| 摘要: |
| 为预测海上养殖装备灾害经济损失,保障海上养殖业的健康发展，文章对福建省沿海地区海上养殖装备损失进行分析和预测。首先，选取了福建沿海地区海上养殖装备的25个相关变量作为研究对象，并使用主成分分析法（PCA）对数据进行降维，从中提取出了4个主要影响损失率的主成分。然后，采用贝叶斯优化支持向量回归（Support Vector Regression，SVR）预测模型，对海上养殖装备经济损失进行预测，并与BP神经网络模型、随机森林算法以及K近邻算法的预测结果进行对比。结果表明，SVR模型在预测海上养殖装备灾害损失方面表现优异，平均相对误差低至0.116%。该模型在应对海上养殖装备经济损失数据中存在的非线性特点时表现出了较好的准确性和鲁棒性，为海上承灾体灾害损失预测提供了模型参考。 |
| 关键词: 灾害损失 海上养殖装备 主成分分析法 贝叶斯优化 支持向量回归 |
| DOI：10.20016/j.cnki.hykfygl.2025.07.001 |
| 投稿时间：2024-08-17修订日期：2025-07-09 |
| 基金项目:北斗三代船载终端“感-传-算-用”一体化关键技术研究与示范应用（FJHYF-ZH-2023-01）. |
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| Predicting the Losses of Marine Aquaculture Equipment Due to Disasters Along the Fujian Coastline Using a Bayesian-Optimized SVR Model |
| LI Xurui,DONG Dibo,GUO Qiaoying |
| Institute of Smart Marine and Engineering, Fujian University of Technology |
| Abstract: |
| To predict the economic losses caused by disasters affecting marine aquaculture equipment and to ensure the healthy development of the marine aquaculture industry, this study analyzes and forecasts the losses of marine aquaculture equipment in the coastal areas of Fujian Province. First, 25 variables related to marine aquaculture equipment in the Fujian coastal region were selected as the research subjects, and the Principal Component Analysis method was used for dimensionality reduction, extracting four principal components that significantly affect the loss rate. Next, a Bayesian-optimized Support Vector Regression (SVR) model was employed to predict the disaster losses of marine aquaculture equipment. The results were compared with those of the BP Neural Network model, Random Forest algorithm, and K-Nearest Neighbors algorithm. The results indicate that the SVR model performed excellently in predicting marine aquaculture equipment disaster losses, with an average relative error as low as 0.116%. The model demonstrated good accuracy and robustness in addressing the nonlinear characteristics of economic loss data related to marine aquaculture equipment, providing a reference model for predicting disaster losses of marine vulnerable assets. |
| Key words: Disaster loss, Marine aquaculture equipment, Principal Component Analysis (PCA), Bayesian optimization,Support Vector Regression (SVR) |
