Prediction of pipe failures in water supply networks using logistic regression and support vector classification

Robles-Velasco, Alicia; Cortés, Pablo; Muñuzuri, Jesús; Onieva, Luis

doi:10.1016/j.ress.2019.106754

Cited by 125 publications

(46 citation statements)

References 35 publications

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“…Common kernels are the polynomial, hyperbolic tangent, and radial basis functions. The SVM model is presented in Equations (11)-(13) [22]. learning, the ML algorithm receives the inputs and intends to converge to the best classifier.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…Common kernels are the polynomial, hyperbolic tangent, and radial basis functions. The SVM model is presented in Equations (11)-(13) [22].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…Common kernels are the polynomial, hyperbolic ions. The SVM model is presented in Equations (11)-(13) [22]. (11) the constrains:…”

Section: Machine Learning Modelsmentioning

confidence: 99%

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Comparison of Statistical and Machine Learning Models for Pipe Failure Modeling in Water Distribution Networks

Giraldo-González

Rodríguez

2020

Water

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The application of statistical and Machine Learning models plays a critical role in planning and decision support processes for efficient and reliable Water Distribution Network (WDN) management. Failure models can provide valuable information for prioritizing system rehabilitation even in data scarcity scenarios, such as developing countries. Few studies have analyzed the performance of more than two models, and examples of case studies in developing countries are insufficient. This study compares various statistical and Machine Learning models to provide useful information to practitioners for the selection of a suitable pipe failure model according to information availability and network characteristics. Three statistical models (i.e., Linear, Poisson, and Evolutionary Polynomial Regressions) were used for failure prediction in groups of pipes. Machine Learning approaches, particularly Gradient-Boosted Tree (GBT), Bayes, Support Vector Machines and Artificial Neuronal Networks (ANNs), were compared in predicting individual pipe failure rates. The proposed approach was applied to a WDN in Bogotá (Colombia). The statistical models showed an acceptable performance (R2 between 0.695 and 0.927), but the Poisson Regression was the most suitable for predicting failures in pipes with lower failure rates. Regarding Machine Learning models, Bayes and ANNs exhibited low performance in the prediction of pipe failure condition. The GBT approach had the best performing classifier.

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Section: Machine Learning Modelsmentioning

confidence: 99%

“…Common kernels are the polynomial, hyperbolic tangent, and radial basis functions. The SVM model is presented in Equations (11)-(13) [22].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

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Comparison of Statistical and Machine Learning Models for Pipe Failure Modeling in Water Distribution Networks

Giraldo-González

Rodríguez

2020

Water

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“…Support vector machines can be used for regression (SVR) and classification (SVC) purposes. This method maps the explanatory variables through non-linear structures into a high dimensional space and then, the hyperplane that optimally adjusts to the data or separates the classes is generated (Kutyłowska, 2018;Robles-Velasco, Cortés, Muñuzuri & Onieva, 2020;Shirzad et al, 2014). Both ANNs and SVMs are informally known as 'black-box' systems.…”

Section: Models: Characteristics and Applicationsmentioning

confidence: 99%

Trends and applications of machine learning in water supply networks management

Robles-Velasco

Muñuzuri

Onieva

et al. 2021

JIEM

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Purpose: This study describes the trends and applications of machine learning systems in the management of water supply networks. Machine learning is a field in constant development, and it has a great potential and capability to attain improvements in real industries. The recent tendency of data storage by companies that manage the water supply networks have created a range of possibilities to apply machine learning. One particular case is the prediction of pipe failures based on historical data, which can help to optimally plan the renovation and maintenance tasks. The objective of this work is to define the stages and main characteristics of machine learning systems, focusing on supervised learning methods. Additionally, singularities that are usually found in data from water supply networks are highlighted.Design/methodology/approach: For this purpose, eight studies which contain real cases from around the world are discussed. From the data processing to the model validation, a tour of the methods used in each study is carried out. Moreover, the trendiest models are briefly defined together with the mechanisms that best suit their performance.Findings: As a result of the study, it was found that the imbalanced class problem is typical of data from water supply networks where only a small percentage of pipes fail. Consequently, it is recommended to use sampling methods to train classifiers, however, it is not necessary if we are training a regression system. Additionally, scaling and transformation of variables has generally a positive impact on the model’s performance. Currently, cross-validation is almost a requirement to obtain reliable and representative results. This technique is employed in all revised studies to train and validate their models.Originality/value: The use of machine learning systems to predict pipe failures in water supply networks is still a developing field. This study tries to define the advantages and disadvantages of different methods to process data from water supply networks, as well as to train and validate the models.

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“…The models worked similarly in terms of identified predictors. Then [9] used logistic regression and the carrier vector classification to predict a probability of failure associated with each sample of pipelines in water supply systems. The results obtained show that the logistic regression (LR) works slightly better than the classification of carrier vectors, and indicates that the number of unexpected failures could be considerably reduced.…”

Section: Related Workmentioning

confidence: 99%

Electrocoagulation Based Chromium Removal Efficiency Classification Using Logistic Regression

et al. 2020

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Surface treatment and tanning industries use huge quantities of heavy metals—especially Chromium (III) and (VI)—in their processes thanks to its physical proprieties. It is used in the composition of special steels and refractory alloys. By dint of using this metal, an enormous quantity of rejects is produced each year and discharged into the oceans. As this is very dangerous for our environment, it is very important to treat these discharges before getting rid of them. This study treats chromium removal as a special type of heavy metals that can be a component of industrial discharges. Electrocoagulation is considered among the best methods used in this kind of treatment. However, it requires a lot of time, energy and remains expensive. This paper presents a predictive model in order to classify the chromium removal efficiency using electrocoagulation method. The proposed model is a logistic regression (LR) that consumes four parameters that we call predictors: pH, time, current, and stirring speed. After the training and validation process, we obtained 88% as classification precision, recall and F-Score metrics values while the use of the 10-Folds cross-validation method gave a minimal area under curve (AUC) value of 97% while the best value attempts 100%. Classification report states that the model performs well comparing to similar experimentation efficiencies.

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Prediction of pipe failures in water supply networks using logistic regression and support vector classification

Cited by 125 publications

References 35 publications

Comparison of Statistical and Machine Learning Models for Pipe Failure Modeling in Water Distribution Networks

Comparison of Statistical and Machine Learning Models for Pipe Failure Modeling in Water Distribution Networks

Trends and applications of machine learning in water supply networks management

Electrocoagulation Based Chromium Removal Efficiency Classification Using Logistic Regression

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