基于机器学习的模拟在北极湖泊的地表水温动态
Hyung Il Kim1,2, Dongkyun Kim3, Mohammad Milad Salamattalab4
1DL E&C, Civil Business Division, Donuimun, D Tower, 134 Tongil-Ro, Jongno-Gu, Seoul, Korea.
Environmental science and pollution research international
|October 3, 2024
概括
北极的湖泊正在迅速变暖. 机器学习模型,特别是长期短期记忆,使用可访问的空气温度数据准确预测湖面水温 (LSWT),帮助气候变化研究.
科学领域:
- 环境科学 环境科学
- 气候科学 气候科学
- 水文学的水文学
背景情况:
- 湖面水温 (LSWT) 对湖泊生态系统至关重要.
- 北极地区的变暖速度快于全球平均水平,需要LSWT监测.
- 在北极地区的卫星和现场LSWT测量受到云层覆盖和可访问性的限制.
研究的目的:
- 开发和评估机器学习模型,用于在北极湖泊中每天进行LSWT预测.
- 为了利用随时可用的空气温度数据用于LSWT建模.
- 为在数据稀缺的北极地区提供可靠的LSWT估计方法.
主要方法:
- 利用了因纳里湖1960-2023年的历史数据.
- 开发了四种机器学习算法:长短期记忆 (LSTM),支持向量回归 (SVR),神经网络 (NN) 和随机森林 (RF).
- 使用确定系数 (R2) 验证的模型性能 (R2).
主要成果:
- 北极空气温度和LSWT都显示出显著的变暖趋势 (分别为0.030°C/年和0.023°C/年).
- LSTM 模型表现出卓越的性能,R2 值在 0.96 到 0.98.9 之间.
- SVR和NN模型表现良好,其次是RF模型.
结论:
- 机器学习模型,特别是LSTM,可以使用空气温度数据准确预测北极LSWT.
- 开发的模型为在数据有限的北极湖泊中进行LSWT估计提供了可行的解决方案.
- 这种方法支持对偏远北极地区的气候变化影响评估.
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