Calibration of Hydraulic Model of Water Distribution System Based on Pressure Estimation
In the calibration process of hydraulic model of a water distribution system,it is challenging to make calibration results consistent with the actual values due to the lack of sufficient monitoring locations but too many calibration parameters.In order to make the calibration results match the actual values more consistently,a hydraulic model calibration method based on pressure estimation was proposed,in which the pressures at non-monitoring locations were estimated based on the measured pressures at the monitoring locations and the inlet flow data,and then the estimated nodal pressures were used as known conditions in the later calibration process.The nodal pressures were estimated by Multi-layer Perceptron(MLP)with the Hazen-William coefficients of pipelines as the calibration parameters,and the objective function was optimized using Genetic Algorithm(GA).The case study showed that the proposed MLP-GA was more effective than the GA method,which reduced the sum of absolute errors of pressures at monitoring locations by 11.00%,from 37.81 m to 33.65 m,and the sum of absolute errors of Hazen-Williams coefficients of pipelines by 9.27%,from 933.5 to 847.0.
water distribution systemhydraulic modelcalibrationpressure estimationneural network
万方数据
维普
