Sensor placement for classifier-based leak localization in water distribution networks using hybrid feature selection

This paper presents a sensor placement approach for classifier-based leak localization in water distribution networks. The proposed method is based on a hybrid feature selection algorithm that combines the use of a filter based on relevancy and redundancy with a wrapper based on genetic algorithms. This algorithm is applied to data generated by hydraulic simulation of the considered water distribution network and it determines the optimal location of a prespecified number of pressure sensors to be used by a leak localization method based on pressure models and classifiers proposed in previous works by the authors. The method is applied to a small-size simplified network (Hanoi) to better analyze its computational performance and to a medium-size network (Limassol) to demonstrate its applicability to larger real-size networks.Peer ReviewedPostprint (author's final draft

A survey of machine learning methods applied to anomaly detection on drinking-water quality data

Abstract: Traditional machine learning (ML) techniques such as support vector machine, logistic regression, and artificial neural network have been applied most frequently in water quality anomaly detection tasks. This paper presents a review of progress and advances made in detecting anomalies in water quality data using ML techniques. The review encompasses both traditional ML and deep learning (DL) approaches. Our findings indicate that: 1) Generally, DL approaches outperform traditional ML techniques in terms of feature learning accuracy and fewer false positive rates. However, is difficult to make a fair comparison between studies because of different datasets, models and parameters employed. 2) We notice that despite advances made and the advantages of the extreme learning machine (ELM), application of ELM is sparsely exploited in this domain. This study also proposes a hybrid DL-ELM framework as a possible solution that could be investigated further and used to detect anomalies in water quality data