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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 Nian, CUI Ningbo, ZHAO Lu, XIAO Lu, ZHANG Fujuan, MA Zelong, YUE Jinhua
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1. State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resources and Hydro Power, Sichuan University, Chengdu 610065, China; 2. Institute of Water-saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, China; 3.Provincial Key Laboratory of Water-saving Agriculture in Hill Area of Southern China, Chengdu 610066, China; 4. Sichuan Provincial Water Conservancy Research Institute, Chengdu 610072, China; 5. Beijing Dongfang Runze Ecological Technology Co., Ltd., Beijing 100191, China
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| Abstract: |
| 【Objective】Accurately simulat 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, M2 model (the input parameters are leaf area index, average temperature, actual water vapor pressure, average relative humidity, net radiation and wind speed), M4 model (the input parameters are leaf area index, average temperature, actual water vapor pressure, average relative humidity, wind speed and soil moisture content) and M12 model (the input parameters are leaf area index, actual water vapor pressure and average relative humidity) had MAE, MBE, R2, 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 M12 had fewer input parameters but higher simulation accuracy, and its MAE, MBE, R2, 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 MⅠ-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 MⅡ-15 model (the input parameters are actual water vapor pressure and average temperature), MⅢ-11 model (the input parameters are average relative humidity、leaf area index and soil moisture content) and MⅣ-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. |
| Key words: sap flow; particle swarm optimization algorithm; extreme learning machine; greenhouse; pear-jujube |
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