├── Visualizations
├── x.png
├── Model Block.PNG
├── feature map.png
├── Flow vs. Leak.PNG
├── Loss Function.PNG
├── Model Measures.PNG
├── Vib vs. Flow.PNG
├── temp vs. Leak.PNG
├── Confusion Matrix.PNG
├── Feature Extraction.PNG
├── Model Architecture.PNG
├── Pressure vs. Flow.PNG
├── Pressure vs. Leak.PNG
├── Vibration vs. Leak.PNG
└── Pressure vs. Vibration.PNG
├── Report - Leakage Detection in Smart water distribution systems - Neelabh & Pavni.pdf
└── README.md
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/Report - Leakage Detection in Smart water distribution systems - Neelabh & Pavni.pdf:
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/README.md:
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1 | # Leakage-Detection-in-Smart-Water-Distribution-Systems
2 |
3 | 
4 | 
5 | 
6 | 
7 |
8 | This project was done in CSIR-CEERI, Pilani in the Cyber Physical Systems department under the mentorship of Principal Scientist [Dr. Bhausaheb Ashoke Botre](https://www.ceeri.res.in/profiles/bhausaheb-ashok-botre/). This repository contains the coding aspect of the project, where the collected data is used for training an ANN model to detect leakage.
9 |
10 | Data - Data was collected experimentally from an IOT-based Smart Water Grid testbed and thus can't be shared publically.
11 |
12 | Team Members - Pavni Jaiswal, Neelabh Sinha
13 |
14 | ### Elements of the Project -
15 |
16 | - A major chunk of project involved the study of Leakage Detection techniques, and then, conducting experiments on a physical testbed (existing in CSIR-CEERI, Pilani) which has a pipeline network with all relevant sensors installed at various places, which mimics the arrangement of a typical water distribution network. PLC and SCADA system is installed for control and data collection.
17 |
18 | - Thereafter, data was collected by conducting experiments by simulating various types of leak and studying the behaviour of the system.
19 |
20 | - Further, the data was carefully analysed to study the dependence of various parameters at various nodes on a leak, and study of how these parameters are interdependent. Further, deciding features were extracted which were to be used as inputs for the classification.
21 |
22 | - An ANN based model was trained to after carefully deciding the features (which were temperature, pressure, flow rate and vibration at different nodes of the network) which classifies a state of operation as leak or not a leak.
23 |
24 | ### Included Files with the repository -
25 | - Data : A portion of used Data to observe the nature
26 | - Analysis_and_Model.ipynb : Analysis of Data, Training the model, Testing the model
27 |
28 | ### Feature Dependency -
29 |
30 | Only the % dependency of leak on various parameters are shown here. Individual Data Analysis graphs and visualizations can be seen from [Visualizations](https://github.com/neelabhsinha/Leakage-Detection-in-Smart-Water-Distribution-Systems/tree/master/Visualizations) Folder.
31 |
32 |
33 |
34 | ### Model -
35 |
36 |
37 |
38 |
39 | ### Results -
40 |
41 | Metric | Value (%)
42 | -------|----------
43 | Accuracy | 92%
44 | Precision | 92.5%
45 | Recall | 90.7%
46 | F1-score | 91.4%
47 |
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