# A Water Supply Pipeline Risk Analysis Methodology Based on DIY and Hierarchical Fuzzy Inference

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## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. Proposed Methodology

_{1}), length (X

_{2}), age (X

_{3}) and leakage (X

_{4}), and the output is the risk of the WS pipeline. The proposed model has three layers (input, middle, output). The input, middle and output layers have consisted of input parameters (X

_{1,}X

_{2}, X

_{3}and X

_{4)}, two sub fuzzy logic modules (M

_{1}_FL and M

_{2}_FL) and one out fuzzy logic module (M

_{3}_FL), respectively. The proposed model has been applied to real data provided by the Electronics and Telecommunications Research Institute (ETRI) organization working on the underground project. The data have been gathered from 1989 to 2010 for water supply pipelines installed at different points in Seoul, South Korea. Only for the above-discussed parameters, the data have been collected. The traditional and proposed fuzzy logic models can be seen in Figure 1 and Figure 2, respectively.

_{1}, A

_{2}, A

_{3}and A

_{n}, respectively.

_{1}_FL, M

_{2}_FL and M

_{3}_FL, have been used in the proposed model. Two inputs parameters, X1 and X2, have been used as inputs to the M

_{1}_FL, and we have defined seven and five membership functions for X1 and X2, respectively. The output of the M1_FL is partial risk 1 (PR1), for which we have defined five membership functions. Hence, 7 × 5 = 35, the number of feasible rules that can be defined for M

_{1}_FL.

_{2}_FL; we have defined seven and five membership functions for input variables X3 and X4. respectively. The M

_{2}_FL has one output variable partial risk 2 (PR2), for which we have defined five membership functions. The number of feasible rules that can be defined for M

_{2}_FL is 5 × 7 = 35. Further, PR1 and PR2 are used as inputs to the M

_{3}_FL; five membership functions are defined for each input variable. M

_{3}_FL has one output variable, namely WSPRI, for which five membership functions have been defined. Hence, 5 × 5 = 25 feasible rules which can be defined for M

_{3}_FL. Therefore, adding the total number of rules of each sub fuzzy logic of the proposed model are 95 to implement the full structure fuzzy logic model.

_{1}) of M

_{1}_FL, seven MFs have been specified. The labels assigned to these MFs of variable depth are ENG, NG, N, ND, D, DR and DT. The ST, S, M, L and LT are specified for the input variable Length (X

_{2}) of M

_{1}_FL. The linguistic terms VLR, LR, MR, HR and VHR have been defined for M

_{1}_FL. Similarly, for input leakage (X

_{3}) of the input variable M

_{2}_FL, the labels ELLP, VLLP, LLP, ILP, HLP, VHLP and EHLP have been assigned to MFs. Similarly, VO, O, IA, N and BN have been specified for input variable age (X

_{4}). For output variables of M

_{1}_FL and M

_{2}_FL, the labels VLR, LR, IR, HR and VHR are assigned to MFs, while these labels, VLLR, LLR, MLR, HLR and VHLR are assigned to the MFs of each variable of M

_{3}_FL module. The details of these abbreviations can be seen in the Table 1. In Table 2, Table 3 and Table 4, rules have been defined for M

_{1}_FL, M

_{2}_FL and M

_{3}_FL, respectively.

## 4. Implementation, Results and Discussion

#### 4.1. Implementation

_{1}_FL are shown in Figure 5 of the M

_{1}_FL.

_{1}_FL and M

_{2}_FL modules are used as inputs to the M

_{3}_FL modules, hence identical MFs as defined for input variables in M

_{1}_FL and M

_{2}_FL modules. For the output variable of the M

_{3}_FL module, the MFs are depicted in Figure 7.

#### 4.2. Results of SHFL Model and Execution Results of DIY

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 4.**The detailed design of the proposed simplified hierarchical fuzzy (SHFIS) model on the Do it Yourself (DIY) toolbox.

**Figure 5.**Input/output membership functions (MFs) of input/output variables of M

_{1}_FL. (

**a**) depth; (

**b**) length; (

**c**) risk.

**Figure 9.**Actual risk index values provided by the Electronics and Telecommunications Research Institute (ETRI) and the calculated risk index values by using the simplified hierarchical fuzzy logic (SHFL).

Abbreviation | Description | Abbreviation | Description |
---|---|---|---|

X1 | Depth | NG | Near to Ground |

X2 | Length | N | Normal |

X3 | Age | ND | Near to Deep |

X4 | Leakage | DR | Deeper |

SHFL | Simplified Hierarchical Fuzzy Logic | DT | Deepest |

WSPL | Water Supply Pipelines | ST | Shortest |

BPM | Business Process Modeling | S | Short |

TFL | Traditional Fuzzy Logic | M | Medium |

HFL | Hierarchical Fuzzy Logic | L | Long |

PFLR | Proposed Fuzzy Logic Rules | LT | Longest |

TFLR | Traditional Fuzzy Logic Rules | VLLR | Very Low-Level Risk |

M1_FL | Middle Layer 1 Fuzzy Logic | LLR | Low-Level Risk |

M2_FL | Middle Layer 2 Fuzzy Logic | MLR | Medium-Level Risk |

M3_FL | Out Fuzzy Logic | HLR | High-Level Risk |

ELP | Extremely Low-level Probability | VHLR | Very High-Level Risk |

VLP | Very Low-level Probability | FL | Fuzzy Logic |

LP | Low-level Probability | WS | Water Supply |

MP | Intermediate-level Probability | RI | Risk Index |

HP | High-level Probability | DT | Deepest |

VHP | Very High-level Probability | ST | Shortest |

EHP | Extremely High-level Probability | S | Short |

VO | Very Old | M | Medium |

O | Old | L | Long |

IA | Intermediate Age | LR | Longer |

N | Normal | FL | Fuzzy Logic |

BN | Brand New | WS | Water Supply |

ENG | Extremely Near to Ground | RI | Risk Index |

D | Deep |

X_{1} | ENG | NG | N | ND | D | DR | DT | |
---|---|---|---|---|---|---|---|---|

X_{2} | ||||||||

ST | VHLR | VLLR | VLLR | LLR | MLR | MLR | VHLR | |

S | VHLR | LLR | LLR | ILR | MLR | HLR | LLR | |

M | VHLR | LLR | MLR | ILR | HLR | VHLR | LLR | |

L | VHLR | ILR | ILR | HLR | VHLR | VHLR | ILR | |

LT | VHLR | ILR | HLR | VHLR | VHLR | VHLR | HLR |

X_{3} | ELLP | VLLP | LLP | ILP | HLP | VHLP | EHLP | |
---|---|---|---|---|---|---|---|---|

X_{4} | ||||||||

VO | VLLR | VLLR | LLR | ILR | ILR | VHLR | VHLR | |

O | VLLR | LLR | ILR | ILR | HLR | VHLR | VHLR | |

IA | LLR | ILR | ILR | HLR | VHLR | VHLR | EHLR | |

N | ILR | ILR | HLR | VHLR | VHLR | VHLR | EHLR | |

BN | ILR | HLR | VHLR | VHLR | VHLR | VHLR | EHLR |

PR1 | VLR | LR | IR | HR | VHR | |
---|---|---|---|---|---|---|

PR2 | ||||||

VLR | VLLR | VLLR | LLR | ILR | ILR | |

LR | VLLR | LLR | ILR | ILR | HLR | |

IR | LLR | ILR | ILR | HLR | VHLR | |

HR | ILR | ILR | HLR | VHLR | VHLR | |

VHR | ILR | HLR | VHLR | VHLR | VHLR |

Component | Description |
---|---|

Hardware | Raspberry Pi 3 Model B |

Operating System | Raspbian |

Memory | 1GB Resources |

Actuators | LEDs |

IDE | Vim, PyCharm (Remote Access) |

Programming Language | Python 3 |

**Table 6.**Development environment for virtual device manager, service composition manager and Business Process Modeling (BPM) manager.

Component | Description |
---|---|

Operation System | Window 7 64 bits |

CPU | Intel Xeon E3-1230 V2 @ 3.3 Ghz × 2 |

Memory | 8GB |

Development environment | Eclipse Luna |

CoAP Platform | Californium |

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## Share and Cite

**MDPI and ACS Style**

Fayaz, M.; Pham, Q.B.; Linh, N.T.T.; Nhi, P.T.T.; Khoi, D.N.; Qureshi, M.S.; Shah, A.S.; Khalid, S.
A Water Supply Pipeline Risk Analysis Methodology Based on DIY and Hierarchical Fuzzy Inference. *Symmetry* **2020**, *12*, 44.
https://doi.org/10.3390/sym12010044

**AMA Style**

Fayaz M, Pham QB, Linh NTT, Nhi PTT, Khoi DN, Qureshi MS, Shah AS, Khalid S.
A Water Supply Pipeline Risk Analysis Methodology Based on DIY and Hierarchical Fuzzy Inference. *Symmetry*. 2020; 12(1):44.
https://doi.org/10.3390/sym12010044

**Chicago/Turabian Style**

Fayaz, Muhammad, Quoc Bao Pham, Nguyen Thi Thuy Linh, Pham Thi Thao Nhi, Dao Nguyen Khoi, Muhammad Shuaib Qureshi, Abdul Salam Shah, and Shah Khalid.
2020. "A Water Supply Pipeline Risk Analysis Methodology Based on DIY and Hierarchical Fuzzy Inference" *Symmetry* 12, no. 1: 44.
https://doi.org/10.3390/sym12010044