基于LSSVM-GA的沟灌入渗参数与糙率估算与验证
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国家自然科学基金资助项目 (41571222、51909208),陕西省科技厅秦创原“科学家+工程师”队伍建设项目(2024QCY-KXJ-100)


Estimation and validation of furrow infiltration parameters and roughness based on LSSVM-GA
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    摘要:

    入渗参数和糙率是沟灌设计和管理中需要确定的重要基本参数。该研究基于WinSRFR软件模拟结果构建样本集,通过最小二乘支持向量机(least squares support vector machines,LSSVM)回归模型来映射水流推进时间、消退时间与入渗参数、糙率之间的非线性关系,并在此基础上提出了结合最小二乘支持向量机和遗传算法(least squares support vector machines-genetic algorithm,LSSVM-GA)的参数估算方法,即利用LSSVM回归模型构建目标函数,并利用GA获得入渗参数和糙率的最优值。在4组尾端封闭沟试验基础上,将LSSVM-GA法与多元非线性回归(multiple nonlinear regression,MNR)及WinSRFR中的Merriam-Keller post-irrigation volume balance analysis(MK-PIVB)进行对比,结果表明,LSSVM-GA法估算的参数对进退水过程的拟合效果较优,其模拟的推进和消退过程均方根误差分别介于1.06~2.12 min和2.28~3.11min之间,表明LSSVM-GA在估算入渗参数和糙率方面的可靠性,这有助于获得更精确的灌水技术要素,进而提高沟灌性能。

    Abstract:

    Infiltration parameters and roughness are essential parameters in the design and management of furrow irrigation. Accurate determination of these parameters is essential for achieving reliable irrigation techniques and improving furrow irrigation performance. Therefore, this study proposed a method combining least squares support vector machines (LSSVM) and genetic algorithm (GA) to invert infiltration parameters and roughness. During the parameters inversion, it is necessary to know the simulated values of advance and recession time to construct the objective function, but the relationship between the furrow irrigation advance, recession time and soil infiltration parameters and roughness is implicit in the zero-inertia model, lacking a direct solution. Manually inputting parameters into WinSRFR for validation would severely hinder the efficiency of parameter estimation. Therefore, this study utilized LSSVM to map the complex nonlinear relationship the furrow irrigation advance, recession times and soil infiltration parameters and roughness. The advance time LSSVM regression model (regularization constant 60, RBF kernel width 35) and recession time LSSVM regression model (regularization constant 70, RBF kernel width 20) were constructed. Results showed that the root mean square error (RMSE) for advance and recession time in training samples ranged from 0.84 to 1.75 min and 2.18 to 3.16 min, respectively. For testing samples, the RMSE values ranged from 0.85 to 1.77 min and 2.41 to 3.62 min, demonstrating high prediction accuracy. This approach reduces the computational burden of manual input in WinSRFR by using LSSVM regression models to simulate advance/recession time, thus facilitating parameter estimation with GA. The key to implementing the LSSVM-GA method lies in the data transfer and iteration between LSSVM and GA when calculating the fitness function. To further verify the reliability of the proposed method, furrow irrigation experiments were conducted on closed-end furrows in cornfields in Shuangzhao Village, Xianyang, Shaanxi Province, China. Two typical furrow lengths were selected, each with two test plots to observe the actual advance and recession times. Each scheme was repeated three times, and the final experimental data were averaged. Multiple nonlinear regression (MNR) and Merriam-Keller post-irrigation volume balance analysis (MK-PIVB) from WinSRFR were also used for comparison. Results indicated that for the advance process simulation, the parameters estimated by MK-PIVB and LSSVM-GA provided similar curves, with RMSE ranging from 1.30 to 2.94 min and coefficient of determination (R2) from 0.97 to 0.99. In contrast, MNR performed slightly worse, particularly for F1, with an RMSE of 5.52 min. Further analysis of the recession process showed that MNR's parameter estimates were not ideal, with RMSE ranging from 4.77 to 6.00 min and R2 below 0.60. MK-PIVB's estimates were satisfactory only for F2, but were inaccurate for the other three furrows with RMSE of 4.75, 4.13, and 4.18 minutes and R2 of 0.49, 0.70, and 0.54, respectively. The LSSVM-GA method provided improved parameter estimates for both processes, with RMSEs ranging from 1.06 to 2.12 minutes for advance and 2.28 to 3.11 minutes for recession. The LSSVM-GA method effectively combined nonlinear mapping and heuristic algorithms, overcoming the limitations of the other two methods, such as the need for predefined regression functions, reliance on manual experience and judgment, and high computational cost.

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周雯,白丹,李一博,马鑫,白雪丽.基于LSSVM-GA的沟灌入渗参数与糙率估算与验证[J].农业工程学报,2024,40(18):62-69. DOI:10.11975/j. issn.1002-6819.202401182

ZHOU Wen, BAI Dan, LI Yibo, MA Xin, BAI Xueli. Estimation and validation of furrow infiltration parameters and roughness based on LSSVM-GA[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2024,40(18):62-69. DOI:10.11975/j. issn.1002-6819.202401182

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  • 收稿日期:2024-01-23
  • 最后修改日期:2024-05-08
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  • 在线发布日期: 2024-09-11
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