A machine learning approach for predicting and localizing the failure and damage point in sewer networks due to pipe properties

This study investigates the applicability of a support vector machine (SVM), a supervised machine learning (ML) algorithm, for the development of a prediction model to predict sewer pipe failures and the effects of manhole proximity. The results show that SVM with an accuracy of 84% can properly approximate the manhole effects on sewer pipe failures.PMID:38557566 | DOI:10.2166/wh.2024.249
Source: Journal of Water and Health - Category: Environmental Health Authors: Source Type: research