使用长短期记忆 (LSTM) 和极端梯度提升 (XGBoost) 进行海洋波预测,用于图班县渔民安全
Riswanda Ayu Dhiya'ulhaq1, Anisya Safira1, Indah Fahmiyah1
1Data Science Technology, Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga, Surabaya 60115, Indonesia.
MethodsX
|December 16, 2024
概括
使用长短期记忆 (LSTM) 和极端梯度提升 (XGBoost) 模型准确预测波浪高度,提高了渔民的安全. LSTM模型在预测多个地点的波浪高度方面表现出卓越的表现,降低了航运事故风险.
科学领域:
- 海洋科学 海洋科学
- 数据科学是数据科学.
- 渔业管理 渔业管理 渔业管理
背景情况:
- 印度尼西亚的渔业在经济上具有重要意义,图班县是其主要贡献者.
- 船舶事故对渔民来说是一个主要的风险,通常受到波浪高度等自然因素的影响.
- 现有的安全法规需要额外的工具来积极减轻风险.
研究的目的:
- 评估长短期记忆 (LSTM) 和极端梯度增强 (XGBoost) 模型在预测波高度方面的有效性.
- 通过提供准确的波浪高度预测来提高渔民的安全性,以便明智作出决策.
- 为了比较LSTM和XGBoost模型的预测准确度,使用网格搜索超参数调整.
主要方法:
- 长短期记忆 (LSTM) 和极端梯度增强 (XGBoost) 机器学习模型的应用.
- 利用网格搜索进行超参数调整以优化模型准确性.
- 评估了与渔业活动相关的四个不同的地理坐标的模型性能.
主要成果:
- 长期短期记忆 (LSTM) 模型在四个位置中的三个显示出优异的预测性能.
- 极端梯度提升 (XGBoost) 在第四个位置取得了最好的结果.
- 在超参数调整后,这两种模型都提供了准确的波高度估计,LSTM显示了更广泛的适用性.
结论:
- 像LSTM和XGBoost这样的先进预测技术显著提高了渔民的海上安全.
- 准确的海浪高度预测使渔民能够在船只出发时做出更安全的决定.
- 该研究强调了机器学习在降低渔业部门船舶事故发生率方面的潜力.
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