyeh Farmani
David Butler
Citations