Naslov
Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network
(Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network)

Autori
Lučin, Ivana ; Grbčić, Luka ; Čarija, Zoran ; Kranjčević, Lado

Izvornik
Sensors (1424-8220) 21 (2021), 1; 245, 15

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
water distribution networks ; water network contamination ; machine learning ; random forest ; neural network

Sažetak
In the case of a contamination event in water distribution networks, several studies have considered different methods to determine contamination scenario information. It would be greatly beneficial to know the exact number of contaminant injection locations since some methods can only be applied in the case of a single injection location and others have greater efficiency. In this work, the Neural Network and Random Forest classifying algorithms are used to predict the number of contaminant injection locations. The prediction model is trained with data obtained from simulated contamination event scenarios with random injection starting time, duration, concentration value, and the number of injection locations which varies from 1 to 4. Classification is made to determine if single or multiple injection locations occurred, and to predict the exact number of injection locations. Data was obtained for two different benchmark networks, medium-sized network Net3 and large-sized Richmond network. Additionally, an investigation of sensor layouts, demand uncertainty, and fuzzy sensors on model accuracy is conducted. The proposed approach shows excellent accuracy in predicting if single or multiple contaminant injections in a water supply network occurred and good accuracy for the exact number of injection locations. View Full-Text

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Strojarstvo, Temeljne tehničke znanosti, Interdisciplinarne tehničke znanosti

POVEZANOST RADA