Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Network Interconnectivity Prediction from SCADA System Data: A Case Study in the Wastewater Industry
KTH, School of Industrial Engineering and Management (ITM).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Prediktion av Nätverkssammankoppling från Data Genererat av SCADA System : En fallstudie inom avloppsindustrin (Swedish)
Abstract [en]

Increased strain on incumbent wastewater distribution networks originating from population increases as well as climate change calls for enhanced resource utilization. Accurately being able to predict network interconnectivity is vital within the wastewater industry to enable operational management strategies that optimizes the performance of the wastewater system. In this thesis, an evaluation of the network interconnectivity prediction performance of two machine learning models, the multilayer perceptron (MLP) and the support vector machine (SVM), utilizing supervisory control and dataacquisition (SCADA) system data for a wastewater system is presented. Results of the thesis imply that the MLP achieves the best predictions of the network interconnectivity. The thesis concludes that the MLP is the superior model and that the highest achievable network interconnectivity accuracy is 56% which is attained by the MLP model.

Abstract [sv]

Den ökade påfrestningen på nuvarande avloppsnät till följd av befolkningstillväxt och klimatförändringar medför att det finns behov för optimerad resursförbrukning. Att korrekt kunna predicera ett avloppsnät är önskvärt då det möjliggör för effektivitetshöjande operativ förvaltning av avloppssystemet. I denna avhandling evalueras hur väl två maskininlärningsmodeller kan predicera nätverketssammankoppling med data från ett system för övervakning och kontroll av data (SCADA) genererat av ett avloppsnätverk. De två modellerna som testas är en multilagersperceptron (MLP) och en stödvektormaskin (SVM). Resultaten av avhandlingen visar på att MLP modellen uppnår den bästa prediktionen av nätverketssammankoppling. Avhandlingen konkluderar att MLP modellen är den bästa modellen för att predicera nätverkets sammankoppling samt att den högsta nåbara korrektheten var 56% vilket uppnåddes av MLP modellen.

Place, publisher, year, edition, pages
2019.
Keywords [en]
MLP, SVM, IoT, Binary Classification, Random Forest, Network Predicition, Wastewater Distrubtion Network, SCADA, Industry 4.0
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-255812OAI: oai:DiVA.org:kth-255812DiVA, id: diva2:1342062
External cooperation
Random Forest
Subject / course
Industrial Economics and Management
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Supervisors
Examiners
Available from: 2019-08-15 Created: 2019-08-12 Last updated: 2019-08-20Bibliographically approved

Open Access in DiVA

fulltext(7244 kB)15 downloads
File information
File name FULLTEXT02.pdfFile size 7244 kBChecksum SHA-512
2549a3f910b81a869c79fa22b7b626117286c3e229f5448de8be5c44ad56ae992a8b87eb626aa9aac8213e8e0f96252c9c46dcd33fc138225fc971794190ddf0
Type fulltextMimetype application/pdf

By organisation
School of Industrial Engineering and Management (ITM)
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 15 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 185 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf