Transient Measurement Site Design in pipe networks using the Decision Table Method (DTM)

Document Type: Research Paper


1 Ph.D. Candidate, Civil Engineering Department, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Ph.D. graduated, Department of Civil Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

3 PhD Student, University of Stuttgart, Institute for Sanitary Engineering, Water Quality and Solid Waste Management, Stuttgart, Germany

4 Associate Professor, Civil engineering department, Faculty of engineering, Shahid Chamran University of Ahvaz, Golestan Boulevard, Ahvaz, Iran.


The accuracy of leak detection and calibration of pipe networks by means of the inverse transient analysis (ITA) is highly affected by the number and location of the measurement sites. This study introduces a conceptual decision-making model, the Decision Table Method (DTM), for the measurement site design of pipe networks with the aim of inverse transient analysis. Through the Decision Table Method, near optimum measurement sites are decided based on two criteria of the maximum sensitivity of measurement sites and the maximum diversity of sensitivity with respect to unknown parameters of leak areas and friction factors. The main advantage of DTM is that even in case of large networks, calculation of the Hessian matrix and the utilization of any optimization algorithm is not required. To evaluate the efficiency and applicability of the method, it is applied on two pipe networks of small and large size from the literature and the results are compared with the previous methods. Accordingly, the DTM is found reliable as well as easy to understand and implement.


  1. Carrera, J. and S.P. Neuman, Estimation of Aquifer Parameters Under Transient and Steady State Conditions: 1. Maximum Likelihood Method Incorporating Prior Information. Water Resources Research, 1986. 22(2): p. 199-210.
  2. Knopman, D.S. and C.I. Voss, Multiobjective sampling design for parameter estimation and model discrimination in groundwater solute transport. Water Resources Research, 1989. 25(10): p. 2245-2258.
  3. Loaiciga Hugo, A., et al., Review of Ground‐Water Quality Monitoring Network Design. Journal of Hydraulic Engineering, 1992. 118(1): p. 11-37.
  4. Ayvaz, M.T. and A. Elçi, Identification of the optimum groundwater quality monitoring network using a genetic algorithm based optimization approach. Journal of Hydrology, 2018. 563: p. 1078-1091.
  5. Luo, Q., et al., Multi-objective optimization of long-term groundwater monitoring network design using a probabilistic Pareto genetic algorithm under uncertainty. Journal of Hydrology, 2016. 534: p. 352-363.
  6. Pham, H.V. and F.T.C. Tsai, Bayesian experimental design for identification of model propositions and conceptual model uncertainty reduction. Advances in Water Resources, 2015. 83: p. 148-159.
  7. Jin, X., R.S. Ranjithan, and G. Mahinthakumar, A Monitoring Network Design Procedure for Three-Dimensional (3D) Groundwater Contaminant Source Identification. Environmental Forensics, 2014. 15(1): p. 78-96.
  8. Dhar, A., Geostatistics-based design of regional groundwater monitoring framework. ISH Journal of Hydraulic Engineering, 2013. 19(2): p. 80-87.
  9. Babbar-Sebens, M. and B.S. Minsker, Interactive Genetic Algorithm with Mixed Initiative Interaction for multi-criteria ground water monitoring design. Applied Soft Computing, 2012. 12(1): p. 182-195.
  10. Kollat, J.B., P.M. Reed, and R.M. Maxwell, Many-objective groundwater monitoring network design using bias-aware ensemble Kalman filtering, evolutionary optimization, and visual analytics. 2011. 47(2).
  11. Dole-Oliver, M.-J., et al., Towards an optimal sampling strategy to assess groundwater biodiversity: comparison across six European regions. 2009. 54(4): p. 777-796.
  12. Li, Y. and A.B. Chan Hilton, Optimal groundwater monitoring design using an ant colony optimization paradigm. Environmental Modelling & Software, 2007. 22(1): p. 110-116.
  13. Wu, J., C. Zheng, and C.C. Chien, Cost-effective sampling network design for contaminant plume monitoring under general hydrogeological conditions. Journal of Contaminant Hydrology, 2005. 77(1): p. 41-65.
  14. Vitkovsky, J.P., et al., Optimal Measurement Site Locations for Inverse Transient Analysis in Pipe Networks. Journal of Water Resources Planning and Management, 2003. 129(6): p. 480-492.
  15. Liggett, J.A. and L.C. Chen, Inverse Transient Analysis in Pipe Networks. Journal of Hydraulic Engineering, 1994. 120(8): p. 934-955.
  16. Shamloo, H. and A. Haghighi, Optimum leak detection and calibration of pipe networks by inverse transient analysis. Journal of Hydraulic Research, 2010. 48(3): p. 371-376.
  17. Gamboa-Medina, M.M. and L.F.R. Reis Sampling Design for Leak Detection in Water Distribution Networks. Procedia Engineering, 2017. 186: p. 460-469.
  18. Jeppson, R. W. (1976). Analysis of flow in pipe networks. Anne Arbor, Mich: Anne Arbor Science Publishers.