Institution profile

Dhaka University of Engineering and Technology

Academic institutionasia · bd
Official website
Research library8linked papers
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Selected work

Representative Papers

A Temporal Planning Framework for Disruption Aware Dynamic Route Optimization in Heterogeneous Railway Systems

Jun 12, 2026

This study addresses the scheduling complexity and safety risks in heterogeneous railway systems arising from multi-gauge compatibility, shared single-track usage, operational disparities among trains, and stochastic disturbances. For the first time, it introduces temporal planning to this domain by constructing a dynamic scheduling model based on PDDL 2.1 that explicitly encodes gauge constraints and disturbance responses. The approach leverages a temporal planner to generate conflict-free, timestamped executable action sequences, validated for feasibility through a dedicated plan verifier. The work also establishes a benchmark suite of 200 large-scale instances—featuring up to 1,000 track segments and 120 trains—that bridges the modeling gap between high-level timetables and low-level operations, substantially reducing manual intervention and enhancing both automation and safety in railway dispatching.

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HaorFloodAlert: Deseasonalized ML Ensemble for 72-Hour Flood Prediction in Bangladesh Haor Wetlands

May 19, 2026

This study addresses the critical gap in flash flood early warning for the Haor wetlands of Bangladesh and the inability of existing models to adequately represent backwater dynamics. To overcome these challenges, the authors propose a deseasonalized machine learning ensemble approach that leverages Otsu-threshold-based change detection from Sentinel-1 SAR imagery to construct a proxy indicator for upstream Barak River conditions. A weighted ensemble of Random Forest and XGBoost models enables 72-hour probabilistic flood forecasting, integrated with a three-tier alert system and a BRRI-calibrated rice loss assessment module. By explicitly removing temperature-driven seasonal biases that artificially inflate accuracy, the framework delivers actionable lead times of approximately 36 hours. Evaluated against 77 historical flood events, the model achieves 89.6% accuracy, 87.5% recall, and an AUC-ROC of 0.943, with spatial validation showing 84–91% agreement.

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Recent publications

Latest Papers

A Temporal Planning Framework for Disruption Aware Dynamic Route Optimization in Heterogeneous Railway Systems

Jun 12, 2026

This study addresses the scheduling complexity and safety risks in heterogeneous railway systems arising from multi-gauge compatibility, shared single-track usage, operational disparities among trains, and stochastic disturbances. For the first time, it introduces temporal planning to this domain by constructing a dynamic scheduling model based on PDDL 2.1 that explicitly encodes gauge constraints and disturbance responses. The approach leverages a temporal planner to generate conflict-free, timestamped executable action sequences, validated for feasibility through a dedicated plan verifier. The work also establishes a benchmark suite of 200 large-scale instances—featuring up to 1,000 track segments and 120 trains—that bridges the modeling gap between high-level timetables and low-level operations, substantially reducing manual intervention and enhancing both automation and safety in railway dispatching.

0 citationsRead paper

HaorFloodAlert: Deseasonalized ML Ensemble for 72-Hour Flood Prediction in Bangladesh Haor Wetlands

May 19, 2026

This study addresses the critical gap in flash flood early warning for the Haor wetlands of Bangladesh and the inability of existing models to adequately represent backwater dynamics. To overcome these challenges, the authors propose a deseasonalized machine learning ensemble approach that leverages Otsu-threshold-based change detection from Sentinel-1 SAR imagery to construct a proxy indicator for upstream Barak River conditions. A weighted ensemble of Random Forest and XGBoost models enables 72-hour probabilistic flood forecasting, integrated with a three-tier alert system and a BRRI-calibrated rice loss assessment module. By explicitly removing temperature-driven seasonal biases that artificially inflate accuracy, the framework delivers actionable lead times of approximately 36 hours. Evaluated against 77 historical flood events, the model achieves 89.6% accuracy, 87.5% recall, and an AUC-ROC of 0.943, with spatial validation showing 84–91% agreement.

0 citationsRead paper