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Explainable deep learning models for predicting water pipe failures
Ridwan Taiwo
,
Tarek Zayed
, Beenish Bakhtawar
, Bryan T. Adey
The Hong Kong Polytechnic University
Research output
:
Journal article publication
›
Journal article
›
Academic research
›
peer-review
19
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Citations (Scopus)
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Dive into the research topics of 'Explainable deep learning models for predicting water pipe failures'. Together they form a unique fingerprint.
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Engineering
Water Distribution
100%
Deep Learning Method
100%
Convolutional Neural Network
100%
Distribution Network
100%
Bayesian Optimization
66%
Model Prediction
33%
Pipe Diameter
33%
Base Model
33%
Predictive Ability
33%
Metrics
33%
Network Model
33%
Deep Neural Network
33%
Computer Science
Deep Learning Model
100%
Probability
100%
Convolutional Neural Network
75%
Distribution Network
75%
Predictive Model
50%
SHapley Additive exPlanation
50%
Neural Network Model
25%
Deep Neural Network
25%
Deep Learning Method
25%
Network Management
25%
Web Application
25%
Predictive Performance
25%
Model Prediction
25%
Leaning Parameter
25%
Matthews Correlation Coefficient
25%
Chemical Engineering
Water Distribution
100%
Deep Learning Method
100%
Neural Network
100%
Bayesian Optimization
66%
Deep Neural Network
33%
Correlation Coefficient
33%
Earth and Planetary Sciences
Probability Theory
100%
Deep Learning Model
100%
Water Distribution
75%
Bayesian Optimization
50%
SHapley Additive exPlanation
50%
Correlation Coefficient
25%
Hong Kong
25%
Material Science
Bayesian Optimization
100%
Keyphrases
TabNet Model
33%