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master's thesis
Analiza gubitka vode u vodooprskbnoj mreži
2021. urn:nbn:hr:190:663402
Marvin, Gabriel
University of Rijeka
Faculty of Engineering
Department of Fluid Mechanics and Computational Engineering
Section of Fluid Mechanics and Hydraulic Machines

Cite this document

Marvin, G. (2021). Analiza gubitka vode u vodooprskbnoj mreži (Master's thesis). Rijeka: University of Rijeka, Faculty of Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:190:663402

Marvin, Gabriel. "Analiza gubitka vode u vodooprskbnoj mreži." Master's thesis, University of Rijeka, Faculty of Engineering, 2021. https://urn.nsk.hr/urn:nbn:hr:190:663402

Marvin, Gabriel. "Analiza gubitka vode u vodooprskbnoj mreži." Master's thesis, University of Rijeka, Faculty of Engineering, 2021. https://urn.nsk.hr/urn:nbn:hr:190:663402

Marvin, G. (2021). 'Analiza gubitka vode u vodooprskbnoj mreži', Master's thesis, University of Rijeka, Faculty of Engineering, accessed 17 December 2024, https://urn.nsk.hr/urn:nbn:hr:190:663402

Marvin G. Analiza gubitka vode u vodooprskbnoj mreži [Master's thesis]. Rijeka: University of Rijeka, Faculty of Engineering; 2021 [cited 2024 December 17] Available at: https://urn.nsk.hr/urn:nbn:hr:190:663402

G. Marvin, "Analiza gubitka vode u vodooprskbnoj mreži", Master's thesis, University of Rijeka, Faculty of Engineering, Rijeka, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:190:663402

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Metadata
TitleAnaliza gubitka vode u vodooprskbnoj mreži
Title (english)Water Distribution Network Leak Detection
AuthorGabriel Marvin
MentorLado Kranjčević (mentor)
MentorSiniša Družeta (komentor)
Committee memberNeven Lovrin (predsjednik povjerenstva)
Committee memberLado Kranjčević (član povjerenstva)
Committee memberMladen Perinić (član povjerenstva)
Committee memberTomislav Senčić (član povjerenstva)
GranterUniversity of Rijeka
Faculty of Engineering
(Department of Fluid Mechanics and Computational Engineering)
(Section of Fluid Mechanics and Hydraulic Machines)
Rijeka
Defense date and country2021-11-25, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mechanical Engineering
Abstract
Tema rada bila je treniranje klasifikacijskog i nekoliko regresijskih modela koji će rješavati razne probleme. Prikupljen je veliki broj simulacija na superračunalu BURA kojima se treniranju modeli te su prikazani njihovi rezultati s obzirom na broj ulaznih podataka. Klasifikacijski modeli trenirani su sa svrhom preciznog lokaliziranja mjesta curenja dok su regresijski modeli predviđali vrijednosti površine, koeficijenta i vremena početka curenja. S obzirom na slične rezultate uspoređena su dva klasifikacijska algoritma te su njihovi rezultati detaljno opisani. Također istrenirani su regresijski modeli te su njihovi rezultati prikazani pomoću grafova ovisnosti koeficijenta R2, srednje kvadratne pogreške i broja simulacija na kojemu je izvršena križna validacija. Podaci kojima su se trenirali modeli prikupljeni su za mrežu Net3 koja je standardna EPANET mreža i mreži Network 1 iz rada [28]. Za Network 1 mrežu prikupljeni su podaci za dvije raspodjele senzora po mreži.
Abstract (english)
The goal of this thesis was to train a classification and several regression models to solve a number of problems. A large amount of data in form of simulations was collected on BURA supercomputer that was used to train the models. The classification models were trained with the purpose to precisely localize the occuring leak while the regression models were trained to predict the value of the area, discharge coefficient and strating time of the leak. In the context of classification we obtained very similar results between two algorithms so the results for both of them are presented with attention to detail. The results obtained by the regression models via cross validation are presented in terms of R2 coefficient and value of the mean squared error with regards to the number of simulations used for training. The data is split in to three sets that were obtained by runinng a very large number of simulations on the EPANET Net3 network and on the Network 1 from [28]. Two sets of data were collected for Network 1 with two different sensor placements.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:190:663402
Study programmeTitle: Mechanical Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka strojarstva (magistar/magistra inženjer/inženjerka strojarstva)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2021-12-09 14:22:03