基于气候测量的区域平均海平面建模,采用堆叠组合方法
Mohamed T Elnabwy1,2, Mosbeh R Kaloop3,4,5, Emad Elbeltagi6
1Coastal Research Institute (CoRI), National Water Research Center, Alexandria, Egypt.
Environmental monitoring and assessment
|January 19, 2026
概括
软计算模型使用气象数据准确预测平均海平面 (MSL) 变化. 随机森林,KNN和高斯过程回归模型表现出强的表现,整体模型实现了海岸弹性高精度.
科学领域:
- 环境科学 环境科学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 鉴于气候变化的影响,评估平均海平面 (MSL) 变化至关重要.
- 软计算为传统的MSL估计方法提供了高效的替代方案.
- 关于应用软计算来分析气候变化对MSL影响的研究有限.
研究的目的:
- 开发和比较软计算技术来建模MSL波动.
- 用气象数据来预测MSL变化.
- 在埃及的达米埃塔站评估模型的有效性.
主要方法:
- 采用随机森林 (RF),支持向量回归 (SVR),K-最近邻居 (KNN),深度神经网络 (DNN),高斯过程回归 (GPR) 和堆叠组合方法.
- 使用的环境变量包括地表水温,空气温度,湿度和风的属性.
- 使用相关系数 (R) 和正常化根平均平方误差 (RMSE) 来统计评估模型性能.
主要成果:
- 在训练和测试期间,RF,KNN和GPR模型在MSL建模中表现出卓越的性能.
- 集成RF,KNN和GPR的加权堆叠组合模型实现了0.88的相关系数 (R) 和0.056米的RMSE.
- 对于水温,风速/方向和大气压力,MSL建模灵敏度最高.
结论:
- 开发的软计算模型为MSL预测提供了一个强大的框架.
- 这种方法对于潮记录有限的沿海地区是有价值的,有助于沿海的弹性.
- 该研究通过加强沿海适应战略,为联合国教科文组织的海洋十年挑战5做出了贡献.
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