Comparison between LSTM neural network and dimensional analysis method in discharge calculation of arc gates
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Abstract:
The control gate is an important control hydraulic structure in open channel water transfer project,which controls the water level and discharge of the channel by adjusting the opening of the gate.Currently,the safety performance and service performance of the arc gate have been paid much attention.The arc gate has been widely used in various open channel water transfer projects because of its advantages such as light weight,small lifting force,stable water flow pattern,simple operation and maintenance.Therefore,the accurate calculation of the discharge of arc gate is of great significance to ensure the reasonable design of the engineering building,the scientific control of the water transmission channel and the safe operation of the water transfer system. In previous studies,the discharge of arc gate was mainly calculated by empirical formula.Due to the complex structure of the arc gate,it was difficult to calibrate the discharge coefficient,submergence coefficient and other parameters in the empirical formula.Besides,the coefficient changed with the change of flow state,so its applicable conditions had certain limitations.In addition,the discharge coefficient of arc gate of the empirical formula was a function of the gate opening degree and the upstream and downstream head difference,and the relationship was mostly nonlinear,which made parameter calibration process complicated and more erroneous.Based on this,the improved arc gate discharge calculation methods are put forward from two different levels of mechanism and data:dimensional analysis method and Long-Short Term Memory neural network. Since the beginning of water transmission of Middle Route of South-to-North Water Transfer Project,a long series of historical water situation data have been accumulated,and the amount of data fully meets the needs of model construction.Consequently,taking 59 control gates in the Middle Route of South-to-North Water Transfer Project as the research object,two different discharge calculation method of arc gates based on dimensional analysis method and Long-Short Term Memory neural network (LSTM) were established.Selecting the original observation data of 2-hour time scale from 2018 to 2019,the average absolute error, average relative error,root mean square error and Nash efficiency coefficient of two models were compared and analyzed,which showed that the error results of the Long-Short Term Memory neural network method was a little better than the dimensional analysis method for the project as a whole,with the average relative errors between the two methods were 2%~2.5% and 3%~4%,respectively. In conclusion, as for parameter calibration,compared with the conventional formula of arc gate discharge,the dimensional analysis method only contained two parameters so that it was simple,economical and easy to linearize.The method of LSTM neural network did not need parameter calibration,which further reduced the workload of calculation.As for method applicability,the dimensional analysis method was greatly affected by the water level fluctuations,and it was more suitable for the overflow calculation of arc gates in the middle and downstream (medium and small discharge) of the Middle Route of South-to-North Water Transfer Project.Contrarily, the LSTM neural network method was relatively slightly affected by water level fluctuation,which was more suitable for discharge calculation of arc gates in the middle and upstream (large and medium discharge).This study provided a scientific basis for the hydraulic calculation and scheduling operation of the gates of the Middle Route of South-to-North Water Transfer Project.However,the methods used were only verified in the arc gates in the Middle Route of South-to-North Water Transfer Project.Whether there are other methods with higher calculation accuracy is worthy of further study.
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