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

Xiaoqiang ZHAO; Fan YANG; Zhufeng YAN

doi:10.11959/j.issn.2096-3750.2021.00192

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

Theory and Technology | 更新时间：2024-06-05

- Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm
- Chinese Journal on Internet of Things Vol. 5, Issue 1, Pages: 99-107(2021)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2021.00192
  CLC： TP181
- Published：30 March 2021，
  
  Published Online：2021-03，
- 稿件说明：
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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.
DOI：

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. DOI： 10.11959/j.issn.2096-3750.2021.00192.

摘要

针对传统土壤含水量预测算法存在的精度和效率较低等问题，采用支持向量机（SVM

support vector machine）建立预测模型，提出一种改进樽海鞘群算法（SSA

salp swarm algorithm）优化SVM的土壤含水量预测算法。首先，引入反向学习和混沌优化对标准樽海鞘群算法进行改进，解决算法易陷入局部最优解和收敛速度慢的问题；其次，利用改进的樽海鞘群算法对影响 SVM 性能的参数进行优化，构建对应的预测模型；最后，将所提模型与粒子群优化SVM预测模型、鲸鱼算法优化SVM预测模型进行对比。实验结果表明，所提模型的均方误差和决定系数分别为0.42和0.901，与其他两种模型相比性能更优，验证了所提算法的有效性。

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.

关键词

土壤含水量预测支持向量机樽海鞘群算法反向学习混沌优化

Keywords

soil water content predictionsupport vector machinesalp swarm algorithmopposition-based learningchaotic optimization

references

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