干旱区生态环境知识资源中心(Arid): 基于PSO-ELM的温室梨枣树液流量模拟

Arid

	基于PSO-ELM的温室梨枣树液流量模拟
其他题名	Sap Flow of Pear-jujube Simulation in Greenhouse Based on PSO-ELM Model
	张念 1; 崔宁博 2; 赵璐 3; 肖璐 1; 张福娟 1; 麻泽龙 4; 乐进华 5
来源期刊	灌溉排水学报
ISSN	1672-3317
出版年	2019
卷号	38 期号:8 页码:1-8
中文摘要	【目的】精准模拟温室梨枣树液流量。【方法】基于粒子群算法(PSO)优化的极限学习机(ELM)模型,选取了西北旱区的温室梨枣树逐日气象资料和梨枣树生理指标作为输入参数,构建了16种不同参数组合的PSO-ELM模型对梨枣树各生育期的液流量进行模拟,并与实测液流值进行对比。【结果】PSO-ELM模型能通过较少的输入参数实现梨枣树液流量的高精度模拟:全生育期液流量模拟中M_2模型(输入参数为叶面积指数、平均气温、实际水汽压、平均相对湿度、净辐射和风速)、M_4模型(输入参数为叶面积指数、平均气温、实际水汽压、平均相对湿度、风速和土壤含水率)及M_(12)模型(输入参数为叶面积指数、实际水汽压和平均相对湿度)的MAE、MBE、R~2、MRE及RRMSE范围分别为1.467 6~1.598 6 mm/d、-0.000 9~0 mm/d、0.370 6~0.435 4、0.177 2~0.185 5及0.202 6~0.214 0,GPI排名分别1、2和5,其中M_(12)的输入参数较少且模拟精度较高,其MAE、MBE、R~2、MRE、RRMSE分别为1.598 6 mm/d、0、0.370 6、 0.185 5、0.214 0;萌芽展叶期、开花坐果期、果实膨大期和果实成熟期液流量模拟结果分别以M_(Ⅰ-11)模型(输入参数为净辐射、叶面积指数和实际水汽压)、M_(Ⅱ-15)模型(输入参数为实际水汽压和平均气温)、M_(Ⅲ-11)模型(输入参数为平均相对湿度、叶面积指数和土壤含水率)和M_(Ⅳ-12)模型(输入参数为叶面积指数、净辐射和平均气温)模拟精度较高,GPI排名分别为8、2、4和5。【结论】PSO-ELM模型模拟温室梨枣树不同生育期液流量均具有较高的精度,可作为温室梨枣树液流量估算的新方法。
英文摘要	【Objective】Accurately simulate the sap flow of pear-jujube in greenhouse.【Method】Based on the extreme learning machine (ELM) model of particle swarm algorithm (PSO) optimization, the daily meteorological data of pear-jujube in arid areas of Northwest China and the physiological index of pear-jujube tree were selected as input parameters, and 16 kinds of PSO-ELM models with different parameter combinations were constructed to simulate the sap flow of pear-jujube in each growth period and compared with the measured sap flow【. Result】 The PSO-ELM model could realize the high precision simulation of pear-jujube sap flow with less input parameters: in total growth period, M_2 model (the input parameters are leaf area index, average temperature, actual water vapor pressure, average relative humidity, net radiation and wind speed), M_4 model (the input parameters are leaf area index, average temperature, actual water vapor pressure, average relative humidity, wind speed and soil moisture content) and M_(12) model (the input parameters are leaf area index, actual water vapor pressure and average relative humidity) had MAE, MBE, R~2, MRE and RRMSE ranges of 1.467 6 to 1.598 6 mm/d, -0.000 9 to 0 mm/d, 0.370 6 to 0.435 4, 0.177 2 to 0.185 5 and 0.202 6 to 0.214 0, respectively, with GPI rankings of 1, 2 and 5 respectively, of which M_(12) had fewer input parameters but higher simulation accuracy, and its MAE, MBE, R~2, MRE and RRMSE were 1.598 6 mm/d, 0, 0.370 6, 0.185 5 and 0.214 0 respectively, and the results of sap flow simulation in the reproductive period showed that the M_(Ⅰ-11) model (the input parameters are net radiation、leaf area index and actual water vapor pressure) were used in the germination period, flowering fruit sitting period, fruit expansion period and fruit ripening period respectively, the simulation accuracy of M_(Ⅱ-15) model (the input parameters are actual water vapor pressure and average temperature), M_(Ⅲ-11) model (the input parameters are average relative humidity 、leaf area index and soil moisture content) and M_(Ⅳ-12) model (the input parameters are leaf area index, net radiation and average temperature) were high, whose GPI rankings were 8, 2, 4 and 5, respectively.【Conclusion】The simulation of sap flow in different growth periods of pear-jujube in PSO-ELM model had high accuracy, which could be a new method for estimating the sap flow of pear-jujube in greenhouse.
中文关键词	液流量 ; 粒子群优化算法 ; 极限学习机 ; 温室 ; 梨枣树
英文关键词	sap flow particle swarm optimization algorithm extreme learning machine greenhouse pear-jujube
语种	中文
收录类别	CSCD
WOS类目	AGRICULTURE MULTIDISCIPLINARY
WOS研究方向	Agriculture
CSCD记录号	CSCD:6584503
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315760
作者单位	1.四川大学/水利水电学院, 水力学与山区河流开发保护国家重点实验室, 成都, 四川 610065, 中国; 2.四川大学/水利水电学院;;西北农林科技大学;;南方丘区节水农业研究四川省重点实验室, 水力学与山区河流开发保护国家重点实验室;;旱区农业水土工程教育部重点实验室;;南方丘区节水农业研究四川省重点实验室, 成都;;杨凌;;成都, ;;陕西;; 610065;;712100;;610066; 3.四川大学/水利水电学院;;南方丘区节水农业研究四川省重点实验室, 水力学与山区河流开发保护国家重点实验室;;南方丘区节水农业研究四川省重点实验室, 成都;;成都, ;; 610065;;610066; 4.四川省水利科学研究院, 成都, 四川 610072, 中国; 5.北京东方润泽生态科技股份有限公司, 北京 100086, 中国
推荐引用方式 GB/T 7714	张念,崔宁博,赵璐,等. 基于PSO-ELM的温室梨枣树液流量模拟[J],2019,38(8):1-8.
APA	张念.,崔宁博.,赵璐.,肖璐.,张福娟.,...&乐进华.(2019).基于PSO-ELM的温室梨枣树液流量模拟.灌溉排水学报,38(8),1-8.
MLA	张念,et al."基于PSO-ELM的温室梨枣树液流量模拟".灌溉排水学报 38.8(2019):1-8.