TwoLeak Isolation in Water Distribution Networks Based on kNN and Linear Discriminant Classifiers
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sensor Placement
2.2. Leak Isolation Strategy
2.2.1. Dataset Generation
2.2.2. Leak Classification
2.2.3. kNN Classifier
 1.
 The training of the kNN classifier is an offline process. In this process, a set of residual samples corresponding to leaks of available classes given by (21) is stored and each residual is assigned to its class label. The dataset used to train the classifier is obtained by performing all possible leak scenarios according to the procedure described in Section 2.2.1.
 2.
 Leak class prediction is an online process. Here, a continuous comparison of the most recent residual is performed with the labeled residuals from the training dataset (21). If the leak class is denoted by ${C}_{p}$ according to (13), and $\mathrm{P}\left(\right)open="("\; close=")">{C}_{p}={C}_{{p}_{i}}\mathbf{r}$ is the probability that the leak location corresponds to the ${C}_{{p}_{i}}$ class given the residual $\mathbf{r}$, the kNN classifier assumes that$$\mathrm{P}\left(\right)open="("\; close=")">{C}_{p}={C}_{{p}_{i}}\mathbf{r}$$$${z}_{KNN}=arg\underset{i}{max}\phantom{\rule{0.166667em}{0ex}}\mathrm{P}\left(\right)open="("\; close=")">{C}_{p}={C}_{{p}_{i}}\mathbf{r}$$
2.2.4. Discriminant Analysis Classifier
 1.
 The training of the DA classifier is an offline process where a set of residual samples corresponding to all possible leakage scenarios are assigned to the corresponding class by means of (21), this stage being when the discriminant functions are generated. In the same way, the dataset to train this classifier is obtained by simulating the leakage scenarios according to the procedure described in Section 2.2.1.
 2.
 Leak class prediction is an online process. In this process, predictions are made using the actual residual and the predictive model obtained in the training stage. If the leak class is denoted by ${C}_{p}$ according to (13), then $\mathrm{P}\left(\right)open="("\; close=")">{C}_{p}={C}_{{p}_{i}}\mathbf{r}$ is the probability that the leak corresponds to the ${C}_{{p}_{i}}$ class given the residual $\mathbf{r}$, and the DA classifier computes$$\mathrm{P}\left(\right)open="("\; close=")">{C}_{p}={C}_{{p}_{i}}\mathbf{r}\mathrm{P}\left(\mathbf{r}\right)$$$$P\left(\right)open="("\; close=")">\mathbf{r}{C}_{p}={C}_{{p}_{i}}$$The class with the highest probability is chosen as the output of the classifier:$${Z}_{DA}=arg\underset{i}{max}\phantom{\rule{0.166667em}{0ex}}\mathrm{P}\left(\right)open="("\; close=")">{C}_{p}={C}_{{p}_{i}}\mathbf{r}$$
3. Results
3.1. Hanoi WDN Case Study
3.1.1. Leak Scenario $\mathcal{A}$
3.1.2. Leak Scenario $\mathcal{B}$
3.1.3. Leak Scenario $\mathcal{C}$
3.1.4. Relaxation Node Analysis
3.2. Madrid’s DMA Case Study
Relaxation Node Analysis
3.3. Discussion
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AI  Artificial Intelligence 
WDN  Water Distribution Network 
OECD  Organization for Economic Cooperation and Development 
FIR  Finite Impulse Response 
RBF  Radial Base Function 
DA  Discriminant Analysis 
kNN  k Nearest Neighbors 
h  Hour 
L/s  Liters per second 
${f}_{R}$  Leak flow rate used for estimation of residuals 
${f}_{S}$  Leak flow rate used for estimation of sensitivities 
$\mathbb{R}$  Set of real numbers 
diag  Diagonal matrix 
max  Maximum Value 
arg max  Maximum argument 
Appendix A. SensorPlacementMethodologyBased Algorithm
Algorithm A1: Sensorplacementmethodologybased algorithm. 

Appendix B. DatasetGenerationMethodologyBased Algorithm
Algorithm A2: Datasetgenerationmethodologybased algorithm 

Appendix C. LeakLocalizationStrategyBased Algorithm
Algorithm A3: Leaklocalizationstrategybased algorithm 

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Sensor’s Number  Optimal Placement 

2 sensors  12, 21 
3 sensors  12, 15, 21 
Zone  Node Set 

${z}_{1}$  1, 2, 3 
${z}_{2}$  4, 5, 6 
${z}_{3}$  7, 8, 9 
${z}_{4}$  10, 11, 12 
${z}_{5}$  13, 14 
${z}_{6}$  16, 17, 18 
${z}_{7}$  19, 20, 21 
${z}_{8}$  22, 23, 24 
${z}_{9}$  15, 25, 26 
${z}_{10}$  27, 28 
${z}_{11}$  29, 30, 31 
Relaxation Nodes  Hanoi WDN  Madrid DMA  

kNN  DA  kNN  DA  
${\mathit{N}}_{\mathit{h}}$= 1  ${\mathit{N}}_{\mathit{h}}$= 24  ${\mathit{N}}_{\mathit{h}}$= 1  ${\mathit{N}}_{\mathit{h}}$= 24  ${\mathit{N}}_{\mathit{h}}$= 1  ${\mathit{N}}_{\mathit{h}}$= 24  ${\mathit{N}}_{\mathit{h}}$= 1  ${\mathit{N}}_{\mathit{h}}$= 24  
0  25.5%  48.0%  53.5%  70.0%  23.5%  36.0%  65.0%  77.0% 
1  40.5%  73.5%  65.0%  82.5%  29.0%  46.0%  75.5%  85.5% 
2  64.0%  82.5%  81.0%  90.5%  35.0%  53.5%  79.0%  89.5% 
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RodríguezArgote, C.A.; BegovichMendoza, O.; NavarroDíaz, A.; SantosRuiz, I.; Puig, V.; DelgadoAguiñaga, J.A. TwoLeak Isolation in Water Distribution Networks Based on kNN and Linear Discriminant Classifiers. Water 2023, 15, 3090. https://doi.org/10.3390/w15173090
RodríguezArgote CA, BegovichMendoza O, NavarroDíaz A, SantosRuiz I, Puig V, DelgadoAguiñaga JA. TwoLeak Isolation in Water Distribution Networks Based on kNN and Linear Discriminant Classifiers. Water. 2023; 15(17):3090. https://doi.org/10.3390/w15173090
Chicago/Turabian StyleRodríguezArgote, Carlos Andrés, Ofelia BegovichMendoza, Adrián NavarroDíaz, Ildeberto SantosRuiz, Vicenç Puig, and Jorge Alejandro DelgadoAguiñaga. 2023. "TwoLeak Isolation in Water Distribution Networks Based on kNN and Linear Discriminant Classifiers" Water 15, no. 17: 3090. https://doi.org/10.3390/w15173090