Parisa Ebadzadeh , Rasoul Daneshfaraz , Bahram Nourani , John Abraham
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引用次数: 0
Abstract
Sluice gates with a semi-cylindrical sill are flow control structures that are used in irrigation canals to regulate water level and flow discharge. To estimate the flow discharge through these structures, it is necessary to accurately estimate the discharge coefficient. The aim of this study is to present a new approach based on data-mining to accurately estimate the Cd based on experimental data. First, standalone data-mining models such as Artificial Neural Network (ANN) and Gaussian Process Regression (GPR) were developed. Then, to improve the performance of the standalone models, a multiple model (MM) strategy was used to develop new multiple models handled by ANN (MM-ANN) and GPR (MM-GPR). Next, an ensemble model (EM) strategy was developed. A total of 107 experiments were conducted to investigate the effect of the semi-cylindrical sill geometry on the discharge coefficient. 70 % of the data was reserved for the training phase, and the remaining 30 % for the testing phase. The ratio of energy head to sill width (h/b) and approach energy head to wetted parameter (h/P) were as input variables and the discharge coefficient (Cd) was an output variable. The outcomes of the multiple models and ensemble model were compared to the standalone methods using statistical metrics (R2, RE%, RMSE, and MAE) and graphical tools (Taylor, Violin, RE%, and scatter plots). The MM-ANN model with R = 0.951, R2 = 0.904, SI = 0.012, RE% = 0.891, MAE = 0.005, and RMSE = 0.007 outperformed the ANN, GPR, MM-GPR, and EM models in accuracy. The h/p variable had the greatest effect on the target variable of MM-ANN evidenced by a SHAP value of 0.45. The MM-ANN model provided reasonable estimates the experimental results. It is recommended to implement the multiple model strategy in order to improve the calculation accuracy of the models in this field.
期刊介绍:
Flow Measurement and Instrumentation is dedicated to disseminating the latest research results on all aspects of flow measurement, in both closed conduits and open channels. The design of flow measurement systems involves a wide variety of multidisciplinary activities including modelling the flow sensor, the fluid flow and the sensor/fluid interactions through the use of computation techniques; the development of advanced transducer systems and their associated signal processing and the laboratory and field assessment of the overall system under ideal and disturbed conditions.
FMI is the essential forum for critical information exchange, and contributions are particularly encouraged in the following areas of interest:
Modelling: the application of mathematical and computational modelling to the interaction of fluid dynamics with flowmeters, including flowmeter behaviour, improved flowmeter design and installation problems. Application of CAD/CAE techniques to flowmeter modelling are eligible.
Design and development: the detailed design of the flowmeter head and/or signal processing aspects of novel flowmeters. Emphasis is given to papers identifying new sensor configurations, multisensor flow measurement systems, non-intrusive flow metering techniques and the application of microelectronic techniques in smart or intelligent systems.
Calibration techniques: including descriptions of new or existing calibration facilities and techniques, calibration data from different flowmeter types, and calibration intercomparison data from different laboratories.
Installation effect data: dealing with the effects of non-ideal flow conditions on flowmeters. Papers combining a theoretical understanding of flowmeter behaviour with experimental work are particularly welcome.