earticle

영어

Buildings flow measurement method using the pre-established upstream and downstream water levels and flow to estimate the flow is the common method for open channel flow measurement. However, due to the changes of import and export, flow pattern, and hydraulic boundary conditions, traditional mechanism modeling-based flow measurement methods which establish the relation between the upstream-downstream water levels and flow by historical records and empirical equation models are usually not able to meet the demands of precision and adaptability. The improvement is based no the neural network (data-driving). However, the neural network based method is commonly offline and the model parameters are constant in the application.If the degree of opening of the weir sluice gate changes frequently, it is hard to construct a neural network model of high precision for on-line and real-time measurement. This research designs a real-time on-line automatic measurement system, for the Pi River canal weir gate, that collects upstream and downstream water levels and the degree of opening of the gate. Moreover, it establishes a three layer BP neural network model based on on-line real-time data correction. This model comprised of a Kalman filter with forgetting factor and a three layer BP neural network data fusion center. In contrast to the standard hydrometric propeller based method, the average relative error is lower than 5%, meeting the “River Discharge Measurement Criterion” proposed by Ministry of Water Resources of the People's Republic of China. Both the precision and the repeatability can cater for the engineering applications.