Q1. Evaluate the role of emerging technologies across the disaster management cycle in India, with special reference to recent governmental innovations and institutional mechanisms.
Analytical Focus for Answer (AFfA):
- Show full-cycle coverage: Prevention → Preparedness → Response → Recovery, with tech examples (AI maps, SATARK, drones, VR, CAP alerts, 3D printing).
- Critically assess impact: Improved forecasting, faster warnings, targeted response, better reconstruction.
- Examine institutional innovations: NDMP, NDMA guidelines, IMD satellite/radar systems, Web-DCRA, Flood Hazard Atlas, CAP & Cell Broadcast.
- Integrate state and community systems: OSDMA SATARK, local simulations, mock drills.
- Highlight limitations: Cost, digital divide, poor data quality, privacy, gendered access gaps.
- Suggest concrete reforms: Capacity building, private sector participation, inclusive tech design, data responsibility norms.
Model Answer
Introduction
Disaster management in India is shifting from a reactive emergency-relief approach to a comprehensive cycle that covers mitigation, preparedness, response and recovery. The use of emerging technologies has strengthened this transition by improving risk identification, early warning, coordination and reconstruction. At the same time, these gains depend heavily on institutional readiness, data quality, and equitable access. The answer evaluates the expanding technological landscape of disaster management in India, while critically analysing its achievements and the challenges that influence its long-term effectiveness.
Body
- Hazard mapping: AI-enabled modelling and geospatial tools support early identification of risk-prone areas. They guide infrastructure planning and reduce exposure.
- Risk atlases: NRSC’s flood-hazard atlases and BMTPC’s digital hazard atlas provide state-specific multi-hazard vulnerability profiles. They help governments prioritise mitigation.
- Glacial-lake data: Remote sensing strengthens Himalayan risk awareness, though the accuracy and frequency of updates remain varied across regions.
- Early warning systems: INSAT-3D/3DR satellites, Doppler radars and automated weather stations enhance IMD’s impact-based forecasting. They support granular alerts.
- Digital alerting: NDMA’s CAP-based SACHET system disseminates geo-targeted warnings across 36 States/UTs. It uses SMS, radio, TV, sirens and browser notifications.
- State innovation: Platforms like OSDMA’s SATARK illustrate decentralised preparedness. They monitor hazards such as lightning, heatwaves and drought.
- Training simulation: AR/VR systems aid community-training and drills. They improve behavioural readiness during disasters.
- Response coordination: Satellite imagery, drones and social-media analytics offer rapid situational awareness. They help locate vulnerable populations.
- Search and rescue: Drone missions, as seen during the Wayanad landslide operations, assist identification of trapped survivors and inaccessible zones.
- Emergency communication: AI-enabled chatbots and automated helplines help manage public queries and reduce pressure on frontline agencies.
- Damage assessment: High-resolution imagery aids rapid evaluation of affected households and infrastructure. This speeds up relief targeting.
- Resource allocation: Drone-based delivery of medicines and critical supplies supports remote-area access.
- Reconstruction tools: Decision-support systems like Web-DCRA guide post-cyclone recovery planning through integrated datasets.
- 3D technologies: Additive manufacturing helps produce replacement components, though scale and institutional uptake remain modest.
- Institutional innovation: NDMP 2019 mainstreams tech-based multi-hazard management across all administrative levels.
- Network expansion: IUINDRR connects universities for DRR-related training and curriculum development. It expands research capacity.
- Regional leadership: Through CDRI, India supports infrastructure resilience in Small Island Developing States.
- Global engagement: Platforms like G20 DRR Working Group and ITEWC enhance India’s role as a regional early-warning provider.
- Cost challenge: High deployment and maintenance costs restrict adoption in poorer districts.
- Data limitation: Issues of accuracy, timeliness and relevance weaken AI models and real-time decision making.
- Digital divide: Limited access among women and marginalised groups weakens the reach of tech-based preparedness.
- Coordination gap: Uneven institutional capacity across states reduces the effectiveness of national platforms.
- Privacy concern: Use of social-media data and drones raises concerns of data protection during crises.
Conclusion
India’s disaster-management landscape has been strengthened considerably through the integration of technology across mitigation, preparedness, response and recovery. The proliferation of satellite-based warnings, digital alerting systems, hazard atlases and drone-enabled assessments demonstrates clear progress. Yet, technology is only as effective as the institutions, data ecosystems and community networks that support it. Addressing uneven capacity, reducing social exclusion, and embedding responsible data frameworks remain essential for transforming technological capability into a resilient and inclusive disaster-management system.
Q2. Discuss the challenges associated with adopting advanced technologies such as AI, remote sensing, drones and cell-broadcast systems in India’s disaster risk reduction framework.
Analytical Focus for Answer (AFfA):
- Core barriers: High cost, technical limitations, poor digital infrastructure, skill deficits.
- Data challenges: Access, quality, timeliness, relevance; real-time decision quality issues.
- Ethical concerns: Privacy, integrity, sensitive data misuse.
- Social aspects: Digital divide, especially gender-based access inequities.
- Institutional constraints: Fragmented coordination, uneven state capacity, limited local-level adoption.
- Way forward: Capacity building, responsible data governance, public–private R&D partnerships, standardization.
Model Answer
Introduction
India’s growing reliance on AI-assisted modelling, remote sensing, drone-based surveillance and cell-broadcast systems represents an important step toward proactive disaster risk reduction. However, the promise of these technologies is unevenly realised due to structural, financial, institutional and social constraints. The constraints weaken the reliability, accessibility and inclusiveness of tech-enabled disaster governance. The answer examines these barriers and evaluates their implications for long-term resilience.
Body
- Infrastructure gap: Many districts lack radar coverage, micro-weather stations and drone-operation facilities, limiting real-time monitoring.
- Connectivity issue: Remote areas face weak networks and irregular power supply, affecting sensors and alerts.
- Hardware limitation: Drones and high-resolution satellites require specialised maintenance beyond local capacities.
- Cost burden: AI platforms and geospatial tools require heavy investment; poorer states struggle to sustain them.
- Operational expense: Software upgrades, data storage and staffing drive recurring costs.
- Data accuracy: Incomplete or outdated datasets weaken forecasts; ground-truthing is inconsistent.
- Data relevance: Centralised models may ignore micro-terrain variations, especially in Himalayan and coastal regions.
- Data ethics: Drone footage, social-media inputs and tracking raise privacy concerns.
- Access inequality: Women, elderly and marginalised groups often lack access to tech-based alerts.
- Language constraints: Alerts in dominant languages exclude linguistic minorities without localisation.
- Trust deficit: Digital alerts may be ignored if unfamiliar or poorly explained.
- Coordination weakness: Overlapping mandates between NDMA, IMD, state and district agencies slow execution.
- Capacity constraint: Personnel lack skills to interpret satellite data, run AI models or operate drones.
- Sustainability issue: Pilot technologies stagnate without maintenance systems and long-term funding.
- Governance need: Stronger data-governance norms are needed for secure and responsible data use.
- PPP potential: Private partnerships can support innovation but require standardisation and accountability.
- Community integration: Last-mile engagement and training are essential for converting alerts into action.
Conclusion
Advanced technologies can significantly improve India’s capacity to anticipate, plan for and respond to disasters. Yet their adoption is constrained by infrastructure deficits, high operating costs, inconsistent data, and deep social inequalities. Without investment in local capacity, interoperable governance systems and inclusive digital access, these benefits remain limited. A resilient DRR framework must pair technological sophistication with community preparedness, responsible data governance and institutional coherence.