Increasing Computational Efficiency of Inverse Transient Analysis for Leak Detection using GA-Kriging Surrogate Model

Document Type : Research Paper


1 PhD Student, Dept. of Civil Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Prof., Dept. of Civil Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

3 Assist. Prof., Dept. of Civil Engineering, Faculty of Engineering Shahid Chamran University of Ahvaz, Ahvaz, Iran


The inverse transient analysis (ITA) method is amongst the successful leak detection methods in water distribution networks. However, determining the unknown leakage parameters such as number, location, and area of leakages is computationally time-consuming and costly due to applying metaheuristic algorithms, like the genetic algorithm (GA). This study aimed to present a novel approach to resolve this issue in order to enhance the accuracy and speed of the ITA method while maintaining its computational structure. In this research, surrogate models were incorporated in the optimization process of the ITA method. Mimicking the behavior of the objective function, surrogate models attempt to represent the most similar behavior at a low computational cost. In this regard, a new optimization algorithm based on the Kriging surrogate model, called GA-Kriging was proposed. In this algorithm, according to the structural characteristics of the Kriging surrogate model, an EI index was presented to modify the offspring selection scheme in GA. In order to evaluate the GA-Kriging algorithm and compare its performance with the conventional GA, a reference water distribution network was considered for leak detection. The accuracy and computational efficiency of the results in the GA-Kriging algorithm were 52% and 75% higher than those of the conventional GA, respectively. The present study concluded that appropriate incorporation of surrogate models in the optimization process can make the computations more intelligent, reduce repeated computations and, ultimately, increase computational efficiency.


Chaudhry, M. H. 2014. Transient flow equations, Springer, New York,
Covas, D. & Ramos, H. 2001. Hydraulic transients used for leakage detection in water distribution systems. Proceeding 4th International Conference on Water Pipeline Systems, United Kingdom.
Haghighi, A., Covas, D. & Ramos, H. 2012a. Direct backward transient analysis for leak detection in pressurized pipelines: from theory to real application. Journal of Water Supply: Research Technology-Aqua, 61, 189-200.
Haghighi, A. & Ramos, H. M. 2012b. Detection of leakage freshwater and friction factor calibration in drinking networks using central force optimization. Water Resources Management, 26, 2347-2363.
Hwang, J. T. & Martins, J. 2018. A fast-prediction surrogate model for large datasets. Journal of Aerospace Science Technology, 75, 74-87.
Jin, Y. 2011. Surrogate-assisted evolutionary computation: recent advances and future challenges. Journal of Swarm Evolutionary Computation, 1, 61-70.
Jin, Y., Olhofer, M. & Sendhoff, B. 2002. A framework for evolutionary optimization with approximate fitness functions. Journal of IEEE Transactions on Evolutionary Computation, 6, 481-494.
Jones, D. R., Schonlau, M. & Welch, W. J. 1998. Efficient global optimization of expensive black-box functions. Journal of Global Optimization, 13, 455-492.
Kapelan, Z. S., Savic, D. A. & Walters, G. A. 2003. A hybrid inverse transient model for leakage detection and roughness calibration in pipe networks. Journal of Hydraulic Research, 41, 481-492.
Keramat, A., Ghidaoui, M. & Wang, X. 2017. Inverse transient analysis for pipeline leak detection in a noisy environment. 37th IAHR World Congress, Kuala Lampur, Malasia.
Liggett, J. A. & Chen, L. C. 1994. Inverse transient analysis in pipe networks. Journal of Hydraulic Engineering, 120, 934-955.
Pudar, R. S. & Liggett, J. A. 1992. Leaks in pipe networks. Journal of Hydraulic Engineering, 118, 1031-1046.
Shamloo, H. & Haghighi, A. 2009. Leak detection in pipelines by inverse backward transient analysis. Journal of Hydraulic Research, 47 (3), 311-318.
Shamloo, H. & Haghighi, A. 2010. Optimum leak detection and calibration of pipe networks by inverse transient analysis. Journal of Hydraulic Research, 48 (3), 371-376.
Shi, L. & Rasheed, K. 2008. ASAGA: an adaptive surrogate-assisted genetic algorithm. Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, ACM, New York. 1049-1056.
Viana, F. A., Haftka, R. T. & Watson, L. T. 2013. Efficient global optimization algorithm assisted by multiple surrogate techniques. Journal of Global Optimization, 56, 669-689.
Vítkovský, J. P., Simpson, A. R., Lambert, M. F. & Management 2000. Leak detection and calibration using transients and genetic algorithms. Journal of Water Resources Planning, 126, 262-265.
Weil, G. J. 1993. Non contact, remote sensing of buried water pipeline leaks using infrared thermography. Water Management in the'90s: a Time for Innovation, ASCE, USA.
Yu, H., Tan, Y., Zeng, J., Sun, C. & Jin, Y. 2018. Surrogate-assisted hierarchical particle swarm optimization. Journal of Information Sciences, 454, 59-72.
Zhou, Z., Ong, Y. S. & Nair, P. B. 2004. Hierarchical surrogate-assisted evolutionary optimization framework. Institute of Electrical and Electronics Engineers, 2, 1586-1593.