Zhanghao Sun, Zhen Wang, Jina Zhang, Jiusheng Li, Yanfeng Li
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The random sampling method (RSM) and uniform sampling method (USM) were optimized for emitters selected through simulation, aiming to achieve higher estimation accuracies of the mean emitter discharge rate of the subunit (\n<span></span><math>\n <mover>\n <mi>q</mi>\n <mo>¯</mo>\n </mover>\n <mo>)</mo></math> and the Christiansen uniformity coefficient (CU) while minimizing the number of emitters tested. In addition, a linear sampling method at predetermined emitter locations (LSMPE) was developed to simplify the evaluation process using a genetic algorithm (GA). The results indicate that the appropriate sample size range for RSM was 20–40, in which the maximum percentage difference between \n<span></span><math>\n <mover>\n <mi>q</mi>\n <mo>¯</mo>\n </mover></math> and CU was maintained at ±10%. 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The absolute relative estimation error of \n<span></span><math>\n <mover>\n <mi>q</mi>\n <mo>¯</mo>\n </mover></math> and CU could be maintained at <1%.</p>","PeriodicalId":14848,"journal":{"name":"Irrigation and Drainage","volume":"73 4","pages":"1262-1278"},"PeriodicalIF":1.6000,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimization of sample size and location for evaluating the hydraulic performance of microirrigation subunits using simulation and genetic algorithms\",\"authors\":\"Zhanghao Sun, Zhen Wang, Jina Zhang, Jiusheng Li, Yanfeng Li\",\"doi\":\"10.1002/ird.2941\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Rapid and accurate field evaluation of hydraulic performance is critical for the operation of a microirrigation system. 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引用次数: 0
摘要
快速准确地实地评估水力性能对微灌系统的运行至关重要。然而,在一个子单元中选择用于现场测试的最佳样本量和发射器的具体位置尚未确定。通过将 MATLAB 和 EPANET 相结合,建立了一个用于有压灌溉系统水力分析的模型(有压灌溉系统水力分析,HAPIS)。对通过模拟选取的喷头进行了随机取样法(RSM)和均匀取样法(USM)的优化,目的是在尽量减少测试喷头数量的同时,获得更高的子单元平均喷头排放率(和克里斯琴森均匀系数(CU))的估算精度。此外,还开发了一种在预定发射器位置进行线性采样的方法(LSMPE),利用遗传算法(GA)简化了评估过程。结果表明,RSM 的合适样本量范围为 20-40,其中与 CU 之间的最大百分比差异保持在 ±10%。对于 USM,约 18 个样本量可提供相对准确的和 CU 估计值,同时建议将采样的排放口分布在 3 至 5 条侧线上。LSMPE 的最佳样本量可减少到约 10 个,所选的排放口沿取样线排列。和 CU 的绝对相对估计误差可保持在小于 1%。
Optimization of sample size and location for evaluating the hydraulic performance of microirrigation subunits using simulation and genetic algorithms
Rapid and accurate field evaluation of hydraulic performance is critical for the operation of a microirrigation system. However, the optimal sample size and the specific locations of the emitters selected in one subunit for field tests have not been determined. A model (Hydraulic Analysis of Pressurized Irrigation System,HAPIS) was constructed for hydraulic analysis of a pressurized irrigation system by coupling MATLAB and EPANET. The random sampling method (RSM) and uniform sampling method (USM) were optimized for emitters selected through simulation, aiming to achieve higher estimation accuracies of the mean emitter discharge rate of the subunit (
and the Christiansen uniformity coefficient (CU) while minimizing the number of emitters tested. In addition, a linear sampling method at predetermined emitter locations (LSMPE) was developed to simplify the evaluation process using a genetic algorithm (GA). The results indicate that the appropriate sample size range for RSM was 20–40, in which the maximum percentage difference between
and CU was maintained at ±10%. For the USM, a sample size of approximately 18 can provide relatively accurate estimations of
and CU, while it is recommended that the sampled emitters be distributed over three to five laterals. The optimal sample size of LSMPE could be decreased to approximately 10, and the selected emitters were arranged along the sampling line. The absolute relative estimation error of
and CU could be maintained at <1%.
期刊介绍:
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