A Study of a Model for Predicting Pneumatic Subsoiling Resistance Based on Machine Learning Techniques

Xia, Li; Jiang, Zhangjun; Wang, Sichao; Li, Xinglong; Liu, Yu; Wang, Xuhui

doi:10.3390/agronomy13041079

Cited by 5 publications

(1 citation statement)

References 46 publications

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“…The machine learning algorithm is very suitable for constructing multi-input and multi-output nonlinear system models, with differences in the principles of different types of machine learning leading to differences in model performance. This paper selects four kinds of machine learning models with good performance, including BP, SVR, RF and XG Boost [32][33][34][35].…”

Section: Multiobjective Optimization Methods Based On Machine Learningmentioning

confidence: 99%

Intelligent Control Technology and System of on-Demand Irrigation Based on Multiobjective Optimization

et al. 2023

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To solve the problem that the parameters of the multiple-input multiple-output (MIMO) irrigation system are difficult to control accurately, an on-demand irrigation control experimental device was developed. The main input parameters of the device are the opening degree of the main pipe valve, the opening frequency of and the pump station, the opening degree of the branch pipeline valve with the different combinations of different opening degrees. Based on these input parameters, four types of experimental methods were designed, and a total of 1695 groups of experiments were designed. The results show that the different opening degree combinations of the branch electric valve cannot significantly affect the flow of the branch pipeline but also significantly affect the pressure of the main pipeline. The prediction error of the operating frequency of the pump station and the opening degree of the branch valve were regarded as the objective function. Six intelligent prediction models were constructed, which are Back Propagation (BP), support vector regression (SVR) Linear, SVR-RBF, SVR-Poly, random forest (RF) and eXtreme Gradient Boosting (XGBoost), respectively. The results show that the XGBoost is the best model among the six models. For the opening degree of three branch valves, the mean absolute error (MAE) between the predicted value and actual value is less than 3.3%, the mean square deviation (RMSE) between the predicted values and actual values is less than 4.5%, and the R2 of between the predicted values and actual values is greater than 0.990. The control models and system can meet the needs of an on-demand irrigation system.

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Section: Multiobjective Optimization Methods Based On Machine Learningmentioning

confidence: 99%

Intelligent Control Technology and System of on-Demand Irrigation Based on Multiobjective Optimization

et al. 2023

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A Machine Learning-Based User-Friendly Approach for Prediction of Traffic-Induced Vibrations and its Application for Parametric Study

Javaid,

Azam,

Saqib

et al. 2023

J. Inst. Eng. India Ser. A

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Parameter Optimization and Test for the Pulse-Type Gas Explosion Subsoiler

Xu,

Jing,

Yao

et al. 2024

Agriculture

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To address the problem of large tractive resistance in traditional subsoiling methods, this paper designed a pulse-type gas explosion subsoiler, as well as an air-blown double-ended chisel type subsoiling shovel and a conduit. The mathematical equation of the influence of the structural parameters of the subsoiler on the groove profile is established. The EDEM 2022 software was used to simulate the subsoiling operation process. The soil disturbance law of the chisel subsoiler was analyzed by the change of soil particle velocity. The optimum value interval of quadratic regression orthogonal rotation combination test factors was determined by using the steepest climb test, with specific tillage resistance and filling power as evaluation indicators. Based on the Box–Behnken design test, a second-order regression model of response value and significance parameter was obtained, and an optimal combination was found by optimizing the significance parameter. The effects of subsoiling air pressure, pulse width and pulse interval on evaluation indicators were analyzed by the response surface method; the test results show that when the air pressure was 0.8 MPa, the pulse width was 0.17 s and the pulse interval was 0.12 s, and the specific tillage resistance was 0.4421 N/mm2 and the filling power was 18.5%; a comparative test between the pulse-type gas explosion subsoiler and a continuous gas explosion subsoiler was carried out, and the specific tillage resistance was reduced by 12.2% and the filling power was reduced by 10.5%; the comparative test shows that the pulse-type gas explosion subsoiler has smaller tractive resistance per unit area and smaller disturbance to soil. The research results provide a theoretical basis and reference for the optimization and improvement of gas explosion subsoilers.

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A Study of a Model for Predicting Pneumatic Subsoiling Resistance Based on Machine Learning Techniques

Cited by 5 publications

References 46 publications

Intelligent Control Technology and System of on-Demand Irrigation Based on Multiobjective Optimization

Intelligent Control Technology and System of on-Demand Irrigation Based on Multiobjective Optimization

A Machine Learning-Based User-Friendly Approach for Prediction of Traffic-Induced Vibrations and its Application for Parametric Study

Parameter Optimization and Test for the Pulse-Type Gas Explosion Subsoiler

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