Ensemble Learning Simulation Method for Hydraulic Characteristic Parameters of Emitters Driven by Limited Data

文献类型： 外文期刊

第一作者： Yu, Jingxin

作者： Yu, Jingxin;Zhangzhong, Lili;Yu, Jingxin;Xu, Linlin;Lan, Renping;Zhang, Xin;Li, Jingjing;Zhang, Xin;Li, Jingjing;Xu, Linlin

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关键词： ensemble learning; irrigation; CFD; data-driven; numerical simulation

期刊名称：AGRONOMY-BASEL （ 影响因子：3.7； 五年影响因子：4.0 ）

ISSN：

年卷期： 2023 年 13 卷 4 期

页码：

收录情况： SCI

摘要： The emitter is one of the most critical components in drip irrigation. The flow path geometry parameters have a significant effect on the emitter's hydraulic performance and have a direct impact on the emitter's irrigation uniformity and lifetime. The hydraulic characteristics of the emitter are the key indicators of its performance. However, obtaining the hydraulic characteristics of the emitter is complex. Typically, only a small number of calibrations are performed for specific equipment models, making it difficult to obtain the parameter. Therefore, limited data corresponding to the morphological parameters and the flow rate were simulated using the FLUENT software, and the influence of the characteristics was analyzeanalyzed, based on which a flow rate prediction model was constructed using the ensemble learning (CatBoost) model. The extended data set was generated by stochastic simulation and parameter fitting. The flow index and flow coefficient prediction model were built and evaluated using the CatBoost model again with the augmented data set as a benchmark. The results show that the significant correlation between the geometric structure and the flow index and flow coefficient provides the basis for the correlation model. CatBoost can fit the complex nonlinear relationships between the parameters well, achieving excellent simulation accuracy for the flow rate (R-2 = 0.9987), flow index (R-2 = 0.9961), and flow coefficient (R-2 = 0.9946), where the path width has the highest importance score in the model construction for the flow index (score = 55.97) and flow coefficient (score = 45.2). Furthermore, the CatBoost models used in this study achieved the best prediction results compared to seven typical models (XGBoost, Bagging, Random Forest, Tree, Adaboost, and KNN).

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