Support Vector Regression Ship Motion Identification Modeling Based on Grey Wolf Optimizer

Meng, Yao; Zhang, Xiufeng; Liu, Zhaochun; Wang, Xiaoxue

doi:10.23919/ccc52363.2021.9549927

Cited by 1 publication

(2 citation statements)

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“…SVR is a support vector machine regression problem extension [41]. Compared with other machine learning approaches, SVR offers the benefits of using fewer samples, obtaining better global optimum solutions, and producing better outcomes when handling multidimensional nonlinear problems [42], [43].…”

Section: ) Theoretical Basis Of Support Vector Regressionmentioning

confidence: 99%

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Prediction Dynamics in Cotton Aphid Using Unmanned Aerial Vehicle Multispectral Images and Vegetation Indices

et al. 2023

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Cotton harvest can be increased by having real-time information on the state of cotton aphid populations. However, traditional cotton aphid monitoring relies on ground sample methods supported by models such as linear regression, resulting in low forecast accuracy. Therefore, this paper purposes to enhance the precision of the remote sensing prediction model by investigating the cotton aphid prediction model construction approach. We explored the effectiveness of the XGBoost algorithm combined with the GWO algorithm and SVR method for cotton aphid prediction relying on vegetation indices derived from UAV multispectral photography. Originally, 12 indices related to cotton aphids were calculated by UAV multispectral reflectance. Additionally, the optimal index combination for pest prediction was determined utilizing analysis of correction and two-way ANOVA, combined with the XGBoost algorithm. Furthermore, a pest prevalence prediction model for cotton aphids was constructed via the SVR methodology associated with the optimal catalog combination, and the model was optimized using the GWO algorithm. Compared with the seven algorithms, experimental results demonstrate that the MSE and MAE of the XGBoost-GWO-SVR model are reduced by 90.20% and 70.36% (SVR), 90.14% and 70.26% (XGBoost-SVR), 7.47% and 0.14% (XGBoost-GA-SVR), 5.80% and 0.11% (XGBoost-PSO-SVR), 12.06% and 58.95% (LR), and 84.77% and 89.22% (BPNN), whereas the R 2 is increased by 22.5% (SVR and XGBoost-SVR), 0.3% (LR), and 12.51% (BPNN). The R 2 of the prediction model of XGBoost-SVR combined with GWO, PSO, and GA is not significantly different. Among these models, the XGBoost-GWO-SVR obtained the highest R 2 of 0.980 and the lowest MAE of 2.838. INDEX TERMScotton aphid, support vector regression, unmanned aerial vehicle, XGBoost algorithm, integrated pest management, grey wolf optimizer I. INTRODUCTION X INJIANG is the largest cotton-producing region in the country. Pests inhibit cotton development throughout the year, resulting in 10-15% yield loss, and pest stress is con-tinuously growing due to global climate change and insufficient ecological and natural management of cotton fields [1]. Cotton aphids are common pests in the cotton industry. Aphid damage results in early leaf development, slow growth, and

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Section: ) Theoretical Basis Of Support Vector Regressionmentioning

confidence: 99%

“…The optimization algorithms of the SVR algorithm are the GA algorithm, PSO algorithm, and GWO algorithm, in which the GWO algorithm can quickly solve the optimal solution of coefficient C and tolerance coefficient ϵ. It has been applied to ship identification, data filtering, and path planning optimization [27]- [29].…”

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confidence: 99%