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APSC Answer Writing (Daily) based on Assam Tribune – 06/03/2026

For APSC CCE and other Assam Competitive examinations aspirants, practicing Daily Answer Writing is vital. This blog covers the most important Main question and its model Answer from the Assam Tribune today (06-03-2026).

📝 Model Question (250 Words)

Question

Technological innovations are increasingly being used for disaster risk reduction in India.
Discuss the significance of sensor-based and AI-enabled landslide early warning systems, such as the one developed by IIT Mandi, in strengthening disaster management in mountainous regions. Also examine the challenges in implementing such systems across India.

(Answer in ~250 words)

Model Answer

Introduction

Landslides are a major natural hazard in India’s Himalayan and hill regions, frequently causing loss of life, infrastructure damage, and disruption of transport networks. In response, researchers at IIT Mandi have developed a real-time landslide monitoring and early warning system that uses sensors, machine learning, and real-time analytics to detect minute ground movements and issue timely alerts to authorities and communities.

Significance of AI-Enabled Landslide Early Warning Systems

1. Early Detection and Risk Reduction

Sensors detect ground movements as small as one millimetre, enabling early identification of potential landslides.
Timely alerts can reduce casualties and property damage.

2. Real-Time Monitoring

Continuous monitoring through sensor networks and digital platforms allows faster response compared to traditional methods.

3. Integration with Disaster Management Systems

Alerts can be transmitted via SMS, mobile applications, and warning signals to authorities and communities.

4. Infrastructure Protection

Helps safeguard roads, railways, hydropower projects, and settlements located in vulnerable mountain regions.

5. Climate Change Adaptation

Increased rainfall variability due to climate change has heightened landslide risks, making predictive technologies essential.

Challenges in Implementation

1. High Cost of Technology

Installation and maintenance of sensors and monitoring systems require significant investment.

2. Limited Geographic Coverage

Difficult terrain and remote locations hinder large-scale deployment.

3. Data Integration Issues

Effective prediction requires combining geological, meteorological, and satellite data.

4. Institutional Coordination

Multiple agencies such as NDMA, GSI, and state disaster authorities must coordinate effectively.

5. Community Preparedness

Early warnings are useful only if local communities understand and respond appropriately.

Way Forward

Expand sensor-based monitoring networks across landslide-prone regions.
Integrate satellite data, GIS, and AI-based predictive models.
Strengthen community awareness and disaster preparedness programmes.

Conclusion Technological innovations like AI-enabled landslide monitoring systems represent a transformative step in disaster management. By enabling early detection, timely warnings, and improved preparedness, such systems can significantly enhance resilience in India’s vulnerable mountainous regions.

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