Dawei Ji
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Dawei Ji has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Lixi Zhao
National Marine Data and Information Service, Tianjin 300171, China
3 shared publications - Jinguo Yang
College of Economics and Management, Tianjin University of Science and Technology, Tianjin 300453, China
2 shared publications - Shuming Liu
National Marine Data and Information Service, Tianjin 300171, China
2 shared publications - Yaning Li
National Marine Data and Information Service, Tianjin 300171, China
2 shared publications - Hongchu Yu
School of Navigation, Wuhan University of Technology, Wuhan 430063, China
1 shared publication - Na Wang
National Marine Data and Information Service, Marine and Islands Research Office, Tianjin 300171, China
1 shared publication
Latest Publications
- Open Access
- 19 September 2026
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 14 (18) , 1740
MarineGuard-GNN: A Physics-Informed Multimodal Heterogeneous Graph Neural Network for Submarine Cable Fault Risk Assessment Under Geographic Shift
With the rapid development of graph neural networks and physics-informed machine learning for critical infrastructure risk, reliable assessment of submarine telecommunication cables has become both a practical resilience requirement and a demanding cross-domain learning problem. These cables carry more than 99% of international data traffic, yet fault risk modeling faces four explicit challenges: (1) modality misalignment, because marine evidence combines gridded environmental fields with irregular vessel trajectories; (2) relational heterogeneity, because hazards interact through semantically distinct spatial links; (3) physical inconsistency, because unconstrained predictions may violate seabed geomechanics under geographic shift; and (4) decision uncertainty, because safety-critical inspection and routing require uncertainty rather than point estimates alone. The closest approaches leave identifiable gaps. Makrakis and colleagues optimized static cable routes without learned hazard interactions or uncertainty; Taghizadeh and colleagues constrained flood graph predictions without cable-specific heterogeneous entities; and Guo and colleagues fused maritime trajectories without forecasting cable faults or screening routes. No existing approaches combine these missing capabilities under geographically held-out cable basins. To address this gap, the present paper proposes MarineGuard-GNN, a physics-informed multimodal heterogeneous graph neural network. Its Cross-Modal Spatiotemporal Tokenizer maps GEBCO bathymetry, CMEMS ocean fields, and NOAA AIS trajectories into a shared 256-dimensional space; a relation-aware Heterogeneous Graph Transformer represents four semantic node types and four physical relation types; a differentiable Mohr–Coulomb loss regularizes geomechanical consistency; and a Monte Carlo dropout risk head estimates segment-level epistemic uncertainty for inspection and routing. Under 4-fold geographic cross-validation at natural prevalence on 15,110 cable nodes (847 faults), MarineGuard-GNN attains cross-basin AUC-ROC, average-precision, and F1 ranges of –, –, and –, respectively; average precision corresponds to a – lift over the no-skill prevalence baseline. Paired basin-stratified bootstrap analysis and Holm-corrected tests confirm improvements over the strongest tabular and graph baselines (AUC-ROC , AP ; ). Matched ablation shows that removing the corrected mechanics term reduces AUC-ROC by and average precision by without improving calibration. Across three densely sampled public cable corridors, uncertainty-aware routing reduces mean predicted risk by at least while limiting distance overhead to below ; the conclusion remains stable for risk thresholds from to . These results demonstrate statistically supported cross-basin generalization, establishing the proposed framework as a reproducible decision support method.- 19 September 2026
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 14 (18) , 1740
Open AccessMarineGuard-GNN: A Physics-Informed Multimodal Heterogeneous Graph Neural Network for Submarine Cable Fault Risk Assessment Under Geographic Shift
With the rapid development of graph neural networks and physics-informed machine learning for critical infrastructure risk, reliable assessment of submarine telecommunication cables has become both a practical resilience requirement and a demanding cross-domain learning problem. These cables carry more than 99% of international data traffic, yet fault risk modeling faces four explicit challenges: (1) modality misalignment, because marine evidence combines gridded environmental fields with irregular vessel trajectories; (2) relational heterogeneity, because hazards interact through semantically distinct spatial links; (3) physical inconsistency, because unconstrained predictions may violate seabed geomechanics under geographic shift; and (4) decision uncertainty, because safety-critical inspection and routing require uncertainty rather than point estimates alone. The closest approaches leave identifiable gaps. Makrakis and colleagues optimized static cable routes without learned hazard interactions or uncertainty; Taghizadeh and colleagues constrained flood graph predictions without cable-specific heterogeneous entities; and Guo and colleagues fused maritime trajectories without forecasting cable faults or screening routes. No existing approaches combine these missing capabilities under geographically held-out cable basins. To address this gap, the present paper proposes MarineGuard-GNN, a physics-informed multimodal heterogeneous graph neural network. Its Cross-Modal Spatiotemporal Tokenizer maps GEBCO bathymetry, CMEMS ocean fields, and NOAA AIS trajectories into a shared 256-dimensional space; a relation-aware Heterogeneous Graph Transformer represents four semantic node types and four physical relation types; a differentiable Mohr–Coulomb loss regularizes geomechanical consistency; and a Monte Carlo dropout risk head estimates segment-level epistemic uncertainty for inspection and routing. Under 4-fold geographic cross-validation at natural prevalence on 15,110 cable nodes (847 faults), MarineGuard-GNN attains cross-basin AUC-ROC, average-precision, and F1 ranges of –, –, and –, respectively; average precision corresponds to a – lift over the no-skill prevalence baseline. Paired basin-stratified bootstrap analysis and Holm-corrected tests confirm improvements over the strongest tabular and graph baselines (AUC-ROC , AP ; ). Matched ablation shows that removing the corrected mechanics term reduces AUC-ROC by and average precision by without improving calibration. Across three densely sampled public cable corridors, uncertainty-aware routing reduces mean predicted risk by at least while limiting distance overhead to below ; the conclusion remains stable for risk thresholds from to . These results demonstrate statistically supported cross-basin generalization, establishing the proposed framework as a reproducible decision support method. - Open Access
- 22 July 2026
- Published by MDPI AG in Preprints.org
MarineGuard-GNN: A Physics-Informed Multimodal Heterogeneous Graph Neural Network for Submarine Cable Fault Risk Assessment and Spatial Route Optimization
Submarine telecommunication cables carry over 99% of international data traffic yet remain persistently vulnerable to geophysical, anthropogenic, and oceanographic hazards. Existing risk assessment methods , GIS-based least-cost path analysis and cable-body condition monitoring , operate in isolation, cannot fuse heterogeneous multi-modal data, and yield neither uncertainty estimates nor physics-consistent predictions. We present MarineGuard-GNN, a physics-informed multimodal heterogeneous graph neural network that unifies four hazard modalities , bathymetric, oceanographic, vessel traffic, and seismic , in a single relational graph over four semantic node types and four typed relation edges. Three novel technical contributions distinguish the framework: (i) the Cross-Modal Spatiotemporal Tokenizer (CMST), which jointly encodes GEBCO bathymetric raster fields via a SatMAE-pretrained ViT-S and NOAA AIS vessel trajectories via a Transformer-XL into a unified 256-dimensional token space, addressing the modality-heterogeneity gap; (ii) a Physics-Informed Heterogeneous Graph Transformer with a differentiable Mohr–Coulomb geomechanical shear-stress regularization loss Lphys that prevents physically inconsistent risk assignments at steep bathymetric gradients; and (iii) a Bayesian Uncertainty-Aware Risk Head using Monte Carlo Dropout (K=50, p=0.3) to produce calibrated epistemic uncertainty maps for inspection prioritization. Under strict 4-fold geographic cross-validation across the Pacific, Atlantic, Indian, and Mediterranean basins using five publicly available datasets, MarineGuard-GNN achieves the highest mean AUC-ROC (0.7228±0.0665) and Average Precision (0.6418±0.1040) among five baselines. A Dijkstra-based spatial route optimizer reduces mean predicted fault risk by 0.0035 absolute on the Atlantic New York–UK corridor, avoiding one additional high-risk node at a distance overhead of only +7.4 km (+0.56%). All code, preprocessing pipelines, and datasets are publicly available.
- 22 July 2026
- Published by MDPI AG in Preprints.org
Open AccessMarineGuard-GNN: A Physics-Informed Multimodal Heterogeneous Graph Neural Network for Submarine Cable Fault Risk Assessment and Spatial Route Optimization
Submarine telecommunication cables carry over 99% of international data traffic yet remain persistently vulnerable to geophysical, anthropogenic, and oceanographic hazards. Existing risk assessment methods , GIS-based least-cost path analysis and cable-body condition monitoring , operate in isolation, cannot fuse heterogeneous multi-modal data, and yield neither uncertainty estimates nor physics-consistent predictions. We present MarineGuard-GNN, a physics-informed multimodal heterogeneous graph neural network that unifies four hazard modalities , bathymetric, oceanographic, vessel traffic, and seismic , in a single relational graph over four semantic node types and four typed relation edges. Three novel technical contributions distinguish the framework: (i) the Cross-Modal Spatiotemporal Tokenizer (CMST), which jointly encodes GEBCO bathymetric raster fields via a SatMAE-pretrained ViT-S and NOAA AIS vessel trajectories via a Transformer-XL into a unified 256-dimensional token space, addressing the modality-heterogeneity gap; (ii) a Physics-Informed Heterogeneous Graph Transformer with a differentiable Mohr–Coulomb geomechanical shear-stress regularization loss Lphys that prevents physically inconsistent risk assignments at steep bathymetric gradients; and (iii) a Bayesian Uncertainty-Aware Risk Head using Monte Carlo Dropout (K=50, p=0.3) to produce calibrated epistemic uncertainty maps for inspection prioritization. Under strict 4-fold geographic cross-validation across the Pacific, Atlantic, Indian, and Mediterranean basins using five publicly available datasets, MarineGuard-GNN achieves the highest mean AUC-ROC (0.7228±0.0665) and Average Precision (0.6418±0.1040) among five baselines. A Dijkstra-based spatial route optimizer reduces mean predicted fault risk by 0.0035 absolute on the Atlantic New York–UK corridor, avoiding one additional high-risk node at a distance overhead of only +7.4 km (+0.56%). All code, preprocessing pipelines, and datasets are publicly available.
- Open Access
- 26 June 2026
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 14 (13) , 1177
Local–Global Spatio-Temporal Learning for Fishing Vessel Behavior Recognition Using AIS Trajectories
Illegal, unreported, and unregulated fishing threatens marine ecosystem health and sustainable fisheries management, highlighting the need for reliable fishing-vessel behavior recognition from Automatic Identification System (AIS) trajectories. However, AIS-derived operational states often exhibit overlapping motion patterns, particularly between Underway and Fishing and between Anchored and Moored. This study proposes FishFormer, a local–global spatio-temporal deep learning framework designed for recognizing four AIS-status-derived fishing-vessel operational states: Underway, Fishing, Anchored, and Moored. FishFormer integrates dual-stream spatio-temporal attention, local–global feature fusion, and feed-forward feature enhancement to capture long-range trajectory dependencies, local motion variations, and heterogeneous kinematic features. Experiments on 8139 real-world AIS trajectory segments from U.S. coastal waters show that FishFormer achieves 96.63% overall accuracy and an F1-score of 0.9661. Compared with seven baseline models under a unified experimental protocol, FishFormer shows superior recognition performance, while ablation, confusion-matrix, and robustness analyses further verify the effectiveness of the proposed modules and their contribution to reducing errors among similar behavior states. These results indicate that local–global spatio-temporal learning improves AIS-based operational-state recognition and can provide a behavioral information layer for fishing-vessel activity monitoring and fishery management decision support.
- 26 June 2026
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 14 (13) , 1177
Open AccessLocal–Global Spatio-Temporal Learning for Fishing Vessel Behavior Recognition Using AIS Trajectories
Illegal, unreported, and unregulated fishing threatens marine ecosystem health and sustainable fisheries management, highlighting the need for reliable fishing-vessel behavior recognition from Automatic Identification System (AIS) trajectories. However, AIS-derived operational states often exhibit overlapping motion patterns, particularly between Underway and Fishing and between Anchored and Moored. This study proposes FishFormer, a local–global spatio-temporal deep learning framework designed for recognizing four AIS-status-derived fishing-vessel operational states: Underway, Fishing, Anchored, and Moored. FishFormer integrates dual-stream spatio-temporal attention, local–global feature fusion, and feed-forward feature enhancement to capture long-range trajectory dependencies, local motion variations, and heterogeneous kinematic features. Experiments on 8139 real-world AIS trajectory segments from U.S. coastal waters show that FishFormer achieves 96.63% overall accuracy and an F1-score of 0.9661. Compared with seven baseline models under a unified experimental protocol, FishFormer shows superior recognition performance, while ablation, confusion-matrix, and robustness analyses further verify the effectiveness of the proposed modules and their contribution to reducing errors among similar behavior states. These results indicate that local–global spatio-temporal learning improves AIS-based operational-state recognition and can provide a behavioral information layer for fishing-vessel activity monitoring and fishery management decision support.