非线性回归树用于检索海平面测量过程中遗漏的数据
Amin Mahdavi-Meymand1, Dawid Majewski1, Wojciech Sulisz1
1Institute of Hydro-Engineering, Polish Academy of Sciences, Poland.
Journal of environmental management
|December 25, 2024
概括
本研究引入了非线性回归树 (NRT),以准确地恢复缺失的海面位移 (SSD) 数据. NRT-Support Vector Regression (NRT-SVR) 模型显著优于其他方法,为环境数据缺口提供了可靠的解决方案.
科学领域:
- 环境工程 环境工程
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 海洋表面移位 (SSD) 在环境工程中至关重要.
- 由于仪器故障和不可预测事件,SSD测量容易丢失数据.
- 准确的SSD数据对于环境监测和分析至关重要.
研究的目的:
- 开发一种创新的非线性回归树 (NRT) 技术来检索丢失的SSD数据.
- 为了比较基于支持向量回归 (SVR) 和自适应神经模糊推理系统 (ANFIS) 的NRT模型的性能.
- 根据环境数据归算的最新算法验证开发的模型.
主要方法:
- 开发了两个NRT模型:NRT-SVR和NRT-ANFIS.
- 在回归树的末节点上利用非线性机器学习算法.
- 训练模型使用声波多普勒电流选器 (ADCP) 的压力和SSD数据作为输入参数.
- 对随机森林 (RF) 和其他现有算法进行验证的模型性能.
主要成果:
- 与随机森林相比,NRT方法在预测准确度上有71.13%的改进,平均RMSE为0.019 m.
- 该NRT-SVR模型实现了最高的准确性,显示了最低的RMSE (0.009米) 和MAE (0.002米).
- NRT-SVR还记录了优异的性能指标,包括R2 (0.997),NSE (0.997) 和IA (0.999).
结论:
- 开发的NRT模型提供了一种简单而高效的方法来归纳缺失的SSD数据.
- NRT-SVR被认为是SSD数据检索中最准确的模型.
- 该NRT技术显示了在各种环境和工程挑战的模式识别中应用的潜力.
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