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A hybrid of six soft models based on ANFIS for pipe failure rate forecasting and uncertainty analysis: a case study of Gorgan city water distribution network

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Abstract

The pipes as one of the main and important components of a water distribution network break during operation due to various factors. Developing models for pipes failure rate prediction can be one of the most important tools for managers and stakeholders during optimal operation of the water distribution network. In this study, the statistical and soft models such as Linear Regression, Generalized Linear Regression, Support Vector Machine, Feed Forward Neural Network (FFNN), Radial-Based Function Neural Network (RBFNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were studied in order to predict the pipes failure rate based on the characteristics of Gorgan city water distribution network including diameter, length, age, installation depth, and number of failures of each pipe. In order to determine the optimal values of the parameters of each model, appropriate error indices including correlation coefficient (R), Mean Square Error (MSE), and Correlation Mean Square Error Ratio (CMSER) for training and test data were calculated, and the values of the parameters related to the model with the highest value of the CMSER index were considered as the model optimal values. Furthermore, in the validation stage, the values of R and MSE error indices for each of the above models were considered as a criterion for selecting the most appropriate model for predicting pipe failure rate. The findings show that among the soft and statistical models investigated, ANFIS with MSE of 0.071 and R of 0.92 can predict the failure rate of the studied network pipes more efficiently and more accurately than other models. Yet, despite the superiority of this model over other models, this model cannot accurately predict the failure rate of the studied network pipes due to its relatively high MSE value. Therefore, a new approach was developed based on the hybridization of trained models to provide a more efficient model for a more accurate prediction of the pipe failure rates of water distribution network. In this approach, the values of the network pipe failure rate predicted by each of the soft and statistical models are considered as independent input variables, and the observational failure rate values are considered as the dependent output variable of the ANFIS model. A comparison between the values of non-hybrid model validation data indices and the results of the proposed hybrid prediction model reveals that the use of the developed hybrid model increased the R error value from 8.1% (compared to the ANFIS model) to 260% (compared to the RBFNN model). It also decreased the MSE error value from 37% (compared to the FFNN model) to 58% (compared to the RBFNN model). Moreover, the hybrid model, compared to the superior non-hybrid ANFIS model, decreased MSE error rates by 45%. The findings show that the proposed model can significantly raise the accuracy of predicting the failure rate of pipes, compared to other existing models.

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References

  • Asnaashari A, McBean EA, Gharabaghi B, Tutt D (2013) Forecasting watermain failure using artificial neural network modelling. Can Water Resour J 38(1):24–33

    Article  Google Scholar 

  • Aydogdu M, Firat M (2015) Estimation of failure rate in water distribution network using fuzzy clustering and LS-SVM methods. Water Resour Manag 29(5):1575–1590

    Article  Google Scholar 

  • Barton NA, Farewell TS, Hallett SH, Acland TF (2019) Improving pipe failure predictions: factors effecting pipe failure in drinking water networks. Water Res 164:114926. https://doi.org/10.1016/j.watres.2019.114926

    Article  Google Scholar 

  • Berardi L, Giustolisi O, Kapelan Z, Savic DA (2008) Development of pipe deterioration models for water distribution systems using EPR. J Hydroinf 10(2):113–126

    Article  Google Scholar 

  • Fares H, Zayed T (2010) Hierarchical fuzzy expert system for risk of failure of water mains. J Pipeline Syst Eng Pract 1(1):53–62

    Article  Google Scholar 

  • Faris Hamdala K, Sagar GY (2016) Statistical analysis of pipe breaks in water distribution systems in Ethiopia, the case of Hawassa. IOSR J Math 12(3):127–136

    Google Scholar 

  • Farmani R, Kakoudakis K, Behzadian Moghadam K, Butler D (2017) Pipe failure prediction in water distribution systems considering static and dynamic factors. Proc Eng 186:117–126

    Article  Google Scholar 

  • Frey HC, Patil SR (2002) Identification and review of sensitivity analysis methods. Risk Anal 22:553–578

    Article  Google Scholar 

  • Gasemnezhad S, Motiee H, Moosavi Nodoushan MS (2014) Prediction of damage rate of urban drinking water network pipes by using and developing statistical models. In: Iranian water and sewerage science and engineering congress (in Persian)

  • Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20(4):269–276

    Article  Google Scholar 

  • Harvey R, McBean EA, Gharabaghi B (2013) Predicting the timing of water main failure using artificial neural networks. J Water Resour Plan Manag 140(4):425–434

    Article  Google Scholar 

  • Ho CI, Lin MD, Lo SL (2010) Use of a GIS-based hybrid artificial neural network to prioritize the order of pipe replacement in a water distribution network. Environ Monit Assess 166(1–4):177–189

    Article  Google Scholar 

  • Islam MS, Sadiq R, Rodriguez MJ, Francisque A, Najjaran H, Hoorfar M (2011) Leakage detection and location in water distribution systems using a fuzzy-based methodology. Urban water J 8(6):351–365

    Article  Google Scholar 

  • Jafar R, Shahrour I, Juran I (2010) Application of Artificial Neural Networks (ANN) to model the failure of urban water mains. Math Comput Model 51(9–10):1170–1180

    Article  Google Scholar 

  • Kakoudakis K, Behzadian K, Farmani R, Butler D (2017) Pipeline failure prediction in water distribution networks using evolutionary polynomial regression combined with K-means clustering. Urban Water J 14(7):737–742

    Article  Google Scholar 

  • Kapelan ZS, Savic DA, Walters GA (2003) A hybrid inverse transient model for leakage detection and roughness calibration in pipe networks. J Hydraul Res 41(5):481–492

    Article  Google Scholar 

  • Kerwin S, de Soto BG, Adey BT (2019) January. Performance comparison for pipe failure prediction using artificial neural networks. In 6th international symposium on life-cycle civil engineering, IALCCE 2018. CRC Press/Balkema, pp 1337–1342

  • Kleiner Y, Rajani B (2002) Forecasting variations and trends in water-main breaks. J Infrastruct Syst 8(4):122–131

    Article  Google Scholar 

  • Malekpour MM, Tabari MMR (2020) Implementation of supervised intelligence committee machine method for monthly water level prediction. Arab J Geosci 13(19):1–14. https://doi.org/10.1007/876s12517-020-06034-x

    Article  Google Scholar 

  • Mounce SR, Day AJ, Wood AS, Khan A, Widdop PD, Machell J (2002) A neural network approach to burst detection. Water Sci Technol 45(4–5):237–246

    Article  Google Scholar 

  • Pandey P, Dongre S, Gupta R (2020) Probabilistic and fuzzy approaches for uncertainty consideration in water distribution networks: a review. Water Supply 20(1):13–27

    Article  Google Scholar 

  • Rajani B, Kleiner Y (2001) Comprehensive review of structural deterioration of water mains: physically based models. Urban Water 3(3):151–164

    Article  Google Scholar 

  • Rajeev P, Kodikara J, Robert D, Zeman P, Rajani B (2014) Factors contributing to large diameter water pipe failure. Water Asset Manag Int 10(3):9–14

    Google Scholar 

  • Robles-Velasco A, Cortés P, Muñuzuri J, Onieva L (2020) Prediction of pipe failures in water supply networks using logistic regression and support vector classification. Reliab Eng Syst Saf 196:106754. https://doi.org/10.1016/j.ress.2019.106754

    Article  Google Scholar 

  • Rogers PD (2011) Prioritizing water main renewals: case study of the Denver water system. J Pipeline Syst Eng Pract 2(3):73–81

    Article  Google Scholar 

  • Sacluti FR (1999) Modelling water distribution pipe failures using artificial neural networks. MSc. thesis, Department of Civil and Envir. Eng. University of Alberta, Canada

  • Sadiq R, Kleiner Y, Rajani B (2007) Water quality failures in distribution networks risk analysis using fuzzy logic and evidential reasoning. Risk Anal Int J 27(5):1381–1394

    Article  Google Scholar 

  • Sattar AM, Gharabaghi B (2015) Gene expression models for prediction of longitudinal dispersion coefficient in streams. J Hydrol 524:587–596

    Article  Google Scholar 

  • Sattar AM, Gharabaghi B, McBean EA (2016) Prediction of timing of watermain failure using gene expression models. Water Resour Manag 30(5):1635–1651

    Article  Google Scholar 

  • Sattar AM, Ertuğrul ÖF, Gharabaghi B, McBean EA, Cao J (2019) Extreme learning machine model for water network management. Neural Comput Appl 31(1):157–169

    Article  Google Scholar 

  • Shamir U, Howard CD (1979) An analytic approach to scheduling pipe replacement. J Am Water Works Assoc 71(5):248–258

    Article  Google Scholar 

  • Shin H, Kobayashi K, Koo J, Do M (2015) Estimating burst probability of water pipelines with a competing hazard model. J Hydroinf 18(1):126–135

    Article  Google Scholar 

  • Soltani J, Tabari MMR (2012) Determination of effective parameters in pipe failure rate in water distribution system using the combination of artificial neural networks and genetic algorithm. J Water Wastewater 23(83):2–15 (In Persian)

    Google Scholar 

  • Soltanjalili M, Bozorg-Haddad O, Mariño MA (2011) Effect of breakage level one in design of water distribution networks. Water Resour Manag 25(1):311–337

    Article  Google Scholar 

  • Tabari MMR, Malekpour Shahraki MM (2018) Reservoir water level prediction using supervised intelligent committee machine method, case study: Karaj Amirkabir Dam. Iran Water Resour Res 14(5):15–30 (in Persian)

    Google Scholar 

  • Tabari MMR, Zarif Sanayei HR (2019) Prediction of the intermediate block displacement of the dam crest using artificial neural network and support vector regression models. Soft Comput 23(19):9629–9645

    Article  Google Scholar 

  • Tabari MMR, Azari T, Dehghan V (2020) A supervised committee neural network for the determination of aquifer parameters: a case study of Katasbes aquifer in Shiraz plain, Iran. Soft Comput. https://doi.org/10.1007/s00500-020-05487-2

    Article  Google Scholar 

  • Tabesh M, Soltani J, Farmani R, Savic D (2009) Assessing pipe failure rate and mechanical reliability of water distribution networks using data-driven modeling. J Hydroinf 11(1):1–17

    Article  Google Scholar 

  • Tavakoli R, Najafi M, Sharifara A (2019) Artificial neural networks and adaptive neuro-fuzzy models for prediction of remaining useful life. arXiv:1909.02115

  • Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22(1):69–77

    Article  Google Scholar 

  • Tu MY, Tsai FTC, Yeh WWG (2005) Optimization of water distribution and water quality by hybrid genetic algorithm. J Water Resour Plan Manag 131(6):431–440

    Article  Google Scholar 

  • Valis KPU (2013) Application of fuzzy logic for failure risk assessment in water supply system management. CEST

  • Verbeeck H, Samson R, Verdonck F, Raoul L (2006) Parameter sensitivity and uncertainty of the forest carbon flux model FOUG: a Monte Carlo analysis. Tree Physiol 26:807–817

    Article  Google Scholar 

  • Walker H (1931) Studies in the History of the Statistical Method. Williams and Wilkins Co., Baltimore, pp 24–25

    Google Scholar 

  • Wang Y, Zayed T, Moselhi O (2009) Prediction models for annual break rates of water mains. J Perform Constr Facil 23(1):47–54

    Article  Google Scholar 

  • Xu Q, Chen Q, Li W, Ma J (2011) Pipe break prediction based on evolutionary data-driven methods with brief recorded data. Reliab Eng Syst Saf 96(8):942–948

    Article  Google Scholar 

  • Xu Q, Chen Q, Ma J, Blanckaert K (2013) Optimal pipe replacement strategy based on break rate prediction through genetic programming for water distribution network. J Hydro Environ Res 7(2):134–140

    Article  Google Scholar 

  • Yoon H, Jun SC, Hyun Y, Bae GO, Lee KK (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396:128–138

    Article  Google Scholar 

  • Zangenehmadar Z, Moselhi O (2016) Application of neural networks in predicting the remaining useful life of water pipelines. In: Pipelines 2016, pp 292–308

Download references

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Authors and Affiliations

  1. Department of Water Engineering, University of Gorgan Agriculture Sciences and Natural Resources, Gorgan, Iran

    Seyed Mehran Jafari & Abdol Reza Zahiri

  2. Department of Water Engineering, University of Tehran, Tehran, Iran

    Omid Bozorg Hadad

  3. Faculty of Technology and Engineering, University of Mazandaran, Mazandaran, Iran

    Mahmoud Mohammad Rezapour Tabari

  4. Center of Excellence in Risk Management and Natural Hazards, Isfahan University and Technology, Isfahan, Iran

    Mahmoud Mohammad Rezapour Tabari

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  1. Seyed Mehran Jafari
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  2. Abdol Reza Zahiri
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  3. Omid Bozorg Hadad
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  4. Mahmoud Mohammad Rezapour Tabari
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Correspondence to Mahmoud Mohammad Rezapour Tabari.

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Jafari, S.M., Zahiri, A.R., Bozorg Hadad, O. et al. A hybrid of six soft models based on ANFIS for pipe failure rate forecasting and uncertainty analysis: a case study of Gorgan city water distribution network. Soft Comput 25, 7459–7478 (2021). https://doi.org/10.1007/s00500-021-05706-4

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