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学者姓名：谢秀栋
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Abstract ：
Landslide susceptibility evaluation plays an important role in disaster prevention and reduction. Feature-based transfer learning (TL) is an effective method for solving landslide susceptibility mapping (LSM) in target regions with no available samples. However, as the study area expands, the distribution of landslide types and triggering mechanisms becomes more diverse, leading to performance degradation in models relying on landslide evaluation knowledge from a single source domain due to domain feature shift. To address this, this study proposes a Multi-source Domain Adaptation Convolutional Neural Network (MDACNN), which combines the landslide prediction knowledge learned from two source domains to perform cross-regional LSM in complex large-scale areas. The method is validated through case studies in three regions located in southeastern coastal China and compared with single-source domain TL models (TCA-based models). The results demonstrate that MDACNN effectively integrates transfer knowledge from multiple source domains to learn diverse landslide-triggering mechanisms, thereby significantly reducing prediction bias inherent to single-source domain TL models, achieving an average improvement of 16.58% across all metrics. Moreover, the landslide susceptibility maps generated by MDACNN accurately quantify the spatial distribution of landslide risks in the target area, providing a powerful scientific and technological tool for landslide disaster management and prevention. (c) 2025 China University of Geosciences (Beijing) and Peking University. Published by Elsevier B.V. on behalf of China University of Geosciences (Beijing). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keyword ：
Data scarcity Data scarcity Deep learning Deep learning Feature domain adaptation Feature domain adaptation Landslide susceptibility Landslide susceptibility MDACNN MDACNN
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| GB/T 7714 | Su, Yan , Fu, Jiayuan , Lai, Xiaohe et al. Complex cross-regional landslide susceptibility mapping by multi-source domain transfer learning [J]. | GEOSCIENCE FRONTIERS , 2025 , 16 (4) . |
| MLA | Su, Yan et al. "Complex cross-regional landslide susceptibility mapping by multi-source domain transfer learning" . | GEOSCIENCE FRONTIERS 16 . 4 (2025) . |
| APA | Su, Yan , Fu, Jiayuan , Lai, Xiaohe , Lin, Chuan , Zhu, Lvyun , Xie, Xiudong et al. Complex cross-regional landslide susceptibility mapping by multi-source domain transfer learning . | GEOSCIENCE FRONTIERS , 2025 , 16 (4) . |
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The construction of an interval prediction model capable of explaining deformation uncertainties is crucial for the long-term safe operation of dams. High effective coverage and narrow interval coverage widths are two key benchmarks to ensure that the prediction interval (PI) can accurately quantify deformation uncertainties. The vast majority of existing models neglect to control the interval coverage width, and overly wide PIs can cause decision confusion when operators are developing safety plans for hydraulic structures. To address this problem, this paper proposes a novel interval prediction model combining bidirectional long-short-term memory network (Bi-LSTM) and split conformal quantile prediction (SCQP) for dam deformation prediction. The model uses Bi-LSTM as a benchmark regressor to extract and explain the nonlinear feature of dam deformation in the continuous time domain. SCQP is used to quantify the uncertainties in dam deformation prediction to ensure that the constructed PI can achieve high effective coverage while further improving the accuracy of the quantification of deformation uncertainties. The effectiveness of the proposed model is validated using deformation monitoring data collected from an arch dam in China. The results show that the average prediction interval effective coverage (PICP) of the proposed model is as high as 0.951 while the mean prediction interval width (MPIW) and coverage width-based criterion (CWC) are both only 5.815 mm. Compared with other models, the proposed method can construct higher-quality PIs, thus providing a better service for the safety assessment of dams.
Keyword ：
Bi-LSTM Bi-LSTM dam deformation prediction dam deformation prediction interval prediction interval prediction split conformal quantile prediction split conformal quantile prediction uncertainty quantification uncertainty quantification
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| GB/T 7714 | Su, Yan , Fu, Jiayuan , Lin, Weiwei et al. Dam Deformation Monitoring Model Based on Deep Learning and Split Conformal Quantile Prediction [J]. | APPLIED SCIENCES-BASEL , 2025 , 15 (4) . |
| MLA | Su, Yan et al. "Dam Deformation Monitoring Model Based on Deep Learning and Split Conformal Quantile Prediction" . | APPLIED SCIENCES-BASEL 15 . 4 (2025) . |
| APA | Su, Yan , Fu, Jiayuan , Lin, Weiwei , Lin, Chuan , Lai, Xiaohe , Xie, Xiudong . Dam Deformation Monitoring Model Based on Deep Learning and Split Conformal Quantile Prediction . | APPLIED SCIENCES-BASEL , 2025 , 15 (4) . |
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Detecting water head is crucial in groundwater utilization, and requires quick and accurate solutions. This study employs a method combining the collocation Trefftz method (CTM) and the fictitious time integration method (FTIM) for groundwater head detection and restoration. The Laplace equation is solved using CTM, and the Trefftz basis function is linearly combined to fit the exact solution while adding characteristics length for numerical stability. The FTIM solves the nonlinear algebraic equations system for water head detection. Numerical examples quantify the method's accuracy, and boundary restoration results are compared with the Picard successive approximation method, showcasing FTIM's advantages in convergence steps and precision. The solution comparison under irregular boundary conditions further verifies the proposed method's efficacy (MAE <= 10-15). The CTM-FTIM calculated water level boundary aligns with the actual boundary, and its noise immunity is verified using real observation well data (MAE <= 10-2, Itertimes <= 5000). The CTM-FTIM method eliminates meshing needs for quick solutions in irregular regions, accurately determining water levels in the study domain using few known boundary points, solving infinite domain groundwater head detection.
Keyword ：
Fictitious time integration method Fictitious time integration method Groundwater head detection recovery Groundwater head detection recovery Noise immunity Noise immunity Observation well data Observation well data Trefftz method Trefftz method
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| GB/T 7714 | Su, Yan , Huang, Bin , Yang, Lingjun et al. Study on the detection of groundwater boundary based on the Trefftz method [J]. | NATURAL HAZARDS , 2024 , 120 (8) : 8057-8085 . |
| MLA | Su, Yan et al. "Study on the detection of groundwater boundary based on the Trefftz method" . | NATURAL HAZARDS 120 . 8 (2024) : 8057-8085 . |
| APA | Su, Yan , Huang, Bin , Yang, Lingjun , Lai, Xiaohe , Lin, Chuan , Xie, Xiudong et al. Study on the detection of groundwater boundary based on the Trefftz method . | NATURAL HAZARDS , 2024 , 120 (8) , 8057-8085 . |
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Given the time-consuming nature of compiling landslide inventories, it is increasingly important to develop transferable landslide susceptibility models that can be applied to regions without existing data. In this study, we propose a feature-based domain adaptation method to improve the transferability of landslide susceptibility models, especially in "no sample" areas. Two typical landslide-prone areas in Fujian province, southeastern China, were chosen as research cases to test the practicality of the transfer effect. Five conventional machine learning algorithms (Support vector machines (SVM), Random Forest (RF), Logistic Regression (LOG), K-nearest neighbor (KNN), and Decision tree (C4.5)) are used to model landslide susceptibility in sampled areas (source domain), and a feature transfer-based landslide susceptibility evaluation model is constructed under coupled feature transfer methods to evaluate the susceptibility of landslide in un-sampled areas (target domain). The results showed that feature transfer can effectively improve the transferability of different machine learning models for cross-regional prediction (The indicators have improved overall by 8.49%), with SVM (increased by 13.68%) and LOG (increased by 10.19%) models showing the most significant improvements. The feature-based domain adaptive method can alleviate the burden of collecting and labeling new data, and effectively improve the assessment performance of machine learning-based landslide susceptibility models in un-sampled areas. This is a new solution for landslide susceptibility assessment in completely "no sample" areas. (c) 2024 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.
Keyword ：
Data scarcity Data scarcity Feature domain adaptation Feature domain adaptation Landslide susceptibility Landslide susceptibility Machine learning Machine learning Reservoir bank slope Reservoir bank slope
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| GB/T 7714 | Su, Yan , Chen, Yaoxin , Lai, Xiaohe et al. Feature adaptation for landslide susceptibility assessment in "no sample" areas [J]. | GONDWANA RESEARCH , 2024 , 131 : 1-17 . |
| MLA | Su, Yan et al. "Feature adaptation for landslide susceptibility assessment in "no sample" areas" . | GONDWANA RESEARCH 131 (2024) : 1-17 . |
| APA | Su, Yan , Chen, Yaoxin , Lai, Xiaohe , Huang, Shaoxiang , Lin, Chuan , Xie, Xiudong . Feature adaptation for landslide susceptibility assessment in "no sample" areas . | GONDWANA RESEARCH , 2024 , 131 , 1-17 . |
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Purpose The macropore structure and seepage characteristics profoundly influence the stability of granite residual soil (GRS) slopes. However, accurately predicting the permeability of undisturbed GRS (U-GRS) is challenging owing to its complex and susceptible pore structure. Aims and methods Employing X-ray computed tomography (CT) technologies, a three-dimensional (3D) pore structure of U-GRS, was established. Permeability prediction for U-GRS samples was conducted using three simulation methods, namely, the pore network model (PNM), finite element method (FEM), and the lattice Boltzmann method (LBM), along with two empirical models (EMs)-specifically, Kozeny-Carman (K-C) and Katz-Thompson (K-T) models. Subsequently, the methods were comparatively analyzed for calculating efficiency and accuracy. Finally, a piecewise permeability prediction model (PPPM) for U-GRS based on the CT-LBM was proposed. Results The ranking of permeability estimation methods in terms of accuracy was as follows: LBM > PNM > FEM > EMs. Substantial disparity was observed in the permeabilities obtained using both FEM and EMs compared to other methods, which exhibited a deviation of up to six orders of magnitude. The PPPM demonstrated smaller prediction deviations than the EMs, with its accuracy influenced by the strategy for selecting calculation parameters. Conclusion The CT-LBM, which uses real pore structures, was employed to estimate the permeability of U-GRS. The PPPM, established based on this method, was found to be applicable for estimating U-GRS permeability.
Keyword ：
Comparative analysis Comparative analysis Granite residual soil Granite residual soil Macropore Macropore Permeability model Permeability model X-ray computed tomography images X-ray computed tomography images
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| GB/T 7714 | Que, Yun , Chen, Xian , Jiang, Zhenliang et al. Pore-scale permeability estimation of undisturbed granite residual soil: A comparison study by different methods [J]. | JOURNAL OF SOILS AND SEDIMENTS , 2024 . |
| MLA | Que, Yun et al. "Pore-scale permeability estimation of undisturbed granite residual soil: A comparison study by different methods" . | JOURNAL OF SOILS AND SEDIMENTS (2024) . |
| APA | Que, Yun , Chen, Xian , Jiang, Zhenliang , Cai, Peichen , Xue, Bin , Xie, Xiudong . Pore-scale permeability estimation of undisturbed granite residual soil: A comparison study by different methods . | JOURNAL OF SOILS AND SEDIMENTS , 2024 . |
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It is crucial to create a 'migratable' landslide susceptibility model considering the time-consuming process of recording landslides. However, a sample set of all known landslide data areas must be used in forecasting the susceptibility of unsampled regions adequately. In this paper, we attempt to develop a TCA-CNN model to enable trans-regional landslide susceptibility evaluation, which is based on the adaption domain of transfer learning by integrating the transfer component analysis (TCA) with deep learning convolutional neural network (CNN). The spatial database of landslides is constructed by extracting 11 environmental factors of the reservoir bank area, then the Mianhuatan reservoir area without samples is then predicted using the susceptibility model from the Chitan reservoir area with samples. The results show that: (1) The maximum mean discrepancy (MMD) of data from different study areas treated by TCA decreased significantly (0.022), and the data is approximately identically distributed; (2) The trans-regional prediction accuracy by the TCA-CNN model is 0.854, which is higher than that of the CNN model (0.791). And it can be verified that the proportion of landslide frequency falling into the high/extremely-high susceptibility interval is the highest (89.1%) in the historical landslide; (3) The area under the receiver operating characteristic (ROC) curve of the TCA-CNN model is 0.93, which is higher than that of the CNN model (0.90). It’s obvious that the TCA-CNN model can effectively use the samples data of the modeling area to realize the susceptibility evaluation of the unsampled area. Compared with the traditional machine model, TCA-CNN model has higher and more stable prediction accuracy and stronger generalization ability in cross-region prediction. © 2024 China University of Geosciences. All rights reserved.
Keyword ：
Convolution Convolution Convolutional neural networks Convolutional neural networks Deep learning Deep learning Forecasting Forecasting Landslides Landslides Neural network models Neural network models Transfer learning Transfer learning
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| GB/T 7714 | Su, Yan , Huang, Shaoxiang , Lai, Xiaohe et al. Evaluation of Trans⁃Regional Landslide Susceptibility of Reservoir Bank Based on Transfer Component Analysis [J]. | Earth Science - Journal of China University of Geosciences , 2024 , 49 (5) : 1636-1653 . |
| MLA | Su, Yan et al. "Evaluation of Trans⁃Regional Landslide Susceptibility of Reservoir Bank Based on Transfer Component Analysis" . | Earth Science - Journal of China University of Geosciences 49 . 5 (2024) : 1636-1653 . |
| APA | Su, Yan , Huang, Shaoxiang , Lai, Xiaohe , Chen, Yaoxin , Yang, Lingjun , Lin, Chuan et al. Evaluation of Trans⁃Regional Landslide Susceptibility of Reservoir Bank Based on Transfer Component Analysis . | Earth Science - Journal of China University of Geosciences , 2024 , 49 (5) , 1636-1653 . |
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To investigate the influence of macropore parameters on the non-uniform migration and stability of slopes under rainfall, a solution model was developed based on the two-domain model and the stability coefficient field principle. This model addressed non-uniform flow and slope stability under rainfall infiltration. Using the COMSOL Multiphysics finite element platform, a corresponding model solving program was created. The numerical results were validated through indoor rainfall tests on macropore soil columns. A comparison was made between slope volume water content and point stability coefficient under conditions of uniform and non-uniform flow. Subsequently, the impact of macropore parameters (namely, the proportion of macropore domain ωf, the ratio of water conductivity between macropore and matrix domain μ, and the macropore empirical parameter rw) on slope seepage field and stability coefficient field was analyzed. The findings revealed that compared to scenarios without macropores, considering macropores led to a 7.7% increase in volume water content in the matrix domain and a 5.1% decrease in the macropore domain. Additionally, infiltration depth increased by 83.3% and 150.0%, respectively, and the shallow instability area of the slope expanded by 3.9%. Infiltration depth decreased with an increase in ωf for both the matrix and macropore domains. Conversely, with an increase in μ, infiltration depth decreased for the matrix domain and increased for the macropore domain. There was no significant relationship observed with the empirical parameter rw. At the end of the rainfall, volume water content in the matrix domain peaked, while the macropore domain increased with higher values of ωf and μ, showing minimal impact from the empirical parameter rw. Water exchange was categorized into negative exchange area, positive exchange area, and no exchange area along the profile. The equilibrium depth of water exchange aligned with the change in infiltration depth of the matrix domain. Both the negative and positive exchange areas exhibited peak values that decreased with higher ωf and increased with higher μ and rw values. Under varying parameter values, the slope experienced shallow instability failures. Higher values of ωf and μ corresponded to deeper instability layers and lower point stability coefficients, indicating that macropores were detrimental to slope stability. © 2024 Sichuan University. All rights reserved.
Keyword ：
Infiltration Infiltration Rain Rain Slope stability Slope stability
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| GB/T 7714 | Que, Yun , Li, Shanghui , Zhan, Xiaojun et al. Influence of Macropore Parameters on Slope Non-uniform Flow and Stability [J]. | Advanced Engineering Sciences , 2024 , 56 (3) : 122-133 . |
| MLA | Que, Yun et al. "Influence of Macropore Parameters on Slope Non-uniform Flow and Stability" . | Advanced Engineering Sciences 56 . 3 (2024) : 122-133 . |
| APA | Que, Yun , Li, Shanghui , Zhan, Xiaojun , Zhang, Jisong , Xue, Bin , Xie, Xiudong . Influence of Macropore Parameters on Slope Non-uniform Flow and Stability . | Advanced Engineering Sciences , 2024 , 56 (3) , 122-133 . |
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考虑到滑坡编录制作的耗时性,建立一种"可迁移"的滑坡易发性模型已越发重要.合理利用现有完整滑坡数据地区的样本集对无样本区域进行易发性预测具有重要意义.运用迁移成分分析(transfer component analysis,TCA)方法,结合深度学习卷积神经网络(convolutional neural network,CNN),尝试引入一种基于迁移学习域自适应方法的TCA-CNN模型,并以福建省两个库岸地区为例,提取 11个库岸相关环境因子建立滑坡空间数据库,将有样本的池潭库区易发性模型迁移至无样本的棉花滩库区进行预测,实现跨区域滑坡易发性评价.通过对棉花滩库区进行易发性预测,结果显示:(1)采用TCA方法处理后的不同研究区数据最大均值差异(maximize mean discrepancy,MMD)明显降低(0.022),数据实现近似同分布;(2)TCA-CNN模型的跨区域预测精度为 0.854,高于CNN模型(0.791),且通过历史滑坡验证其落入高、极高易发性区间的滑坡频率比占比最高(89.1%);(3)受试者工作特性(receiver operating characteristic,ROC)曲线下面积TCA-CNN模型为 0.93,高于CNN模型的 0.90.可见TCA-CNN模型能够有效运用建模区的样本数据实现对无样本区域的易发性评价,且相比于传统机器模型在进行跨区域预测时具有更高、更稳定的预测准确率,具备更强的泛化能力.
Keyword ：
卷积神经网络 卷积神经网络 库岸边坡 库岸边坡 数据缺失 数据缺失 滑坡 滑坡 滑坡易发性 滑坡易发性 灾害 灾害 迁移成分分析 迁移成分分析
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| GB/T 7714 | 苏燕 , 黄绍翔 , 赖晓鹤 et al. 基于迁移成分分析的库岸跨区域滑坡易发性评价 [J]. | 地球科学 , 2024 , 49 (5) : 1636-1653 . |
| MLA | 苏燕 et al. "基于迁移成分分析的库岸跨区域滑坡易发性评价" . | 地球科学 49 . 5 (2024) : 1636-1653 . |
| APA | 苏燕 , 黄绍翔 , 赖晓鹤 , 陈耀鑫 , 杨凌鋆 , 林川 et al. 基于迁移成分分析的库岸跨区域滑坡易发性评价 . | 地球科学 , 2024 , 49 (5) , 1636-1653 . |
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花岗岩残积土分布相当广泛,是工程建设中经常遇到的土体之一.借助非饱和三轴仪进行花岗岩残积土抗剪强度试验分析,探究基质吸力对其强度参数影响.试验结果表明基质吸力和净围压的增大对花岗岩残积土强度有提升作用,花岗岩残积土黏聚力随基质吸力增大呈线性增长趋势,内摩擦角随基质吸力增大呈曲线增长趋势.基质吸力对土体弹性模量的影响也是正相关的,即弹性模量随基质吸力增大呈递增规律.根据试验结果,得出抗剪强度参数与基质吸力的关系式及不同基质吸力下土体的弹性模量表达式.
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| GB/T 7714 | 谢秀栋 , 邱文杰 , 郭国林 . 基质吸力对花岗岩残积土强度影响分析 [J]. | 水利与建筑工程学报 , 2021 , 19 (2) : 19-23,60 . |
| MLA | 谢秀栋 et al. "基质吸力对花岗岩残积土强度影响分析" . | 水利与建筑工程学报 19 . 2 (2021) : 19-23,60 . |
| APA | 谢秀栋 , 邱文杰 , 郭国林 . 基质吸力对花岗岩残积土强度影响分析 . | 水利与建筑工程学报 , 2021 , 19 (2) , 19-23,60 . |
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针对道路拓宽改造引起邻近软土地基沉降时间长、不均匀沉降大且难以预测等问题,以某道路拓宽改造项目为工程背景,通过有限元分析、现场实测和理论计算研究邻近软土地基的沉降变形规律,提出一种附加荷载作用下软土地基变形的有限元分析预测方法.结果表明:出水池地基在不同监测点处沉降量变化较大,沉降不均匀;在同一监测点处,土层越浅,沉降速率越大,分层沉降量的最大值发生在压缩模量小的软土层中;考虑软土蠕变-固结耦合效应的数值分析结果与实测数据吻合较好,可用于预测软土地基工后沉降;采用一维固结理论沉降的计算结果与实测值相比偏大,因此,软土地基的沉降计算中考虑软土的蠕变效应是有必要的.
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| GB/T 7714 | 王燕 , 谢秀栋 , 张飞 . 道路拓宽改造对邻近建筑地基变形影响分析与控制 [J]. | 河南理工大学学报（自然科学版） , 2021 , 40 (2) : 179-185 . |
| MLA | 王燕 et al. "道路拓宽改造对邻近建筑地基变形影响分析与控制" . | 河南理工大学学报（自然科学版） 40 . 2 (2021) : 179-185 . |
| APA | 王燕 , 谢秀栋 , 张飞 . 道路拓宽改造对邻近建筑地基变形影响分析与控制 . | 河南理工大学学报（自然科学版） , 2021 , 40 (2) , 179-185 . |
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