Uncertainty Analysis of Looped Water Distribution Networks Using Linked EPANET-GA Method

• Published in: Water resources management Vol. 30; no. 1; pp. 331 - 358
• Main Authors: Sivakumar, P, Prasad, R. K, Chandramouli, S
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01-01-2016; Springer Nature B.V
• Subjects: Algorithms; Atmospheric Sciences; Civil Engineering; Computer simulation; Earth and Environmental Science; Earth Sciences; Environment; Fluid dynamics; Fluid flow; Fuzzy sets; Genetic algorithms; Geotechnical Engineering & Applied Earth Sciences; Hydraulic gradient; Hydraulics; Hydrogeology; Hydrology/Water Resources; Linear programming; Networks; Optimization; Optimization techniques; Parameters; Pipe; pipes; pumps; Random variables; roughness; Set theory; Simulation; Studies; Uncertainty; uncertainty analysis; Water analysis; Water distribution; Water levels; Water pipes; Water resources management; EPANET 2; α-cut; Genetic algorithm; Uncertainty analysis; Water distribution network
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-015-1165-x

The hydraulic analysis of water distribution network is generally done by the conventional method. The obtained results such as pipe discharges, hydraulic gradient level of nodes, nodal concentrations etc., are normally crisp values assuming crisp input parameters. But, in real networks, there are many uncertainties in nodal demands, roughness, length, diameters of pipes, valve operations, water levels in reservoirs, head-discharge characteristics of pumps, etc. The results obtained by the conventional method may not be satisfactory in practice. Hence, in this study, an attempt is made to consider pipe roughness as uncertain parameter and to evaluate the pipe discharges and nodal hydraulic gradient levels accordingly. The Genetic Algorithm Optimization based methodology has been used to obtain the unknown parameters at each α-cut level. The hydraulic simulations are done by using EPANET 2 in MATLAB environment. The maximum and minimum values of pipe discharges and nodal hydraulic gradient levels are obtained in each simulation run. The results show that the required time to run the simulation is same for all networks irrespective of the size after adopting a particular Genetic Algorithm parameters tuning. The obtained results are compared with past studies and it is found that present method is effective in analyzing the uncertainty problem, particularly for large scale networks. The results of pipe discharges are found to vary at 30, 48.3, 58 and 9 % and of the hydraulic heads at nodes at 3.17 m, 4.3 m, 2.2 m and 12.64 m for the selected problems taken from the literature. This study would help to analyze the pipe network under the conditions of uncertainty in input parameters.