| Cite this article: | 张念,崔宁博,赵璐,等.基于PSO-ELM的温室梨枣树液流量模拟[J].灌溉排水学报,2019,(8):-. |
| Zhang Nian,Cui Ningbo,Zhao Lu,et al.基于PSO-ELM的温室梨枣树液流量模拟[J].灌溉排水学报,2019,(8):-. |
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| This article has been：Browse 2471Times Download 0Times |
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| DOI：10.13522/j.cnki.ggps.2019012 |
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| Sap Flow of Pear-jujube Simulation in Greenhouse Based on PSO-ELM Model |
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Zhang Nian1, Cui Ningbo1, Zhao Lu1, Xiao Lu1, Zhang Fujuan1, Ma Zelong2, Yue Jinhua3
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1.Sichuan University State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resources and Hydro Power;2.Sichuan Provincial Water Conservancy Research Institute;3.Beijing Dongfang Runze Ecological Technology Co.
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| Abstract: |
| 【Objective】Accurate simulation of the sap flow of pear-jujube in greenhouse is important to realize its precise irrigation. 【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, M2 model (input leaf area index, average temperature, actual water vapor pressure, average relative humidity, net radiation and wind speed), M4 model (input leaf area index, average temperature, actual water vapor pressure, average relative humidity, wind speed and soil moisture content ) and M12 model (input leaf area index, actual water vapor pressure and average relative humidity) had MAE, MBE, R2, MRE and RRMSE ranges of 1.4676 to 1.5986mm/d, -0.0009 to 0mm/d, 0.3706 to 0.4354, 0.1772 to 0.1855 and 0.2026 to 0.2140, respectively, with GPI rankings of 1, 2 and 5 respectively, of which M12 had fewer input parameters but higher simulation accuracy, and its MAE, MBE, R2, MRE and RRMSE were 1.5986mm/d, 0, 0.3706、0.1855 and 0.2140 respectively, and the results of sap flow simulation in the reproductive period showed that the MⅠ-11 model (input 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 MⅡ-15 model (input actual water vapor pressure and average temperature), MⅢ-11 model (input average relative humidity、leaf area index and soil moisture content) and MⅣ-12 model (input 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. |
| Key words: sap flow; particle swarm optimization algorithm; extreme learning machine; greenhouse; pear-jujube |
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