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Journals / Journal of Physical Chemistry and Functional Materials / 2024 / Volume: 7 Issue: 2

Machine Learning Methods in IoT Based Embedded Systems for Classifying Physical Faults in Water Distribution Networks

İrfan Kılıç(FIRAT ÜNİVERSİTESİ)0000-0001-5079-2825
Orhan Yaman(FIRAT UNIVERSITY)0000-0001-9623-2284
Şeyma Saylan(FIRAT UNIVERSITY)0009-0003-2581-4414
İlayda Hörgüşlüoğlu(FIRAT UNIVERSITY)0009-0009-4839-6394
Betül Demirelli(FIRAT UNIVERSITY)0009-0000-1663-5885
Pages
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Publication Date
2024-12-18
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results obtained are close to the state-of-the-art results.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Water is the most important factor for the survival of living things on Earth.
📊
Key Findings & Data:Although 70% of the Earth is water, the amount of drinkable water is approximately 0.3%.
💡
Conclusion & Impact:The results obtained are close to the state-of-the-art results.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Water pipe faults · IoT-based embedded systems · ESP

Abstract

Water is the most important factor for the survival of living things on Earth. Although 70% of the Earth is water, the amount of drinkable water is approximately 0.3%. Therefore, creating a sustainable water policy and carrying out studies are very important for our world and our future. Most of the potable water resources are physical losses. In the evaluations made based on metropolitan municipalities, it was seen that the water loss rate was approximately 50%. The study aims to find water pipe faults using IoT (Internet of Things) based machine learning classifiers to prevent physical losses in water distribution networks. Within the scope of this study, an experimental environment was created and an IMU (Inertial Measurement Unit) sensor was fixed on plastic pipes of different diameters and lengths. Vibration data collected in different scenarios (pressure, etc. factors) were transferred to the ThingSpeak platform over the internet. The transferred data could be monitored in real-time on a server. Physical damage in the pipes was detected using signal pre-processing, feature extraction, and feature selection algorithms on vibration data. In the study, damages were classified using machine learning-based classification (Decision Trees, k-Nearest Neighbors, Linear Discriminant, Support Vector Machines) methods to predict the type of damage (solid, hole, multi-hole). The data set revealed within the scope of the study is thought to lead to scientific studies in this field. The results obtained are close to the state-of-the-art results.

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Cite This Article

Kılıç, İ., Yaman, O., Saylan, Ş., Hörgüşlüoğlu, İ., Demirelli, B. (2024). Machine Learning Methods in IoT Based Embedded Systems for Classifying Physical Faults in Water Distribution Networks. Journal of Physical Chemistry and Functional Materials, Volume: 7 Issue: 2. https://doi.org/10.54565/jphcfum.1588037

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