干旱区生态环境知识资源中心(Arid): 基于MEA-BPNN的西北旱区参考作物蒸散量预报模型

Arid

	基于MEA-BPNN的西北旱区参考作物蒸散量预报模型
其他题名	Reference Crop Evapotranspiration Prediction Model of Arid Areas of Northwest China Based on MEA-BPNN
	崔宁博 1; 魏俊 2; 赵璐 1; 张青雯 2; 龚道枝 3; 王明田 4
来源期刊	农业机械学报
ISSN	1000-1298
出版年	2018
卷号	49 期号:8 页码:228-236,307
中文摘要	为有效提高西北旱区参考作物蒸散量(Reference crop evapotranspiration,ET_0)预报精度,在西北旱区选择5个代表性气象站点,构建10种基于思维进化算法(Mind evolutionary algorithm,MEA)优化的误差反向传波神经网络(Back propagation neural network,BPNN)ET_0预报模型,并将其与Hargreaves-Samani模型、Irmak模型和48-PM模型等3种在西北旱区ET_0计算精度较高的模型进行比较。结果表明:在不同输入的情况下MEA-BPNN模型模拟精度具有相对较高水平,其中MEA-BPNN1(输入最高气温T_(max)、最低气温T_(min)、相对湿度R_H、日照时数n和距地面2 m高处的风速u_2 )、MEA-BPNN2(输入T_(max)、T_(min)、n和u_2 )及MEA-BPNN3(输入T_(max)、T_(min)、R_H和u_2 )模型的R~2、NSE均大于0.96,RMSE、MAE也分别小于0.34、0.25 mm/d,以上3种MEA-BPNN模型的整体评价指标(Global performance indicator,GPI)排名分别为1、2、3; MEA-BPNN7(输入T_(max)、T_(min)和u_2)的R~2、NSE分别为0.966 2、0.962 2,RMSE、MAE分别为0.361 0、0.276 1 mm/d,模拟精度较高;MEA-BPNN模型可移植性的分析表明: MEA-BPNN模型在西北旱区具有较强的泛化能力,基于不同站点数据构建的预报模型也有较高精度;在相同输入情况下MEA-BPNN模型模拟精度均高于Hargreaves-Samani模型、Irmak模型和48-PM模型。因此,在气象资料缺乏情景下MEA-BPNN模型可作为西北旱区ET_0计算的推荐模型,可为实时精准灌溉预报的实现提供科学依据。
英文摘要	To effectively improve the prediction accuracy of the reference crop evapotranspiration (ET_0) in the arid regions of Northwest China, five representative meteorological sites were selected in the arid Northwest China to construct 10 errors back propagation neural network (BPNN) optimized by mind evolutionary algorithm (MEA) model. This model was used to forecast ET_0 and compared with the three models of Hargreaves-Samani model, Irmak model and 48-PM model which had higher accuracy in the northwest arid region. The results showed that the simulation accuracy of the MEA-BPNN model was basically high at different input levels, including MEA-BPNN1 (input T_(max), T_(min), R_H, n and u_2), MEA-BPNN2 (input T_(max), T_(min), n and u_2) and MEA-BPNN3 (input T_(max), T_(min), R_H and u_2). The determination coefficient R~2 and Nash-Sutcliffe efficiency coefficient NSE of the models were greater than 0.96, RMSE and MAE was less than 0.34 mm/d and 0.25 mm/d. The GPI rankings of the above three MEA-BPNN models were 1, 2 and 3, respectively. The R~2 and NSE of MEA-BPNN7 (input T_(max), T_(min), and u_2) was 0.966 2 and 0.962 2, RMSE and MAE was 0.361 0 mm/d and 0.276 1 mm/d, respectively, and the simulation accuracy was high. The analysis of the portability of the MEA-BPNN model showed that the MEA-BPNN model in the arid northwestern China had strong generalization ability, and the forecasting model constructed based on different site data also had high accuracy. The simulation accuracy of the MEA-BPNN model was higher than that of the Hargreaves-Samani model, Irmak model and 48-PM model with the same input. Therefore, in the absence of meteorological data, the MEA-BPNN model can be used as a recommended model for the calculation of ET_0 in the northwest arid regions, which can provide a scientific basis for real-time accurate irrigation forecasting.
中文关键词	参考作物蒸散量 ; 预报模型 ; 思维进化 ; 神经网络 ; 西北旱区 ; 可移植性
英文关键词	reference crop evapotranspiration forecast model mind evolutionary neural networks northwest arid area portability
语种	中文
国家	中国
收录类别	CSCD
WOS类目	AGRICULTURE MULTIDISCIPLINARY
WOS研究方向	Agriculture
CSCD记录号	CSCD:6301470
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/238013
作者单位	1.四川大学;;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;;南方丘区节水农业研究四川省重点实验室, 成都;;成都, ;; 610065;;610066; 2.四川大学, 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; 3.中国农业科学院农业环境与可持续发展研究所, 作物高效用水与抗灾减损国家工程实验室, 北京 100081, 中国; 4.中国气象局成都高原气象研究所, 成都, 四川 610071, 中国
推荐引用方式 GB/T 7714	崔宁博,魏俊,赵璐,等. 基于MEA-BPNN的西北旱区参考作物蒸散量预报模型[J],2018,49(8):228-236,307.
APA	崔宁博,魏俊,赵璐,张青雯,龚道枝,&王明田.(2018).基于MEA-BPNN的西北旱区参考作物蒸散量预报模型.农业机械学报,49(8),228-236,307.
MLA	崔宁博,et al."基于MEA-BPNN的西北旱区参考作物蒸散量预报模型".农业机械学报 49.8(2018):228-236,307.