College of Life Science and Technology, Jinan University, Guangzhou 510632, China.
College of Life Science and Technology, Jinan University, Guangzhou 510632, China; Green Development Institute of Zhaoqing, Zhaoqing, China.
Sci Total Environ. 2022 Sep 10;838(Pt 1):155886. doi: 10.1016/j.scitotenv.2022.155886. Epub 2022 May 13.
An accurate estimation of thaw depth is critical to understanding permafrost changes due to climate warming on the Qinghai-Tibetan Plateau (QTP). However, previous studies mainly focused on the interannual changes of active layer thickness (ALT) across the QTP, and little is known about the changes in the seasonal thaw depth. Machine learning (ML) is a critical tool to accurately estimate the ALT of permafrost, but a direct comparison of ML with deep learning (DL) in ALT projection regarding the model performance is still lacking. Here, ML, namely random forest (RF), and DL algorithms like convolutional neural networks (CNN) and long short-term memory (LSTM) neural networks were compared to estimate the interannual changes of ALT and seasonal thaw depth on the QTP. Meteorological series, in-situ collected ALT observations, and geospatial information were used as predictors. The results show that both ML and DL methods are capable of estimating ALT and seasonal thaw depth in permafrost areas. The CNN and LSTM models developed using longer lagging times exhibit better performance in thaw depth prediction while the RF models are either mediocre or sometimes even worse as the lagging time increases. The results show that the ALT from 2003 to 2011 on the QTP exhibits an increasing trend, especially in the northern region. In addition, 68.8%, 88.7%, 52.5%, and 47.5% of the permafrost regions on the QTP have deepened seasonal thaw depth in spring, summer, autumn, and winter, respectively. The correlation between air temperature and permafrost thaw depth ranges from 0.65 to 1 with the time lag ranging from 1 to 32 days. This study shows that ML and DL can be effectively used in retrieving ALT and seasonal thaw depth of permafrost, and could present an efficient way to figure out the interannual and seasonal variations of permafrost conditions under climate warming.
准确估计融深对于理解青藏高原(QTP)气候变暖导致的多年冻土变化至关重要。然而，以前的研究主要集中在青藏高原多年冻土活动层厚度(ALT)的年际变化上，而对季节性融深变化知之甚少。机器学习(ML)是准确估计多年冻土 ALT 的重要工具，但在 ALT 预测方面，ML 与深度学习(DL)的模型性能直接比较仍然缺乏。在这里，将机器学习，即随机森林(RF)，和深度学习算法，如卷积神经网络(CNN)和长短期记忆(LSTM)神经网络，进行了比较，以估计青藏高原的多年冻土 ALT 和季节性融深的年际变化。气象系列、现场收集的 ALT 观测值和地理空间信息被用作预测因子。结果表明，ML 和 DL 方法都能够估计多年冻土地区的 ALT 和季节性融深。使用较长滞后时间开发的 CNN 和 LSTM 模型在融深预测方面表现出更好的性能，而 RF 模型在滞后时间增加时表现不佳，有时甚至更差。结果表明，青藏高原 2003 年至 2011 年的 ALT 呈增加趋势，尤其是在北部地区。此外，青藏高原上 68.8%、88.7%、52.5%和 47.5%的多年冻土区在春季、夏季、秋季和冬季的季节性融深分别加深。气温与多年冻土融深之间的相关性在 0.65 到 1 之间，时间滞后在 1 到 32 天之间。本研究表明，ML 和 DL 可有效地用于获取多年冻土的 ALT 和季节性融深，为在气候变暖条件下确定多年冻土条件的年际和季节性变化提供了一种有效的方法。
