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Abstract ：
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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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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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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This paper introduces an advanced localized space-time Trefftz method tackling boundary value predicaments within complex two-dimensional domains governed by diffusion equations. In contrast to the widespread spacetime collocation Trefftz method, which typically produces dense and ill-conditioned matrices, the proposed strategy employs a localized collocation scheme to remove these constraints. In particular, this is beneficial in multi-connected configurations or when dealing with significant variations in field values. To the best of our knowledge, this is the first space-time collocation Trefftz method adaptation, which is referred to as the localized space-time Trefftz method in this paper. The latter combines the space-time collocation Trefftz method principles, which allows to eliminate the need for mesh and numerical quadrature in its application. The localized space-time Trefftz method represents each interior node expressed as a linear blend of its immediate neighbors, while the space-time collocation Trefftz method applies numerical techniques within distinct subdomains. A sparse system of linear algebraic equations with internal points satisfying the governing equation, and boundary points satisfying the boundary conditions, allows to obtain numerical solutions using matrix systems. The localized space-time Trefftz method retains the easy-to-use properties and mesh-free structure of the space-time collocation Trefftz method, and it mitigates its ill-conditioning characteristics. Due to the localization principle and the consideration of overlapping subdomains, the solutions in the proposed localized space-time Trefftz method are more simply and compactly expressed compared with those in the space-time collocation Trefftz method, especially when dealing with multiply-connected domains. Numerical examples for simply-connected and multiply-connected domains are then provided to demonstrate the high precision and simplicity of the proposed localized space-time Trefftz method. The obtained results show that the latter has very high accuracy in solving two-dimensional diffusion problems. Compared with the traditional space-time collocation Trefftz method, the proposed mesh-free strategy yields solutions with higher precision while significantly reducing the instability.
Keyword ：
Diffusion equation Diffusion equation Localized method Localized method Space-time Trefftz method Space-time Trefftz method
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| GB/T 7714 | Hong, Li-Dan , Yeih, Weichung , Ku, Cheng-Yu et al. Localized space-time Trefftz method for diffusion equations in complex domains [J]. | ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS , 2024 , 169 . |
| MLA | Hong, Li-Dan et al. "Localized space-time Trefftz method for diffusion equations in complex domains" . | ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS 169 (2024) . |
| APA | Hong, Li-Dan , Yeih, Weichung , Ku, Cheng-Yu , Su, Yan . Localized space-time Trefftz method for diffusion equations in complex domains . | ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS , 2024 , 169 . |
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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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本实用新型涉及一种应用于坡面改平的拼插式生态护坡砌块，包括护坡砌块主体，所述护坡砌块主体沿其外周各侧面依次设置有滑动燕尾榫和自嵌卯，所述护坡砌块主体的顶部与水平面保持平行，所述护坡砌块主体的底部倾斜设置且与坡面保持平行，所述护坡砌块主体上竖向开设有贯通其顶部与底部的大生态孔，所述护坡砌块主体于相邻的滑动燕尾榫和自嵌卯之间设置有侧棱切割面，该应用于坡面改平的拼插式生态护坡砌块保证了砌块与砌块间的连接稳定性，且开孔率高，砖中土壤保持水平稳定状态，有助于植物生长, 提高水土保持效果。
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| GB/T 7714 | 苏燕 , 周文涛 , 吴梦萱 et al. 一种应用于坡面改平的拼插式生态护坡砌块 : CN202321700815.1[P]. | 2023-06-30 00:00:00 . |
| MLA | 苏燕 et al. "一种应用于坡面改平的拼插式生态护坡砌块" : CN202321700815.1. | 2023-06-30 00:00:00 . |
| APA | 苏燕 , 周文涛 , 吴梦萱 , 叶恩杰 , 祁湘平 , 蔡玉迅 . 一种应用于坡面改平的拼插式生态护坡砌块 : CN202321700815.1. | 2023-06-30 00:00:00 . |
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Extreme weather events and anthropogenic activity have severely impacted the Minjiang estuary (MJE) with evolution patterns in recent decades. Determining the estuarine alluvial evolution pattern is crucial for the sustainable development of densely populated coastal areas. Using chart data, this paper analyzed how the MJE's flushing and siltation changed over time (1950-2019). The findings indicate that, in the past 70 years, the MJE has evolved through seven stages of flushing and siltation under the condition of decreasing incoming sediment: "significant siltation -siltation -minor siltation -significant erosion -significant siltation -minor siltation -significant erosion". Despite decreased incoming material, the estuary underwent substantial siltation from 1998 to 2005. Extreme meteorological conditions were the cause of this anomaly. The "severe flushing" phenomenon occurred in the study area from 2011 to 2019, and the comparison of pre-dam and post-dam at the estuary revealed that extreme weather can only adjust in stages and cannot change the overall state of the estuary flushing due to the reduction of incoming sediment in the basin. According to the EOF (Empirical Orthogonal/ Eigen Function) study of four sections of the estuary, it is most impacted by incoming sediment from the watershed, followed by severe weather conditions. Our research is crucial for comprehending how human behavior and harsh weather might affect the development of MJE, and it offers sound advice for the long-term management of MJE.
Keyword ：
China China Climate change Climate change Geomorphological response Geomorphological response Human activities Human activities Minjiang estuary Minjiang estuary
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| GB/T 7714 | Lai, Xiaohe , Hou, Yuebao , Jia, Jianping et al. Rapid change of erosion-deposition evolution in the Minjiang estuary, Southeast China [J]. | OCEAN & COASTAL MANAGEMENT , 2023 , 238 . |
| MLA | Lai, Xiaohe et al. "Rapid change of erosion-deposition evolution in the Minjiang estuary, Southeast China" . | OCEAN & COASTAL MANAGEMENT 238 (2023) . |
| APA | Lai, Xiaohe , Hou, Yuebao , Jia, Jianping , Chen, Cheng , Su, Yan , Jiang, Jun et al. Rapid change of erosion-deposition evolution in the Minjiang estuary, Southeast China . | OCEAN & COASTAL MANAGEMENT , 2023 , 238 . |
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