APSC Current Affairs: Assam Tribune Notes with MCQs and Answer Writing (11/08/2025)
For APSC CCE and other Assam Competitive examinations aspirants, staying updated with current affairs is vital. This blog covers most important topics from the Assam Tribune today (09-08-2025). These issues are key for both APSC Prelims and Mains preparation, offering insights into the APSC CCE Syllabus.
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🛡️ Assam Police to Implement AI-Based Crime Prediction System
📘 GS Paper 3: Internal Security | Science & Technology | Cyber Security
📘 GS Paper 2: Governance | E-Governance Initiatives
📘 GS Paper 5 (Assam): Law and Order | Police Modernisation in Assam
🔹 Introduction
The Assam Police has announced the introduction of an AI-based Crime Prediction and Analysis System aimed at improving law enforcement efficiency, resource deployment, and crime prevention. This aligns with the broader vision of Smart Policing under the Assam Police Modernisation Strategy and the Digital India initiative.
🔑 Key Points
| Feature | Details |
| Implementing Agency | Assam Police in collaboration with tech partners. |
| Technology Used | Artificial Intelligence (AI), Machine Learning (ML), Big Data Analytics. |
| Primary Objective | Predict crime hotspots, identify repeat offenders, and plan preventive measures. |
| Data Sources | FIR databases, CCTV feeds, social media analytics, geospatial data. |
| Expected Benefits | Faster response, reduced crime rates, improved public trust. |
| Pilot Areas | Likely to start in urban centres like Guwahati, Dibrugarh, and Silchar. |
🧠 Prelims Pointers
Crime Mapping: Geospatial mapping of crime incidents to identify trends.
Predictive Policing: Use of AI/ML to forecast potential crimes based on historical patterns.
National Crime Records Bureau (NCRB): Maintains nationwide crime data in India.
Crime and Criminal Tracking Network & Systems (CCTNS): A nationwide police digitisation project.
UNODC Model: United Nations Office on Drugs and Crime advocates AI in policing with safeguards.
📝 Mains Pointers
A. Importance for Assam
Efficient Resource Deployment – Police personnel and vehicles can be strategically placed in high-risk areas.
Crime Prevention – Focus shifts from reactive policing to proactive prevention.
Evidence-Based Governance – Decisions backed by data analytics improve transparency.
Public Safety in Urban Areas – Supports rapid response to rising urban crimes.
Integration with Smart City Projects – Aligns with Guwahati’s Smart City Mission.
B. Challenges
| Challenge | Explanation |
| Data Privacy Concerns | Risk of misuse or unauthorised access to personal data. |
| Algorithmic Bias | AI systems may reflect societal or historical biases in policing data. |
| Infrastructure Gaps | Rural and remote areas lack necessary digital infrastructure. |
| Cybersecurity Threats | System could be targeted by hackers. |
| Training Deficit | Need for skilled police personnel to interpret AI outputs. |
C. Government Initiatives
Assam Police Citizen Portal – For public grievance redressal and service requests.
National AI Mission – Promotes AI adoption across governance sectors.
CCTNS Integration – Assam Police digitisation drive to link all police stations.
NCRB Modernisation Schemes – Standardisation of crime data formats for analytics.
D. Way Ahead
Robust Data Protection Framework – Align with the Digital Personal Data Protection Act, 2023.
Bias Mitigation in AI – Use diverse datasets and independent audits of algorithms.
Capacity Building – Train officers in data interpretation and cyber forensics.
Community Engagement – Public awareness campaigns on the benefits and safeguards of AI policing.
Pilot & Scale Approach – Start in high-crime zones and gradually expand coverage.
🧩 Conclusion
The AI-based crime prediction system is a transformative step in modernising Assam’s policing. If implemented with strong privacy protections, transparency, and community trust, it can significantly enhance safety, deter crime, and set a benchmark for tech-enabled law enforcement in India.
🛰️ ISRO to Launch GISAT-2 for Real-Time Earth Observation
📘 GS Paper 3: Science & Technology | Space Technology | Disaster Management
📘 GS Paper 1: Geography – Remote Sensing Applications
📘 GS Paper 5 (Assam): Space Applications for NE Development
🔹 Introduction
The Indian Space Research Organisation (ISRO) has announced plans to launch GISAT-2, a geostationary earth observation satellite, designed for real-time, high-resolution monitoring of India’s borders, disaster zones, and agricultural landscapes. It builds upon the capabilities of GISAT-1, which was lost during launch in 2021.
🔑 Key Points
| Feature | Details |
| Full Form | Geo Imaging Satellite – 2. |
| Orbit Type | Geostationary orbit (~36,000 km altitude). |
| Coverage | Continuous view of the Indian subcontinent and surrounding areas. |
| Applications | Border surveillance, disaster management, crop monitoring, forest cover mapping. |
| Payload | High-resolution multispectral, hyperspectral, and panchromatic cameras. |
| Operator | ISRO in collaboration with National Remote Sensing Centre (NRSC). |
🧠 Prelims Pointers
Geostationary Orbit: Satellites appear fixed over a point on Earth, useful for continuous coverage.
Difference from Sun-Synchronous Orbit: Sun-synchronous satellites (like Cartosat series) provide periodic global coverage; geostationary gives continuous local coverage.
NRSC: Part of ISRO, responsible for satellite data acquisition and processing.
Bhuvan Platform: ISRO’s geoportal for satellite imagery and thematic maps.
Disaster Management Support Programme: ISRO’s initiative for rapid satellite-based disaster response.
📝 Mains Pointers
A. Importance for India & Assam
Border & Security Monitoring – Continuous observation of sensitive areas along NE borders with China, Myanmar, and Bangladesh.
Flood Monitoring in Assam – Real-time imaging can track river swelling and inundation patterns.
Agricultural Planning – Crop health and acreage estimation for schemes like PMFBY.
Disaster Response – Near-instant imaging of landslides, forest fires, and earthquakes.
Climate Studies – Monitoring glacier retreat and land use change in NE India.
B. Challenges
| Challenge | Explanation |
| High Development Cost | Space technology requires significant investment. |
| Data Security Concerns | Sensitive imagery could be targeted by cyber threats. |
| Technical Complexity | Geostationary imaging at high resolution is challenging due to distance. |
| Inter-Agency Coordination | Requires integration between ISRO, disaster agencies, and state governments. |
C. Govt Initiatives
SpaceCom Policy 2023 – Liberalises satellite operations for public-private collaboration.
North Eastern Space Applications Centre (NESAC) – Special focus on space applications for NE development.
Digital India GIS Portal – Integrates satellite imagery with governance.
National Disaster Management Plan – Incorporates satellite-based decision-making.
D. Way Ahead
State-Level Data Utilisation Units – Build capacity in Assam and NE states to use GISAT data.
Public–Private Partnerships – Involve Indian startups in satellite analytics.
Integration with AI – Automate flood/drought alerts using satellite feeds.
International Collaboration – Share data for regional disaster preparedness with neighbouring countries.
🧩 Conclusion
The launch of GISAT-2 will mark a significant leap in real-time earth observation for India, enhancing disaster preparedness, security, and agricultural productivity. For Assam and the North-East, it offers a powerful tool to address chronic challenges like floods, border monitoring, and climate change impacts.
🌾 Assam to Introduce Digital Crop Survey for Precision Agriculture
📘 GS Paper 3: Agriculture | E-Technology in Aid of Farmers | Data Governance
📘 GS Paper 2: Government Policies & Interventions | E-Governance
📘 GS Paper 5 (Assam): State Agricultural Development Initiatives
🔹 Introduction
The Government of Assam has decided to launch a Digital Crop Survey programme to modernise agricultural data collection, replacing outdated manual surveys. This will integrate satellite imagery, mobile applications, and AI analytics to create a real-time, accurate database for agricultural planning and policy-making.
🔑 Key Points
| Feature | Details |
| Implementing Agency | Assam Agriculture Department in collaboration with NESAC & NIC. |
| Technology Used | Satellite remote sensing, geo-tagged mobile surveys, AI-based crop classification. |
| Objective | Improve accuracy of crop data for insurance, subsidies, procurement, and disaster compensation. |
| Data Sources | Farmer declarations, Kisan Credit Card (KCC) databases, weather and soil maps. |
| Benefits | Faster scheme delivery, reduced fraud, targeted support to farmers. |
| Pilot Districts | Likely in flood-affected areas and high-value crop belts. |
🧠 Prelims Pointers
Digital Crop Survey (DCS): National initiative led by the Ministry of Agriculture for uniform crop data.
PMFBY: Pradhan Mantri Fasal Bima Yojana – crop insurance scheme dependent on accurate acreage data.
Bhuvan Platform: ISRO portal that hosts agricultural and land use maps.
Geo-Tagging: Adding location metadata to photographs/data points for verification.
NESAC: North Eastern Space Applications Centre, Shillong – specialises in remote sensing for NE India.
📝 Mains Pointers
A. Importance for Assam
Flood Compensation Accuracy – Real-time crop loss assessment in flood-prone districts.
Targeted Policy Formulation – Evidence-based planning for irrigation, seeds, and fertilisers.
Transparency in Subsidies – Prevents ghost beneficiaries in agricultural schemes.
Market Linkages – Data helps plan aggregation and marketing of surplus crops.
Support for Climate-Resilient Agriculture – Aligns with Assam’s Climate-Resilient Agriculture Mission.
B. Challenges
| Challenge | Explanation |
| Digital Divide | Limited digital literacy among small farmers. |
| Connectivity Issues | Remote areas lack stable internet for app-based surveys. |
| Data Privacy Concerns | Need for secure storage and farmer consent protocols. |
| Inter-Departmental Coordination | Agriculture, revenue, and disaster management must work in sync. |
C. Govt Initiatives
National Crop Insurance Portal – Unified platform for PMFBY claims and assessments.
Digital India Land Records Modernization Programme (DILRMP) – Links land records with crop survey data.
Assam Agritech Mission – Promotes technology adoption in farming.
Use of GIS in Assam Flood Relief – For rapid damage assessment.
D. Way Ahead
Farmer Training – Conduct workshops for app usage and data submission.
Offline Data Collection Modes – Ensure inclusion in low-connectivity areas.
AI-Based Verification – Cross-check farmer claims with satellite and drone imagery.
Integration with Crop Advisory Services – Use data for personalised weather and pest alerts.
🧩 Conclusion
The Digital Crop Survey is a game-changer for Assam’s agricultural governance, enabling precision agriculture, transparent subsidies, and efficient disaster compensation. With proper implementation and farmer inclusion, it can become a model for data-driven rural development in India.
🏭 Assam Petrochemicals Ltd to Commission Green Methanol Plant
📘 GS Paper 3: Infrastructure | Energy | Environment & Climate Change Mitigation
📘 GS Paper 2: Government Policies & Interventions | Industrial Development
📘 GS Paper 5 (Assam): State Industrial Policy | Energy Transition in Assam
🔹 Introduction
Assam Petrochemicals Ltd (APL) is set to commission India’s first large-scale Green Methanol Plant in the North-East. This facility will produce methanol using renewable energy and carbon capture technologies, aiming to reduce fossil fuel dependency and promote sustainable industrial growth.
🔑 Key Points
| Feature | Details |
| Location | Namrup, Dibrugarh district, Assam. |
| Production Capacity | ~100 tonnes per day (TPD) of green methanol. |
| Feedstock | Renewable hydrogen (from electrolysis) + captured CO₂. |
| Technology Partners | Collaboration with global clean energy firms. |
| End Uses | Marine fuel, chemical manufacturing, power generation, transport fuel. |
| Funding & Support | State Industrial Policy + central green energy incentives. |
🧠 Prelims Pointers
Methanol Economy: Concept promoting methanol as a clean energy carrier and industrial feedstock.
Green Methanol vs. Grey Methanol: Green is made from renewable sources; grey is from fossil fuels.
National Policy on Biofuels (NPB) 2018: Includes methanol blending in transport fuel strategy.
International Maritime Organization (IMO): Endorses methanol as a low-carbon marine fuel.
Namrup Industrial Complex: Known for fertilisers, petrochemicals, and energy-intensive industries.
📝 Mains Pointers
A. Importance for Assam
Energy Transition Leader – Positions Assam as a pioneer in green fuels in the NE region.
Industrial Diversification – Expands chemical manufacturing beyond conventional petrochemicals.
Export Potential – Green methanol in demand for shipping and global decarbonisation goals.
Job Creation – Skilled and semi-skilled employment in manufacturing and logistics.
Environmental Benefits – Reduces CO₂ emissions, air pollutants, and fossil fuel use.
B. Challenges
| Challenge | Explanation |
| High Production Costs | Renewable hydrogen and carbon capture are still expensive. |
| Infrastructure Gaps | Storage, transport, and blending facilities need development. |
| Market Development | Domestic methanol demand in transport and power still emerging. |
| Technology Dependence | Relies on imported equipment and know-how initially. |
C. Govt Initiatives
National Green Hydrogen Mission – Promotes hydrogen production for fuels like methanol.
SATAT Scheme – Encourages compressed bio-gas and other clean fuels.
Assam Industrial & Investment Policy 2022 – Incentives for green industry investment.
IMO 2030 Targets – Cutting shipping emissions by 40% by 2030, creating global demand.
D. Way Ahead
Scaling Production – Economies of scale to reduce costs.
Public–Private Partnerships – Collaborations for R&D and market expansion.
Integration with Renewable Energy Projects – Solar and hydro for green hydrogen.
Policy Push for Methanol Blending – Mandating methanol use in marine and road transport fuels.
🧩 Conclusion
The commissioning of APL’s Green Methanol Plant is a milestone for Assam’s industrial and energy transition journey. If coupled with robust market development, infrastructure, and policy support, Assam can emerge as a hub for clean fuel innovation in India’s North-East.
APSC Prelims Practice Questions
1. AI-Based Crime Prediction System in Assam
Q1. With reference to Predictive Policing, consider the following statements:
- It uses Artificial Intelligence to analyse historical crime data to forecast potential criminal activity.
- The Crime and Criminal Tracking Network & Systems (CCTNS) is a purely state-level initiative without central coordination.
- Predictive policing has been endorsed by the United Nations Office on Drugs and Crime (UNODC) with guidelines on ethical AI use.
Which of the statements given above is/are correct?
A. 1 and 2 only
B. 1 and 3 only
C. 2 and 3 only
D. 1, 2 and 3
✅ Answer: B. 1 and 3 only
Explanation: CCTNS is a centrally coordinated project under NCRB; predictive policing uses AI/ML for hotspot forecasting; UNODC has issued ethical guidelines.
Q2. Which of the following is/are part of modern “Smart Policing” initiatives in India?
- Facial recognition integrated with CCTV surveillance.
- Drone-based crowd monitoring.
- AI-enabled crime mapping.
- Blockchain-based FIR registration.
Select the correct answer using the code below:
A. 1, 2 and 3 only
B. 1, 3 and 4 only
C. 2 and 4 only
D. 1, 2, 3 and 4
✅ Answer: A. 1, 2 and 3 only
Explanation: Blockchain-based FIR registration is not in mainstream deployment in India.
2. GISAT-2 Launch by ISRO
Q3. Consider the following pairs:
| Satellite | Primary Function |
| 1. Cartosat-3 | High-resolution mapping |
| 2. RISAT-2B | All-weather radar imaging |
| 3. GISAT-2 | Real-time earth observation from geostationary orbit |
How many of the above pairs are correctly matched?
A. Only one
B. Only two
C. All three
D. None
✅ Answer: C. All three
Explanation: All three satellites serve the functions mentioned; GISAT-2 is geostationary for continuous coverage.
Q4. Which of the following statements correctly distinguishes a geostationary orbit from a sun-synchronous orbit?
A. Geostationary orbits are low-earth orbits, whereas sun-synchronous orbits are high-earth orbits.
B. Geostationary orbits maintain a constant position relative to the Earth’s surface, whereas sun-synchronous orbits pass over the same region at the same local solar time.
C. Sun-synchronous orbits are used for weather monitoring, whereas geostationary orbits are used only for communications.
D. Geostationary orbits are always polar, whereas sun-synchronous orbits are equatorial.
✅ Answer: B
Explanation: Geostationary stays fixed relative to Earth’s rotation; sun-synchronous maintains consistent local solar time overpasses.
3. Digital Crop Survey in Assam
Q5. In the context of agricultural data collection in India, consider the following:
- Digital Crop Survey uses remote sensing and geo-tagged field surveys for crop identification.
- The programme is implemented only in Assam.
- Accurate crop data can improve delivery of schemes like PMFBY and MSP procurement.
Which of the statements given above is/are correct?
A. 1 and 3 only
B. 2 and 3 only
C. 1 and 2 only
D. 1, 2 and 3
✅ Answer: A. 1 and 3 only
Explanation: Digital Crop Survey is a national initiative; Assam is adopting it as part of a larger programme.
Q6. Which of the following institutions are directly involved in satellite-based agricultural monitoring in the North-East?
- North Eastern Space Applications Centre (NESAC)
- Indian Council of Agricultural Research (ICAR)
- National Remote Sensing Centre (NRSC)
Select the correct answer using the code below:
A. 1 and 3 only
B. 2 and 3 only
C. 1 and 2 only
D. 1, 2 and 3
✅ Answer: D. 1, 2 and 3
Explanation: NESAC, ICAR, and NRSC all have roles in agricultural monitoring and data integration.
4. Green Methanol Plant in Assam
Q7. Which of the following are sources for producing green methanol?
- Renewable hydrogen from electrolysis.
- Captured carbon dioxide from industrial processes.
- Biomass gasification.
- Methane extracted from coal seams.
Select the correct answer using the code below:
A. 1 and 2 only
B. 1, 2 and 3 only
C. 2, 3 and 4 only
D. 1, 2, 3 and 4
✅ Answer: B. 1, 2 and 3 only
Explanation: Methane from coal seams is fossil-based and not part of “green” methanol production.
Q8. The concept of a “Methanol Economy” is primarily aimed at:
A. Replacing fossil fuels with methanol as a clean energy carrier and feedstock.
B. Using methanol only for medical purposes.
C. Making methanol the base for all plastics manufacturing.
D. Producing methanol only for export purposes.
✅ Answer: A
Explanation: The methanol economy concept focuses on using methanol from renewable or low-carbon sources for transport, power, and industry.
APSC Mains Practice Question
GS Paper 3 – Internal Security | Science & Technology | Cyber Security
(Also relevant to GS Paper 2 – Governance and GS Paper 5 – Assam Specific)
Q. Discuss the potential of AI-based crime prediction systems in improving law and order in states like Assam. What are the key challenges and safeguards needed for their effective implementation?
Introduction
Predictive policing, powered by Artificial Intelligence (AI) and Machine Learning (ML), involves analysing historical crime data and real-time inputs to forecast potential hotspots and patterns of criminal activity. In 2025, the Assam Police announced the adoption of an AI-based Crime Prediction and Analysis System, making it among the first in the North-East to integrate such advanced tools into law enforcement.
Body
I. Potential Benefits for Assam
- Proactive Crime Prevention
- Shifts policing from reactive investigation to preventive deployment.
- Identifies likely crime locations and timings, enabling strategic patrolling.
- Efficient Resource Deployment
- Optimises allocation of limited police personnel and vehicles in high-risk zones.
- Enhanced Urban Security
- Useful in Guwahati, Dibrugarh, and other urban centres facing rising crimes.
- Integration with Smart City Initiatives
- Can be linked to CCTV networks, drone surveillance, and GPS-enabled patrol units.
- Data-Driven Decision Making
- Eliminates guesswork and enables evidence-based strategies for law and order.
II. Relevance to Assam’s Security Context
- Border Challenges – Continuous monitoring in sensitive districts near Bangladesh and Myanmar.
- Ethnic and Communal Tensions – Early detection of patterns preceding violence.
- Cybercrime & Financial Fraud – AI algorithms can detect anomalies in digital transaction patterns.
III. Key Challenges
| Challenge | Explanation |
| Data Privacy Risks | Potential misuse of citizens’ personal data without strong safeguards. |
| Algorithmic Bias | If past policing data has biases, AI may replicate and reinforce them. |
| Digital Infrastructure Gaps | Many rural areas lack the necessary connectivity and hardware. |
| Cybersecurity Threats | AI systems could be hacked or manipulated. |
| Capacity Deficit | Need for trained officers to interpret AI outputs correctly. |
IV. Government Initiatives & Legal Safeguards
- Crime and Criminal Tracking Network & Systems (CCTNS) – Digitisation of FIRs and case data.
- Digital Personal Data Protection Act, 2023 – Framework for lawful data processing.
- National Cyber Crime Reporting Portal – For reporting online offences.
- UNODC Guidelines – International ethical principles for AI in law enforcement.
V. Way Forward
- Privacy-by-Design Systems – Ensure minimal and consent-based data collection.
- Bias Audits – Independent algorithm review to detect and correct unfair patterns.
- Training & Capacity Building – Equip officers with AI interpretation and cyber skills.
- Pilot Implementation – Test in select urban areas before statewide rollout.
- Public Communication – Build citizen trust through transparency about AI use.
Conclusion
The AI-based crime prediction system is a transformative policing innovation for Assam, offering the promise of safer cities, efficient policing, and data-driven governance. However, without robust privacy safeguards, bias mitigation, and community trust, such technology risks undermining civil liberties. A balanced, ethical, and transparent approach can ensure that Assam becomes a model for responsible AI adoption in law enforcement.
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