吉林大学学报(工学版) ›› 2021, Vol. 51 ›› Issue (5): 1798-1807.doi: 10.13229/j.cnki.jdxbgxb20200545

• 计算机科学与技术 • 上一篇    

自构建改进型鲸鱼优化BP神经网络的ET0模拟计算

姚引娣(),贺军瑾,李杨莉,谢荡远,李英   

  1. 西安邮电大学 通信与信息工程学院,西安 710121
  • 收稿日期:2020-07-23 出版日期:2021-09-01 发布日期:2021-09-16
  • 作者简介:姚引娣(1978-),女,高级工程师,硕士生导师.研究方向:物联网和嵌入式系统设计.E-mail:yaoyindi@xupt.edu.cn
  • 基金资助:
    工信部软科学项目(2021-R-47);陕西省科技厅农业项目(2021NY-180);西安市科技计划项目(2019218114GXRC017CG018-GXYD17.2);物联网技术及应用科技创新团队项目(2019TD-028);西安邮电大学研究生创新基金项目(CXJJLY2019060)

ET0 simulation of self⁃constructed improved whale optimized BP neural network

Yin-di YAO(),Jun-jin HE,Yang-li LI,Dang-yuan XIE,Ying LI   

  1. College of Communication and Information Eingineering,Xi'an University of Posts and Telecommunications,Xi'an 710121,China
  • Received:2020-07-23 Online:2021-09-01 Published:2021-09-16

摘要:

参考作物蒸发蒸腾量(ET0)是影响现代水文研究的关键因素,本文建立了一种改进的鲸鱼优化算法(GWOA)和BP神经网络的ET0模型。通过遗传算法中选择机制与自适应的变异因子代替了传统鲸鱼优化算法(WOA)的最佳搜索代理选择,优化了鲸鱼算法种群多样性与跳出局部最优的能力;借助改变鲸鱼算法收敛因子与权重因子的更新策略,明显提高了鲸鱼优化算法拟合精度和收敛速度。在陕西省北部地区5个站点ET0模拟仿真中,将基础气象数据作为要素输入,利用FAO 56 Penman-Monteith(FAO P-M)模型运算结果作为参考值,同时将GWOA-BP模型模拟结果与其他算法模型的模拟结果进行评价参数的对比,在仅有气温数据时,GWOA-BP模型仍能较好反映气象因子与ET0之间的非线性约束关系(平均R2为0.990,平均RMSE为0.287 mm/d),完全可以代替传统模型Hargreavers模型(R2提升了5%,RMSE下降了63%);在不同气象因子输入下的模拟计算数据表明,陕西省北部地区ET0的重要气象因子排序为日最高温度Tmax日最低温度Tmin逐日日照对数n、逐日空气相对湿度RH、逐日平均风速u2;在相同气象因子输入下的模拟计算数据表明,GWOA-BP拟合精度均高于WOA-BP、BP模型。

关键词: 计算机应用, 植物营养学, 参考作物蒸发蒸腾量, 鲸鱼优化算法, Penman-Monteith模型

Abstract:

Reference evapotranspiration (ET0) is a key factor affecting modern hydrological research, and is one of the core variables to be controlled in the process of crop transpiration. An improved genetic whale optimization algorithm (GWOA) and ET0model of BP neural network were established. The traditional whale optimization algorithm was replaced by the selection mechanism and adaptive mutation factor in genetic algorithm, The results show that the optimal search agent selection of WOA optimizes the population diversity and the ability to jump out of the local optimum of the whale algorithm, likewise significantly improves the fitting accuracy and convergence speed of the whale optimization algorithm by changing the convergence factor and weight factor of the whale algorithm. In the ET0 simulation of five stations in Northern Shaanxi Province, the basic meteorological data were input as elements, The calculation results of FAO P-M model were used as reference value, and the simulation results of GWOA-BP model were compared with those of other algorithm models. When there is only temperature data, GWOA-BP model can still reflect the nonlinear constraint relationship between meteorological factors and ET0 (average R2 is 0.990, average RMSE is 0.287 mm/d), which can completely replace the traditional model hargreavers model (R2 increased by 5% and RMSE decreased by 63%);The simulation data under different meteorological factors input show that the important meteorological factors of ET0 in Northern Shaanxi Province are Tmax、Tmin、n、RH and u2; the simulation data under the same meteorological factor input show that the fitting accuracy of GWOA-BP is higher than that of WOA-BP and BP models.

Key words: computer application, plant nutrition, reference evapotranspiration, whale optimization algorithm, Penman-Monteith model

中图分类号: 

  • TP391.9

图1

BP神经网络结构图"

表1

七个基准测试函数"

类型表达式WOAGWOA
F1i=1nx122.10e-0781.24e-309
F2i=1n|xi|+i=1n|xi|9.57e-0565.4e-089
F3max|xi|,1in65.46326.942
F4i=1nxi+0.520.903 20.330 6
F5i=1n-xisin|xi|-8 735-11 910
F6i=1nixi4+random0,10.002 370.000 10
F7i=1nxi2-10cos2πxi+101.13e-0131.13e-013

图2

GWOA与WOA算法对函数F1、F4、F5、F6相同维数的寻优收敛曲线"

表2

不同输入因子的组合及拟合结果"

方案输入因子R2RMSE/(mm·d-1
神木洛川榆林延长定边神木洛川榆林延长定边
GWOA-BP1Tmin、Tmax0.8390.8590.8790.9080.8810.7701.2021.2710.9691.256
GWOA-BP2Tmin、Tmax、RH0.9400.9090.9260.9260.9130.4870.9580.9880.8711.132
GWOA-BP3Tmin、Tmax、u20.9090.8630.8980.9110.8980.6151.1921.1830.9521.111
GWOA-BP4Tmin、Tmax、n0.8910.9450.9430.9750.9470.6380.7470.8650.4940.815
GWOA-BP5Tmin、Tmax、RH、u20.9630.9130.9320.9260.9220.3770.9480.9510.8710.998
GWOA-BP6Tmin、Tmax、u2、n0.9480.9530.9570.9760.9590.4620.6870.7530.4860.708
GWOA-BP7Tmin、Tmax、n、RH0.9670.9700.9700.9810.9660.3880.5530.6310.4290.680
GWOA-BP8Tmin、Tmax、n、RH、u20.9770.9760.9780.9820.9750.2940.4910.5320.4230.549
GWOA-BP9Tmin、Tmax、Ra0.9990.9860.9900.9920.9860.0010.3850.3630.2800.406
HarTmin、Tmax、Ra0.9390.9400.9430.9460.9390.4820.7420.8890.8000.740

图3

测试样本拟合结果对比"

图4

检验期GWOA-BP8、WOA-BP8、BP8模型结果与FAO P-M结果比较的散点图"

表3

三种算法拟合精度"

评价指标GWOA-BPWOA-BPBP
R20.9830.9650.900
RMSE/(mm?d-10.2580.3720.629
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