[1]尹起,周建平,许燕,等.基于粒子群优化（PSO）超限学习机预测新疆参考作物蒸散量[J].江苏农业学报,2021,(03):622-631.[doi:doi:10.3969/j.issn.1000-4440.2021.03.010]
　YIN Qi,ZHOU Jian-ping,XU Yan,et al.Prediction of reference crop evapotranspiration in Xinjiang based on particle swarm optimization(PSO) optimized extreme learning machine[J].,2021,(03):622-631.[doi:doi:10.3969/j.issn.1000-4440.2021.03.010]
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基于粒子群优化（PSO）超限学习机预测新疆参考作物蒸散量()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2021年03期
页码:
622-631
栏目:
耕作栽培·资源环境
出版日期:
2021-06-30

文章信息/Info

Title:
Prediction of reference crop evapotranspiration in Xinjiang based on particle swarm optimization(PSO) optimized extreme learning machine
作者:
尹起1; 周建平1; 许燕1; 李志磊2; 樊湘鹏1; 魏禹同1
（1.新疆大学机械工程学院，新疆乌鲁木齐830000;2.新疆大学工程训练中心，新疆乌鲁木齐830000）
Author(s):
YIN Qi1; ZHOU Jian-ping1; XU Yan1; LI Zhi-lei2; FAN Xiang-peng1; WEI Yu-tong1
(1.College of Mechanical Engineering，Xinjiang University，Urumqi 830000，China;2.Engineering Training Center，Xinjiang University，Urumqi 830000，China)
关键词:
新疆; 粒子群优化; 超限学习机; 参考作物蒸散量; 模型精度
Keywords:
Xinjiang; particle swarm optimization; extreme learning machine; reference crop evapotranspiration; model accuracy
分类号:
S27；TP312
DOI:
doi:10.3969/j.issn.1000-4440.2021.03.010
文献标志码:
A
摘要:
参考作物蒸散量(ET0)的准确预测对于作物需水量预测、农田精准灌溉和提高水资源利用效率等具有重要意义。为了解决传统方法获取ET0的弊端，本研究基于粒子群优化（Particle swarm optimization，PSO）-超限学习机（Extreme learning machine，ELM）预测ET0。通过选取新疆地区3个站点（乌鲁木齐、喀什、哈密）的最高气温（Tmax）、最低气温（Tmin）、平均相对湿度（RH）、风速（u2）、光照时间（n）等气象数据，建立PSO-ELM预测模型，对模型精度和普适性进行研究，并通过与ELM、Makkink、I-A模型的对比，探究不同气象因子组合模型的预测精度。结果表明，PSO-ELM模型在5种气象因子输入下具有最高预测精度（平均R2=0.974 7,平均MAE=0.252 0 mm/d,平均RMSE=0.364 3 mm/d）。由PSO-ELM6模型与ELM、Makkink、I-A模型的对比结果看出，在相同的气象因子输入条件下，3个站点用PSO-ELM6模型预测的效果最好。通过对PSO-ELM3模型在新疆地区普适性的研究发现，该模型具有较高的预测精度（平均R2=0.946 5,平均MAE=0.307 0 mm/d,平均RMSE=0.356 9 mm/d）。不同站点、不同气象因子输入的PSO-ELM模型能够较为精准地反映气象因子与ET0之间复杂的非线性关系，且模型在新疆地区的普适性较好，可以为新疆地区逐日ET0预测提供新的方法。
Abstract:
Accurate prediction of reference crop evapotranspiration (ET0) is of great significance in predicting crop water demand, precise irrigation of farmland and improving water resource utilization efficiency. To solve the disadvantages of traditional methods in obtaining ET0, ET0 was predicted based on particle swarm optimization (PSO)-extreme learning machine (ELM) in this study. By selecting meteorological data such as maximum temperature (Tmax), minimum temperature (Tmin), average relative humidity (RH), wind speed (u2) and illumination time (n) of three stations in Xinjiang (Urumqi, Kashgar and Hami), the PSO-ELM prediction model was established. The accuracy and universality of the model was studied, and the prediction accuracy of models combined with different meteorological factors was explored by comparing with ELM, Makkink and I-A models. The results showed that, PSO-ELM model showed the highest prediction accuracy under the input condition of five meteorological factors (average R2=0.974 7, average mean absolute error=0.252 0 mm/d, average root mean square error=0.364 3 mm/d). The prediction effect of PSO-ELM6 model was the best under the same meteorological factor input conditions of three stations by comparing the PSO-ELM6 model with ELM, Makkink, I-A models. The research on the universality of PSO-ELM3 model in Xinjiang showed that, the model had high prediction accuracy (average R2=0.946 5, average mean absolute error=0.307 0 mm/d, average root mean square error=0.356 9 mm/d). The PSO-ELM model with different meteorological inputs at different stations can accurately reflect the complex non-linear relationship between meteorological factors and ET0, and the model shows good generalizability in Xinjiang, which can provide new methods for daily ET0 prediction in Xinjiang.

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备注/Memo

备注/Memo:
收稿日期：2020-08-27基金项目：国家级大学生创新创业训练计划项目（201810755079S）；新疆维吾尔自治区研究生科研创新项目（XJ2019G033）作者简介：尹起（1994-），男，河南新乡人，硕士研究生，主要从事智慧农业灌溉技术研究。（E-mail）794760934@qq.com通讯作者：周建平，（E-mail）linkzhou@163.com
更新日期/Last Update: 2021-07-05