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学者姓名：杨丁颖
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Abstract ：
In addressing the intricate dynamic responses of pipeline conveying fluid characterized by spatiotemporal multiscales and multi-modal contributions, Fourier feature-embedded physics-information neural network (FF-PINN) is proposed. By introducing Fourier feature mapping to decompose the temporal and spatial scale information, FF-PINN precisely captures the relatively low-frequencies on the macroscopic time scale as well as the relatively high-frequencies on the microscopic scale of the pipeline's vibration. This approach significantly overcomes the spectral bias encountered by PINN when learning high-frequency information. To verify the effectiveness and accuracy of this method, the proposed FF-PINN is applied to solve the pipeline conveying fluid model with fixed support at both ends. The relative L2 error between the obtained results and the reference solution is 1.8 x 10-2, concurrently with a significant reduction in computational time. Additionally, an analysis of hyperparameter sigma selection is conducted to evaluate its impact on the performance of FF-PINN, while establishing the correspondence between hyperparameter and eigenvector frequency. The results demonstrate that choosing appropriate hyperparameters empowers FF-PINN to better learn the vibration of specific frequencies, enabling the accurate modeling of pipeline vibrations' dynamic response. It provides a potent solution for solving spatiotemporal multi-scale complexity problems involving the superposition of high-and low-frequencies.
Keyword ：
Fourier feature Fourier feature Physics-information neural network Physics-information neural network Pipeline conveying fluid Pipeline conveying fluid Spatiotemporal multi-scales Spatiotemporal multi-scales Vibration characteristics Vibration characteristics
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| GB/T 7714 | Zhang, Ting , Yan, Rui , Zhang, Siqian et al. Application of Fourier feature physics-information neural network in model of pipeline conveying fluid [J]. | THIN-WALLED STRUCTURES , 2024 , 198 . |
| MLA | Zhang, Ting et al. "Application of Fourier feature physics-information neural network in model of pipeline conveying fluid" . | THIN-WALLED STRUCTURES 198 (2024) . |
| APA | Zhang, Ting , Yan, Rui , Zhang, Siqian , Yang, Dingying , Chen, Anhao . Application of Fourier feature physics-information neural network in model of pipeline conveying fluid . | THIN-WALLED STRUCTURES , 2024 , 198 . |
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Escalation in flash floods and the enhanced devastations, especially in the arid and semiarid regions of the world has required precise mapping of the flash flood susceptible zones. In this study, we applied six novel credal decision tree (CDT)-based ensemble models-1. CDT, 2. CDT Alternative Decision Tree (ADTree), 3. CDT- Reduced Error Pruning Tree (REPT), 4. CDT- Rotational Forest (RF), 5. CDT-FT, 6. CDT- Naive Bias Tree (NBTree). For preparing the flash flood susceptibility maps (FFSM), 206 flood locations were selected in the Neka-roud watershed of Iran with 70% as training data and 30% as testing data. Moreover, 18 flood conditing factors were considered for FFSM and a multi-colinearity test was performed for determining the role of the factors. Our results show that the distance from the stream plays a vital role in flash floods. The CDT-FT is the best-fit model out of the six novel algorithms employed in this study as demonstrated by the highest values of the area under the curve (AUC) of the receiver operating curve (ROC) (AUROC 0.986 for training data and 0.981 for testing data). Our study provides a novel approach and useful tool for flood management.
Keyword ：
Credal decision tree Credal decision tree Flash flood mapping Flash flood mapping Flood management Flood management Machine learning algorithms Machine learning algorithms Neka-roud watershed Neka-roud watershed Novel Ensemble models Novel Ensemble models
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| GB/T 7714 | Yang, Dingying , Zhang, Ting , Arabameri, Alireza et al. Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches [J]. | EARTH SCIENCE INFORMATICS , 2023 , 16 (4) : 3143-3161 . |
| MLA | Yang, Dingying et al. "Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches" . | EARTH SCIENCE INFORMATICS 16 . 4 (2023) : 3143-3161 . |
| APA | Yang, Dingying , Zhang, Ting , Arabameri, Alireza , Santosh, M. , Saha, Ujwal Deep , Islam, Aznarul . Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches . | EARTH SCIENCE INFORMATICS , 2023 , 16 (4) , 3143-3161 . |
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Landslides are a prevalent geologic phenomenon that substantially threatens human life and infrastructure, resulting in considerable loss and destruction. The practice of landslide susceptibility mapping is crucial for the mitigation of risks connected with this natural disaster. This work aims at investigating the influence of varying sample sizes on the precision of landslide susceptibility modelling using a case study conducted in the Alamout basin, Iran. The researchers used a machine learning methodology based on tree algorithms to construct a model for predicting the likelihood of landslides. Additionally, they adopted a multi-scenario strategy to address the inherent uncertainty associated with the input data. The integration of the naive Bayes tree (NBTree), random forest (RF), logistic model tree (LMT) and J48 algorithms was performed. The modelling process included using 20 predictive parameters across four distinct scenarios. Four models, labelled S1, S2, S3 and S4, were used in this study. These models utilized 25%, 50%, 75% and 100% of the available inventory data. The research presented in this study is distinguished by using a tree-based methodology for landslide susceptibility modelling and incorporating a multi-scenario strategy to address the inherent uncertainty associated with the input data. The findings indicated that the augmentation of the sample size improved the precision of the models. The efficacy of using a multi-scenario strategy in enhancing the dependability of the model is also underscored. Among the 20 input elements used in the modelling process, it was seen that slope angle accounted for the highest relative significance, constituting 25.60% of the overall influence. Following more closely, distance to fault contributed significantly, with a relative importance of 23.40%. Additionally, rainfall and elevation exhibited notable contributions, with relative volumes of 7.91% and 5.50%, respectively. All four landslide models showed adequate learning and forecasting ability throughout the training and testing phases. During the testing phase, the true skill score (TSS) values exhibited a range of 0.631-0.804, while the area under the receiver operating characteristic curve values showed a range of 0.745-0.921. The susceptibility maps indicated that a significant portion of the region exhibits moderate to very high susceptibility zones, with the northern and eastern sectors displaying greater landslide values than the western region. The model's performance showed improvement from S1 to S4 in both the training and testing phases. The performance of the models exhibited the following trend: in scenario 1, the RF model outperformed the J48, LMT and NBTree models; in scenario 2, the RF model surpassed the NBTree and LMT models, while being on par with the J48 model; in scenarios 3 and 4, the RF model showed superior performance compared to the NBTree, J48 and LMT models. Therefore, the RF model proved to be the most effective among the models evaluated. The findings derived from this research have the potential to serve as valuable references for the purposes of land-use planning and catastrophe risk management.
Keyword ：
decision tree modelling decision tree modelling landslide events landslide events landslide susceptibility mapping landslide susceptibility mapping random forest random forest
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| GB/T 7714 | Yang, Dingying , Jiang, Xi , Arabameri, Alireza et al. Landslide risk assessment and management using hybrid machine learning-based empirical models [J]. | GEOLOGICAL JOURNAL , 2023 , 59 (3) : 885-905 . |
| MLA | Yang, Dingying et al. "Landslide risk assessment and management using hybrid machine learning-based empirical models" . | GEOLOGICAL JOURNAL 59 . 3 (2023) : 885-905 . |
| APA | Yang, Dingying , Jiang, Xi , Arabameri, Alireza , Santosh, M. , Egbueri, Johnbosco C. . Landslide risk assessment and management using hybrid machine learning-based empirical models . | GEOLOGICAL JOURNAL , 2023 , 59 (3) , 885-905 . |
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Abstract ：
Escalation in flash floods and the enhanced devastations, especially in the arid and semiarid regions of the world has required precise mapping of the flash flood susceptible zones. In this study, we applied six novel credal decision tree (CDT)-based ensemble models-1. CDT, 2. CDT Alternative Decision Tree (ADTree), 3. CDT- Reduced Error Pruning Tree (REPT), 4. CDT- Rotational Forest (RF), 5. CDT-FT, 6. CDT- Naive Bias Tree (NBTree). For preparing the flash flood susceptibility maps (FFSM), 206 flood locations were selected in the Neka-roud watershed of Iran with 70% as training data and 30% as testing data. Moreover, 18 flood conditing factors were considered for FFSM and a multi-colinearity test was performed for determining the role of the factors. Our results show that the distance from the stream plays a vital role in flash floods. The CDT-FT is the best-fit model out of the six novel algorithms employed in this study as demonstrated by the highest values of the area under the curve (AUC) of the receiver operating curve (ROC) (AUROC 0.986 for training data and 0.981 for testing data). Our study provides a novel approach and useful tool for flood management.
Keyword ：
Credal decision tree Credal decision tree Flash flood mapping Flash flood mapping Flood management Flood management Machine learning algorithms Machine learning algorithms Neka-roud watershed Neka-roud watershed Novel Ensemble models Novel Ensemble models
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yang, Dingying , Zhang, Ting , Arabameri, Alireza et al. Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches [J]. | EARTH SCIENCE INFORMATICS , 2023 , 16 (4) : 3143-3161 . |
| MLA | Yang, Dingying et al. "Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches" . | EARTH SCIENCE INFORMATICS 16 . 4 (2023) : 3143-3161 . |
| APA | Yang, Dingying , Zhang, Ting , Arabameri, Alireza , Santosh, M. , Saha, Ujwal Deep , Islam, Aznarul . Flash-flood susceptibility mapping: a novel credal decision tree-based ensemble approaches . | EARTH SCIENCE INFORMATICS , 2023 , 16 (4) , 3143-3161 . |
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Abstract ：
This work aims to model and explore the competitive mechanism between the internal flow effect (IFE) and external current effect (ECE) on the cross-flow vortex-induced vibration (VIV) of a free-span submarine pipeline transporting an axial internal flow. Considering the coupling between the structure and fluids, the partial differential equations of pipeline system are described by Euler-Bernoulli beam theory coupled with the wake oscillator model. To obtain the VIV response of pipeline at different internal flow and external current velocities, the generalized finite difference method (GFDM) and Houbolt method (HM) are employed for spatial and temporal discretizations for equations, respectively. The results indicates that when the IFE is not accounted, the higher-order VIV modes of the pipe are successively excited by ECE. Of interest is that coupling flutter phenomenon between even-order and adjacent modes will occur. Whereas, when the fluid inside the pipe flows, which means the IFE occurrence to be observed, the coupled flutter phenomenon disappears along with significant enlargement of the amplitude in even-order modes. Meanwhile, the mode transition is associated with internal flow velocity and a continuous change in the external flow velocity.
Keyword ：
Competitive mechanism Competitive mechanism Internal flow effect (IFE) Internal flow effect (IFE) Mode transition Mode transition Numerical investigation Numerical investigation Submarine pipeline Submarine pipeline Vortex-induced vibration (VIV) Vortex-induced vibration (VIV)
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| GB/T 7714 | Zhang, Ting , Zhang, Siqian , Yang, Dingying et al. Numerical investigation on competitive mechanism between internal and external effects of submarine pipeline undergoing vortex-induced vibration [J]. | OCEAN ENGINEERING , 2022 , 266 . |
| MLA | Zhang, Ting et al. "Numerical investigation on competitive mechanism between internal and external effects of submarine pipeline undergoing vortex-induced vibration" . | OCEAN ENGINEERING 266 (2022) . |
| APA | Zhang, Ting , Zhang, Siqian , Yang, Dingying , Huang, Guanyi . Numerical investigation on competitive mechanism between internal and external effects of submarine pipeline undergoing vortex-induced vibration . | OCEAN ENGINEERING , 2022 , 266 . |
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