Course Structure Overview
| Semester | Course Code | Full Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | DM-101 | Introduction to Disaster Management | 3-0-0-3 | - |
| 1 | DM-102 | Environmental Science and Sustainability | 3-0-0-3 | - |
| 1 | DM-103 | Basic Engineering for Disaster Resilience | 3-0-0-3 | - |
| 1 | DM-104 | Introduction to GIS and Remote Sensing | 2-0-0-2 | - |
| 1 | DM-105 | Public Policy and Governance in Disasters | 3-0-0-3 | - |
| 1 | DM-106 | Introduction to Risk Assessment | 2-0-0-2 | - |
| 1 | DM-107 | Disaster Communication and Public Awareness | 3-0-0-3 | - |
| 2 | DM-201 | Hazard Identification and Classification | 3-0-0-3 | DM-101, DM-102 |
| 2 | DM-202 | Disaster Mitigation Strategies | 3-0-0-3 | DM-101, DM-201 |
| 2 | DM-203 | Civil Infrastructure and Resilience | 3-0-0-3 | DM-103 |
| 2 | DM-204 | Hydrology and Flood Risk Modeling | 2-0-0-2 | DM-102 |
| 2 | DM-205 | Emergency Management Systems | 3-0-0-3 | DM-101, DM-201 |
| 2 | DM-206 | Psychology of Disasters and Human Behavior | 2-0-0-2 | - |
| 2 | DM-207 | Sustainable Development and Climate Change | 3-0-0-3 | DM-102 |
| 3 | DM-301 | Data Analytics for Disaster Response | 3-0-0-3 | DM-205, DM-104 |
| 3 | DM-302 | Urban Resilience Engineering | 3-0-0-3 | DM-203 |
| 3 | DM-303 | Disaster Risk Assessment and Mapping | 3-0-0-3 | DM-201, DM-206 |
| 3 | DM-304 | Cybersecurity in Critical Infrastructure | 2-0-0-2 | DM-103 |
| 3 | DM-305 | Humanitarian Logistics and Supply Chain | 3-0-0-3 | DM-205 |
| 3 | DM-306 | Disaster Communication and Media Strategy | 2-0-0-2 | DM-107 |
| 3 | DM-307 | Post-Disaster Reconstruction Techniques | 3-0-0-3 | DM-202, DM-203 |
| 4 | DM-401 | Advanced Machine Learning for Hazards | 3-0-0-3 | DM-301 |
| 4 | DM-402 | Community-Based Disaster Risk Reduction | 3-0-0-3 | DM-303 |
| 4 | DM-403 | Climate Adaptation and Mitigation Strategies | 3-0-0-3 | DM-207 |
| 4 | DM-404 | Disaster Response Simulation Lab | 1-0-0-1 | DM-305 |
| 4 | DM-405 | Disaster Management Capstone Project | 4-0-0-4 | All previous semesters |
| 4 | DM-406 | Research Methodology in Disaster Studies | 2-0-0-2 | - |
| 4 | DM-407 | Disaster Ethics and Human Rights | 2-0-0-2 | - |
Advanced Departmental Electives
The department offers several advanced elective courses that allow students to specialize in niche areas of disaster management. These courses are designed to provide in-depth knowledge and practical skills needed for real-world applications.
- Machine Learning for Hazard Prediction: This course explores how machine learning algorithms can be applied to predict natural hazards like earthquakes, floods, and cyclones. Students learn to use Python libraries such as scikit-learn, TensorFlow, and Keras to build predictive models using historical data.
- Urban Flood Risk Modeling: Students study the hydrological and hydraulic processes involved in urban flooding. Using software tools like HEC-RAS and SWMM, they model flood scenarios and evaluate mitigation strategies for low-lying areas in cities.
- Disaster Psychology and Behavioral Resilience: This course examines psychological factors influencing behavior during disasters. Topics include trauma recovery, group dynamics, and community-level resilience building strategies.
- GIS Mapping for Disaster Management: A hands-on course covering the use of GIS in disaster preparedness, response, and recovery. Students learn to create risk maps, analyze spatial data, and develop decision-support systems using ArcGIS and QGIS.
- Early Warning Systems Design: Focuses on designing and implementing early warning systems for various types of disasters. Students explore sensor networks, communication protocols, and public alert mechanisms tailored to local contexts.
- Cybersecurity in Emergency Communication: Teaches students about securing communication channels during emergencies. Topics include network security, data encryption, threat analysis, and incident response planning in critical infrastructure environments.
- Disaster Recovery and Reconstruction Planning: Covers the process of rebuilding after disasters. Students learn to develop recovery plans that address economic, social, and environmental factors while ensuring long-term resilience.
- Humanitarian Logistics and Supply Chain Management: This course explores supply chain logistics in humanitarian settings. Students study procurement, transportation, warehouse management, and coordination strategies for delivering aid during disasters.
- Climate Change and Adaptation Strategies: Analyzes the impact of climate change on disaster frequency and intensity. Students learn to design adaptation strategies using frameworks like the IPCC guidelines and integrate sustainable development goals into policy-making.
- Community-Based Risk Reduction: Emphasizes grassroots-level initiatives that empower communities to prepare for and respond to disasters. Students engage in participatory approaches, community mapping, and collaborative governance models to build local resilience.
Project-Based Learning Philosophy
The department strongly believes in experiential learning through project-based assignments. The curriculum integrates mini-projects throughout the academic journey, culminating in a final-year capstone thesis or project.
Mini-projects are assigned in the third year and involve small groups of students working on real-world problems provided by faculty members or industry partners. These projects help students apply theoretical concepts to practical situations, develop teamwork skills, and enhance their problem-solving abilities.
The final-year project is a significant component of the program, requiring students to conduct independent research or collaborate with external organizations on complex disaster management challenges. Projects are supervised by faculty mentors who guide students through the research process, data collection, analysis, and presentation.
Students can choose from a wide range of topics related to their interests or industry needs. For example, a group might develop an AI-powered flood prediction model, create a community resilience plan for a vulnerable district, or evaluate the effectiveness of early warning systems in rural areas. The evaluation criteria include innovation, technical rigor, impact potential, and presentation quality.