Water distribution pipe lifespans: Predicting when to repair the pipes in municipal water distribution networks using machine learning techniques DOI Creative Commons
Nacer Farajzadeh, Nima Sadeghzadeh,

Nastaran Jokar

и другие.

PLOS Water, Год журнала: 2024, Номер 3(1), С. e0000164 - e0000164

Опубликована: Янв. 9, 2024

Water is one of the essential matters that keeps living species alive; yet, lifespan pipes has two direct impacts on wasting water in very great amounts: pipe leakages and bursts. Consequently, proper detection aged distribution networks always been an issue overcoming problem. This makes monitoring important duty municipalities. Traditionally, bursts were only detected visually or through reports local areas, leading municipalities to change old pipes. Although this helps fix issue, a more desired way perhaps let officials know about possibilities such problems advance by predicting which are aged, so they can prevent wastage. Therefore, automate process, study, we take initial steps predict needing repair particular area using machine learning methods. We first obtain private dataset provided municipality Saveh, Iran outlines damaged previously. then train three algorithms whether set prone damage. To achieve this, One-Class (OC) Classification methods as OC-SVM, Isolation Forest, Elliptic Envelope used achieved highest accuracy 0.909. study value since it requires zero additional devices (i.e., sensors).

Язык: Английский

SPS Vision Net: Measuring Sensory Processing Sensitivity via an Artificial Neural Network DOI
Nima Sadeghzadeh, Nacer Farajzadeh, Novia Dattatri

и другие.

Cognitive Computation, Год журнала: 2023, Номер 16(3), С. 1379 - 1392

Опубликована: Ноя. 4, 2023

Язык: Английский

Процитировано

3
Water distribution pipe lifespans: Predicting when to repair the pipes in municipal water distribution networks using machine learning techniques DOI Creative Commons
Nacer Farajzadeh, Nima Sadeghzadeh,

Nastaran Jokar

и другие.

PLOS Water, Год журнала: 2024, Номер 3(1), С. e0000164 - e0000164

Опубликована: Янв. 9, 2024

Water is one of the essential matters that keeps living species alive; yet, lifespan pipes has two direct impacts on wasting water in very great amounts: pipe leakages and bursts. Consequently, proper detection aged distribution networks always been an issue overcoming problem. This makes monitoring important duty municipalities. Traditionally, bursts were only detected visually or through reports local areas, leading municipalities to change old pipes. Although this helps fix issue, a more desired way perhaps let officials know about possibilities such problems advance by predicting which are aged, so they can prevent wastage. Therefore, automate process, study, we take initial steps predict needing repair particular area using machine learning methods. We first obtain private dataset provided municipality Saveh, Iran outlines damaged previously. then train three algorithms whether set prone damage. To achieve this, One-Class (OC) Classification methods as OC-SVM, Isolation Forest, Elliptic Envelope used achieved highest accuracy 0.909. study value since it requires zero additional devices (i.e., sensors).

Язык: Английский

Процитировано

0