自构建改进型鲸鱼优化BP神经网络的ET0模拟计算

doi:10.13229/j.cnki.jdxbgxb20200545

Abstract

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 R_MSE is 0.287 mm/d）， which can completely replace the traditional model hargreavers model （R²increased by 5% and R_MSE decreased by 63%）；The simulation data under different meteorological factors input show that the important meteorological factors of ET₀ in Northern Shaanxi Province are T_max、T_min、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

CLC Number:

TP391.9

Yin-di YAO,Jun-jin HE,Yang-li LI,Dang-yuan XIE,Ying LI. ET₀ simulation of self⁃constructed improved whale optimized BP neural network[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1798-1807.

Figures/Tables 7

Fig.1

Table 1

Seven benchmark functions"

类型	表达式	WOA	GWOA
F₁	$∑ i = 1 n x 12$	2.10e-078	1.24e-309
F₂	$∑ i = 1 n \| x i \| + ∏ i = 1 n \| x i \|$	9.57e-056	5.4e-089
F₃	$m a x \| x i \|, 1 ≤ i ≤ n$	65.463	26.942
F₄	$∑ i = 1 n x i + 0.5 2$	0.903 2	0.330 6
F₅	$∑ i = 1 n - x i s i n \| x i \|$	-8 735	-11 910
F₆	$∑ i = 1 n i x i 4 + r a n d o m 0,1$	0.002 37	0.000 10
F₇	$∑ i = 1 n x i 2 - 10 c o s 2 π x i + 10$	1.13e-013	1.13e-013

Table 1

Fig.2

Table 2

Fig.3

Fig.4

Table 3

Fitting accuracy of three algorithms"

评价指标	GWOA-BP	WOA-BP	BP
R²	0.983	0.965	0.900
R_MSE/（ $m m ? d - 1$ ）	0.258	0.372	0.629

Table 3

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方案	输入因子	R²					R_MSE/（mm·d^-1）
方案	输入因子	神木	洛川	榆林	延长	定边	神木	洛川	榆林	延长	定边
GWOA-BP1	T_min、T_max	0.839	0.859	0.879	0.908	0.881	0.770	1.202	1.271	0.969	1.256
GWOA-BP2	T_min、T_max、RH	0.940	0.909	0.926	0.926	0.913	0.487	0.958	0.988	0.871	1.132
GWOA-BP3	T_min、T_max、u₂	0.909	0.863	0.898	0.911	0.898	0.615	1.192	1.183	0.952	1.111
GWOA-BP4	T_min、T_max、n	0.891	0.945	0.943	0.975	0.947	0.638	0.747	0.865	0.494	0.815
GWOA-BP5	T_min、T_max、RH、u₂	0.963	0.913	0.932	0.926	0.922	0.377	0.948	0.951	0.871	0.998
GWOA-BP6	T_min、T_max、u₂、n	0.948	0.953	0.957	0.976	0.959	0.462	0.687	0.753	0.486	0.708
GWOA-BP7	T_min、T_max、n、RH	0.967	0.970	0.970	0.981	0.966	0.388	0.553	0.631	0.429	0.680
GWOA-BP8	T_min、T_max、n、RH、u₂	0.977	0.976	0.978	0.982	0.975	0.294	0.491	0.532	0.423	0.549
GWOA-BP9	T_min、T_max、R_a	0.999	0.986	0.990	0.992	0.986	0.001	0.385	0.363	0.280	0.406
Har	T_min、T_max、R_a	0.939	0.940	0.943	0.946	0.939	0.482	0.742	0.889	0.800	0.740

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

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

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