冰冻圈科学与冻土工程全国重点实验室

Deep Learning Advances Arctic River Water Temperature Predictions

Chang, Shuyu Y , Schwenk, Jon , Solander, Kurt C

The accelerated warming in the Arctic poses serious risks to freshwater ecosystems by altering streamflow and river thermal regimes. However, limited research on Arctic River water temperatures exists due to data scarcity and the absence of robust methodologies, which often focus on large, major river basins. To address this, we leveraged the newly released, extensive AKTEMP data set and advanced machine learning techniques to develop a Long Short-Term Memory (LSTM) model. By incorporating ERA5-Land reanalysis data and integrating physical understanding into data-driven processes, our model advanced river water temperature predictions in ungauged, snow- and permafrost-affected basins in Alaska. Our model outperformed existing approaches in high-latitude regions, achieving a median Nash-Sutcliffe Efficiency of 0.95 and root mean squared error of 1.0 degrees C. The LSTM model learned air temperature, soil temperature, solar radiation, and thermal radiation-factors associated with energy balance-were the most important drivers of river temperature dynamics. Soil moisture and snow water equivalent were highlighted as critical factors representing key processes such as thawing, melting, and groundwater contributions. Glaciers and permafrost were also identified as important covariates, particularly in seasonal river water temperature predictions. Our LSTM model successfully captured the complex relationships between hydrometeorological factors and river water temperatures across varying timescales and hydrological conditions. This scalable and transferable approach can be potentially applied across the Arctic, offering valuable insights for future conservation and management efforts.

期刊论文 2025-06-01 DOI: 10.1029/2024WR039053 ISSN: 0043-1397

Prediction of pore water pressure generation of liquefiable clean sands under cyclic shear loading through deep learning

Birinci, Omer Tugsad , Ulker, Mehmet Baris Can , Kaya, Gulsen Taskin

In this study, a novel data-driven approach is carried out to predict the pore pressure generation of liquefiable clean sands during cyclic loading. An extensive and comprehensive database of actual stress-controlled cyclic simple shear test results in terms of pore pressure time histories is gathered from a large number of experiments. While the classical machine learning (ML) algorithms help predict the number of liquefaction cycles in a few models, the desired level of accuracy in predicting the actual trend and robustness in pore pressure build-up is only achieved in deep learning (DL) methods. Results indicate that the Long-Short Term Memory (LSTM) working model, employed with Stacked LSTM and the Windowing data processing method, is necessary for making fairly good cyclic pore pressure build-up predictions. This study proposes a model that can ultimately be utilised to predict the pore pressure response of in-situ liquefiable sandy soil layers without resorting to plasticity-based complex theoretical models, which has been the current practice. The robustness achieved in the model reassures the reliability of the study, raising confidence in developing data-driven constitutive models for soils that have the potential to replace conventional plasticity-based theories.

期刊论文 2025-04-17 DOI: 10.1080/17486025.2025.2491493 ISSN: 1748-6025

多年冻土区冻结层上水动态及模拟——以青藏高原风火山流域为例

邹禧 , 王根绪 , 吴碧琼 , 宋春林 , 郭林茂 , 李金龙

冻结层上水是寒区冻土水文循环的关键层，揭示其动态演变规律，对认知冻土区地下水运移机制及精准预测具有重要科学意义。然而，由于多年冻土区原位监测数据的匮乏，以及非线性适应型水文过程模型构建的缺失，冻结层上水动态时空预测精度难以满足科学研究和工程实践需求。本研究以青藏高原风火山小流域（海拔4063～5398 m）为典型研究区，基于2021—2023年原位观测气象数据（精度±0.1℃/±0.1 mm）、逐日土壤水热(精度±1℃/±0.03 m3·m-3)及冻结层上水位（精度±0.14 cm）原位监测数据，揭示坡面尺度冻结层上水动态的水热时空协同机制；集成气温、降水、土壤温湿度和初始水位等多要素，构建及评估基于长短期记忆神经网络（LSTM）的冻土水文预测模型的适应性。研究发现：（1）冻结层上水动态具有显著季节分异特征，其水位波动（年变幅0～1.53 m）与活动层土壤温湿度呈现一致性，基于Boltzmann函数的平均拟合优度为0.90。（2）所构建的基于LSTM方法的冻结层上水位预测模型（学习率0.002）在坡面多梯度验证中表现出卓越性能，平均纳什效率系...