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学者姓名：林川
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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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An accurate and reliable multi-step prediction model for dam displacement prediction can provide decision- makers with crucial forecast information, mitigating safety risks associated with abnormal displacements. However, conventional deep learning models overlook the variability exhibited by the lag effect of dam displacement in continuous time domains. The detrimental effect of this limitation on model performance is further amplified in the dam displacement multi-step prediction scenario. To address this issue, this paper proposes a sequence-to-sequence Chrono-initialized long short-term memory (S2S-CLSTM), which can take into account the dynamic nature of dam displacement lag effect in explaining the nonlinear relationship between displacement and complex external features. Specifically, we redefine LSTM initialization using the Chrono initializer and construct a sequence-to-sequence model (S2S-CLSTM) based on the Chrono-initialized Long shortterm memory network (CLSTM). CLSTM adapts to dynamic displacement lag effect by incorporating temporal dependency information in implicit parameters, enhancing model generalization. The S2S paradigm aids in maintaining robustness while interpreting dam displacements across different time scales. Secondly, the study introduce a special Re-optimization strategy tailored for S2S-CLSTM to mitigate performance degradation caused by ambiguous definitions of displacement lag extents. Using monitoring data from a real arch dam, the effectiveness of model and method is verified. Within the prediction step range of 3 to 7, the S2S-CLSTM achieves an impressive average R2 of 0.764, with MAE and RMSE values of only 0.623 mm and 0.797 mm, respectively. In addition, this work also explores the influence of the dam section location on the displacement lag effect model to emphasize the importance of the proposed methods in dam displacement multi-step prediction.
Keyword ：
Chrono-initialized Long short-term memory network Chrono-initialized Long short-term memory network Dam displacement multi-step prediction Dam displacement multi-step prediction Deep learning Deep learning Time lag effect Time lag effect
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| GB/T 7714 | Su, Yan , Fu, Jiayuan , Lin, Chuan et al. A novel deep learning multi-step prediction model for dam displacement using Chrono-initialized LSTM and sequence-to-sequence framework [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 271 . |
| MLA | Su, Yan et al. "A novel deep learning multi-step prediction model for dam displacement using Chrono-initialized LSTM and sequence-to-sequence framework" . | EXPERT SYSTEMS WITH APPLICATIONS 271 (2025) . |
| APA | Su, Yan , Fu, Jiayuan , Lin, Chuan , Lai, Xiaohe , Zheng, Zhiming , Lin, Youlong et al. A novel deep learning multi-step prediction model for dam displacement using Chrono-initialized LSTM and sequence-to-sequence framework . | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 271 . |
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我国东南沿海地区丘陵山地广布,在台风暴雨等因素激发下,各类地质灾害频发.该地区由暴雨引发的泥石流灾害具有突发性、群发性和破坏性特点,当此类灾害发生在临近河道沟谷时,可能冲击水工建筑物甚至造成堵江等严重危害.由于灾害过程的高度不确定性,有效的动力学参数反演方法对分析灾害演化特征和制定有效的减灾措施具有重要意义.本研究以"5·8泰宁泥石流"事件为案例,提出了一种基于多输出支持向量回归机(M-SVR)子模型的参数反演方法.首先,在泥石流动力学计算模型Geoflow_SPH的基础上,构建并行调用框架,对包含内摩擦角、容重、平均物源厚度的触发特征参数组合进行数值模拟,生成了包含输入参数和运动特征的初始训练样本.随后,将该样本集(1000组)按比例划分训练集和测试集,并结合网格搜索技术训练得到M-SVR子模型.再后,使用该子模型对所建反演计算细分样本集(8000组)进行预测计算,以地勘报告中记录的3处控制断面泥石流流通速度为基准,通过计算预测结果的均方误差(MSE),筛选出 MSE 值最小的参数组合作为最终的反演结果.最后,进一步分析透水格栅结构在阻滞泥石流运动和控制影响范围的作用.研究成果有助于明晰暴雨型泥石流的灾害演化规律,为减灾措施的科学应用提供理论支撑.
Keyword ：
参数反演 参数反演 多输出支持向量回归 多输出支持向量回归 泥石流 泥石流 透水格栅 透水格栅
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| GB/T 7714 | 林川 , 林彦喆 , 林威伟 et al. 暴雨型泥石流特征参数反演方法及透水格栅效能评价研究 [J]. | 水力发电学报 , 2025 , 44 (2) : 1-14 . |
| MLA | 林川 et al. "暴雨型泥石流特征参数反演方法及透水格栅效能评价研究" . | 水力发电学报 44 . 2 (2025) : 1-14 . |
| APA | 林川 , 林彦喆 , 林威伟 , 郭朝旭 , 黄学钊 , 杜哲镓 et al. 暴雨型泥石流特征参数反演方法及透水格栅效能评价研究 . | 水力发电学报 , 2025 , 44 (2) , 1-14 . |
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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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Deformation monitoring data provide a direct representation of the structural behavior of reservoir bank rock slopes, and accurate deformation prediction is pivotal for slope safety monitoring and disaster warning. Among various deformation prediction models, hybrid models that integrate field monitoring data and numerical simulations stand out due to their well-defined physical and mechanical concepts, and their ability to make effective predictions with limited monitoring data. The predictive accuracy of hybrid models is closely tied to the precise determination of rock mass mechanical parameters in structural numerical simulations. However, rock masses in rock slopes are characterized by intersecting geological structural planes, resulting in reduced strength and the creation of multiple fracture flow channels. These factors contribute to the heterogeneous, anisotropic, and size-dependent properties of the macroscopic deformation parameters of the rock mass, influenced by the coupling of seepage and stress. To improve the predictive accuracy of the hybrid model, this study introduces the theory of equivalent continuous media. It proposes a method for determining the equivalent deformation parameters of fractured rock mass considering the coupling of seepage and stress. This method, based on a discrete fracture network (DFN) model, is integrated into the hybrid prediction model for rock slope deformation. Engineering case studies demonstrate that this approach achieves a high level of prediction accuracy and holds significant practical value.
Keyword ：
equivalent deformation parameters equivalent deformation parameters fractured rock mass fractured rock mass hybrid safety monitoring model hybrid safety monitoring model slope deformation slope deformation
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| GB/T 7714 | Liang, Jiachen , Chen, Jian , Lin, Chuan . A Hybrid Prediction Model for Rock Reservoir Bank Slope Deformation Considering Fractured Rock Mass Parameters [J]. | WATER , 2024 , 16 (13) . |
| MLA | Liang, Jiachen et al. "A Hybrid Prediction Model for Rock Reservoir Bank Slope Deformation Considering Fractured Rock Mass Parameters" . | WATER 16 . 13 (2024) . |
| APA | Liang, Jiachen , Chen, Jian , Lin, Chuan . A Hybrid Prediction Model for Rock Reservoir Bank Slope Deformation Considering Fractured Rock Mass Parameters . | WATER , 2024 , 16 (13) . |
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山体滑坡是全球范围内多发的一种地质灾害.由于滑坡具有突发性和破坏性强的特点,建立有效的数值分析模型将有助于制定针对性的防治策略.本文针对滑坡运动过程中表现出的颗粒流特性,基于 μ(I)模型提出了针对浅层滑坡的动态摩擦系数表达式,并构建了对应的光滑粒子流体动力学(SPH)方法求解框架.考虑到颗粒破碎对滑坡运动性的显著影响,结合基于破坏势能的颗粒破碎法则,建立 μ(I)模型中基底摩擦力与颗粒分布之间的关系.通过两个经典的三维斜面模型试验,验证了 μ(I)模型在滑坡运动分析中的应用价值,并进行了颗粒破碎效应参数敏感性分析,为后续滑坡灾害的防治工作提供参考.
Keyword ：
μ(I)模型 μ(I)模型 光滑粒子流体动力学 光滑粒子流体动力学 滑坡 滑坡 颗粒破碎 颗粒破碎
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| GB/T 7714 | 林川 , 林彦喆 , 苏燕 et al. 颗粒流运动SPH方法及滑坡破碎效应研究 [J]. | 水力发电学报 , 2024 , 43 (7) : 61-72 . |
| MLA | 林川 et al. "颗粒流运动SPH方法及滑坡破碎效应研究" . | 水力发电学报 43 . 7 (2024) : 61-72 . |
| APA | 林川 , 林彦喆 , 苏燕 , 潘依琳 , 高献 . 颗粒流运动SPH方法及滑坡破碎效应研究 . | 水力发电学报 , 2024 , 43 (7) , 61-72 . |
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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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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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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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