基于改进樽海鞘群寻优SVM的土壤含水量预测算法

doi:10.11959/j.issn.2096-3750.2021.00192

Abstract

Abstract:

Aiming at the problems of low accuracy and low efficiency of traditional soil water content prediction methods, support vector machine (SVM) was used to establish a prediction model, and the soil water content prediction method based on SVM optimized was proposed by the improved salp swarm algorithm.Firstly, the opposition-based learning and chaotic optimization were introduced to improve the standard salp swarm algorithm to solve the problem that the algorithm was easy to fall into the local optimal solution and its convergence speed was slow.Secondly, the improved salp swarm algorithm was used to optimize the parameters that affect the performance of SVM and the corresponding prediction model was built.Finally, the proposed model was compared with the particle swarm optimization SVM and the whale algorithm optimized SVM prediction model.The experimental results show that the mean square error and decision coefficient of the proposed model are 0.42 and 0.901, which are better than the other two models which verified the effectiveness of the proposed method.

Key words: soil water content prediction, support vector machine, salp swarm algorithm, opposition-based learning, chaotic optimization

CLC Number:

TP181

Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN. Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm[J]. Chinese Journal on Internet of Things, 2021, 5(1): 99-107.

Figures/Tables 8

函数名称	计算式	范围	最优值
Sphere	$F_{1} (x) = \sum_{i = 1}^{n} x_{i}^{2}$	[-100,100]	0
Rastrigin	$F_{9} (x) = \sum_{i = 1}^{n} [x_{i}^{2} - 10 \cos (2 π x_{i}) + 10]$	[-5.12,5.12]	0
Griewank	$F_{11} (x) = \frac{1}{4 000} \sum_{i = 1}^{n} x_{i}^{2} - \prod_{i = 1}^{n} \cos (\frac{x_{i}}{\sqrt{i}}) + 1$	[-600,600]	0
Penalize2	$\begin{array}{l} F_{13} (x) = 0.1 {\sin^{2} (3 π x_{1}) + \sum_{i = 1}^{n} {(x_{i} - 1)}^{2} [1 + \sin^{2} (3 π x_{i} + 1)] + \\ {(x_{n} - 1)}^{2} [1 + \sin^{2} (2 π x_{n})]} + \sum_{i = 1}^{n} u (x_{i}, 5, 100, 4) \end{array}$	[-50,50]	0

References 13

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函数名称	统计值	OCSSA	PSO	SSA	WOA
Sphere	最优值	0	4.13×10^-6	4.15×10^-9	3.68×10^-88
	平均值	0	2.81×10^-5	5.69×10^-9	9.25×10^-78
	标准差	0	2.88×10^-5	1.74×10^-9	1.81×10^-77
Rastrigin	最优值	0	2.60×10¹	2.98×10¹	0
	平均值	0	3.23×10¹	4.76×10¹	5.68×10^-15
	标准差	0	5.90	9.96	1.04×10^-14
Griewank	最优值	0	3.94×10^-2	4.89×10^-7	0
	平均值	0	1.83×10^-1	6.41×10^-3	0
	标准差	0	1.18×10^-1	6.33×10^-3	0
Penalize2	最优值	6.00×10^-12	2.25×10^-7	1.10×10^-2	4.47×10^-2
	平均值	9.00×10^-12	2.10×10^-6	1.43×10^-2	1.81×10^-1
	标准差	1.69×10^-12	1.09×10^-6	6.62×10^-3	1.02×10^-1

时间	空气温度/℃	空气湿度	土壤温度/℃	土壤含水量	光照强度/lx
2019/9/9 12:00	40.4	47.6%	32.1	27.6%	23.9
2019/9/10 14:00	41.0	44.5%	34.3	26.4%	28.0
2019/9/11 16:00	41.4	38.9%	34.4	24.9%	31.8
2019/9/12 18:00	37.4	47.2%	32.2	24.4%	1.1
…	…	…	…	…	…
2019/9/23 13:00	40.4	17.3%	31.1	22.8%	23.3
2019/9/24 16:00	39.1	16.7%	31.7	22.0%	28.4

运行次数	模型参数C	模型参数γ	MSE	R²
1	14.843 0	7.356 1	0.42	0.903
2	20.999 9	7.047 6	0.39	0.909
3	17.957 1	6.583 7	0.44	0.897
4	9.728 3	5.233 8	0.44	0.897
5	17.326 9	6.878 5	0.43	0.901

模型名称	MSE	R²
OCSSA-SVM	0.42	0.901
SSA-SVM	0.59	0.864
PSO-SVM	0.67	0.845
WOA-SVM	0.62	0.859

Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm

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