The aim of this research is to optimize the combination mode of pipe network in agricultural pipeline irrigation, improve irrigation efficiency and cut irrigation costs. Support vector machine (SVM) coupled with genetic algorithm (GA) was employed to optimize the model, and the optimal combination of the number of standard sections of branch pipes with different diameters that met the constraints and the objective function was obtained. The objective function was to minimize the cost per unit area of pipeline under the assumption of unlimited irrigation area; the decision variables were the number of standard sections of branch and capillary; the constraint conditions were the maximum allowable pressure difference of the system and the minimum number of pipes constituting the pipe network. Results showed that when capillary pipes were laid in both directions, the cost of pipes per unit area was the lowest, 8 755.7 yuan/hm2, the irrigation area was 24.15 hm2, and the water head difference of the pipe network reached 97.6%-99.7% allowed by the system. Compared with one-way laying, the cost of pipe per hectare of two-way laying was reduced by about 6.5%, and the irrigation area was increased by about 103%. Compared with the results in existing literature, the cost per hectare of pipeline decreased by 5%, and the irrigation area increased by 23%. In conclusion, support vector machine coupled with genetic algorithm can better optimize the design of pipe network, and provides a reference for agricultural water-saving irrigation.
Key words
irrigation pipeline /
support vector machine /
genetic algorithm /
optimal combination /
lowest cost /
irrigation area
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1] 沈海标, 奕永庆. 浙江省推广喷滴灌技术措施[J]. 排灌机械工程学报, 2015, 33(7): 640-644.
[2] KEIIER J, KARMELI D. Trickle Irrigation Design Parameters[J]. Transactions of the ASAE, 1974, 17(4): 678-684.
[3] MONSERRAT J,BARRAGAN J,COTS L.Design of Paired Laterals on Uniformly Sloping Fields[J].Journal of Irrigation & Drainage Engineering,2018,144(6):04018008.
[4] JU X L, WECKLER P R, WU P T, et al. New Simplified Approach for Hydraulic Design of Micro-irrigation Paired Laterals[J]. Transactions of the ASABE, 2015, 58(6): 1521-1534 .
[5] BAIAMONTE G,PROVENZANO G,GIOVANNI R.Analytical Approach Determining the Optimal Length of Paired Drip Laterals in Uniformly Sloped Field[J].Journal of Irrigation and Drainage Engineering,2015,141(1):04014042.
[6] GB/T 50485—2009,微灌工程技术规范[S]. 北京: 中国计划出版社, 2009.
[7] 李援农, 朱 锋. 基于最大控制面积和最低费用的微灌小区管网优化[J]. 农业工程学报, 2015, 31(23):80-87.
[8] 朱 锋. 基于MATLAB遗传算法的微灌小区管网最优单元设计[D]. 杨凌: 西北农林科技大学, 2016.
[9] 郭 铭. 基于遗传算法的滴灌支管轮灌小区管网优化布置研究[J]. 灌溉排水学报, 2018, 37(2): 72-76.
[10] 马朋辉, 胡亚瑾, 刘韩生, 等. 微灌田间管网布置与管径同步优化及影响因素分析[J]. 水利学报, 2019, 50(11): 1350-1364, 1373.
[11] 马朋辉, 刘韩生, 胡亚瑾. 机压微灌管网系统布置与管径同步优化设计[J]. 农业机械学报, 2019, 50(4):236-244.
[12] 钱秋培, 崔伟杰, 包腾飞, 等. 基于SVM 的混凝土坝变形监控模型预测能力实例分析[J]. 长江科学院院报, 2018, 35(8): 46-50.
[13] 汪海燕, 黎建辉, 杨风雷. 支持向量机理论及算法研究综述[J]. 计算机应用研究, 2014, 31(5):1281-1286.
[14] 饶云康, 丁 瑜, 许文年, 等. 应用GA-BP神经网络预估砾类土的最大干密度[J]. 长江科学院院报, 2019,36(4):88-92.
[15] 张泽麟, 徐金玉. 结合支撑向量机的混合遗传算法[J]. 现代计算机(专业版), 2013, (26): 3-6, 11.
[16] 蔡慧娟, 侯 丽, 张 胤. 基于SVM-GA方法的软土复合地基边坡稳定预测模型研究及应用[J]. 水利水电技术, 2018, 49(9): 178-183.
[17] 白 丹. 多孔管允许最大长度的计算[J]. 农业机械学报, 1994, 25(4):49-52.
[18] 何文学. 水力学[M]. 2版.北京: 中国水利水电出版社, 2013: 101-130.
[19] 刘志刚, 李德仁, 秦前清, 等. 支持向量机在多类分类问题中的推广[J]. 计算机工程与应用, 2004, 40(7): 10-13, 65.
[20] 张志昊, 王 珍, 栗岩峰, 等. 滴灌系统毛管单/双向供水方式对灌水和施肥均匀性的影响[J]. 水利学报, 2020, 51(6): 727-737.