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Title:Razvoj, ovrednotenje in primerjava odločitvenih modelov za napovedovanje delovanja čistilne naprave : diplomska naloga
Authors:ID Štemberger, Marko (Author)
ID Boshkoska, Biljana Mileva (Mentor) More about this mentor... New window
Files:.pdf VS_2023_Marko_Stemberger.pdf (3,71 MB)
MD5: 702B37B0CA0EC183CA0644384670C29A
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FIŠ - Faculty of Information Studies in Novo mesto
Abstract:V diplomski nalogi raziskujemo, kako lahko odločitvene modele v programu Orange uporabimo za čiščenje odpadne vode. Glavno vprašanje, ki nas zanima, je, kako dobro lahko ti modeli napovedujejo učinkovitost čistilnih naprav za odpadne vode, še posebej ko gre za razmerje med različnimi vhodnimi in izhodnimi parametri. Da bi to ugotovili, smo se lotili kombinacije empirične analize in strojnega učenja. Uporabili smo tri različne algoritme: Neural Networks (NN), Random Forest (RF) in Naivni Bayes (NB). Da bi še dodatno izboljšali naše modele, smo vključili tudi algoritem ReliefF, ki nam je pomagal izbrati tiste spremenljivke, ki najbolj vplivajo na naše rezultate. Glavni cilj naše raziskave je bil razjasniti, kako lahko odločitveni modeli pomagajo pri čiščenju odpadne vode. Končni cilji so bili jasni: ustvariti robustne odločitvene modele, preveriti, kako dobro delujejo, in ugotoviti, katere spremenljivke so ključne za uspešno čiščenje odpadne vode.
Keywords:strojno učenje, nevronske mreže, naključni gozd, naivni Bayes, algoritem ReliefF, odločitveni modeli, Orange
Place of publishing:Novo mesto
Place of performance:Novo mesto
Publisher:[M. Štemberger]
Year of publishing:2023
Year of performance:2023
Number of pages:XV, 83 str.
PID:20.500.12556/ReVIS-10135 New window
COBISS.SI-ID:174939907 New window
UDC:004.8(043.2)
Note:Na ov.: Diplomska naloga : visokošolskega strokovnega študijskega programa prve stopnje;
Publication date in ReVIS:06.12.2023
Views:964
Downloads:50
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Licences

License:CC BY-NC-SA 4.0, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Link:http://creativecommons.org/licenses/by-nc-sa/4.0/
Description:A Creative Commons license that bans commercial use and requires the user to release any modified works under this license.
Licensing start date:06.12.2023

Secondary language

Language:English
Abstract:In this thesis, we explore how decision models in Orange can be used for wastewater treatment. The main question we are interested in is how well these models can predict the performance of wastewater treatment plants, especially when it comes to the relationship between different input and output parameters. In order to find out, we have undertaken a combination of empirical analysis and machine learning. We used three different algorithms: Neural Networks (NN), Random Forest (RF) and Naive Bayes (NB). In order to further improve our models, we also included the ReliefF algorithm, which helped us to select those variables that have the most impact on our results.The main objective of our research was to clarify how decision models can help in wastewater treatment. The final objectives were to create robust decision models, to test how well they work, and to find out which variables are key to successful wastewater treatment.
Keywords:machine learning, neural networks, random forest, naive Bayes, ReliefF algorithm, decision models, Orange


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