IELTS Reading: Role of Technology in Disaster Management – Đề thi mẫu có đáp án chi tiết

Mở bài

Vai trò của công nghệ trong quản lý thảm họa là một chủ đề ngày càng phổ biến trong các kỳ thi IELTS Reading, đặc biệt trong bối cảnh biến đổi khí hậu và các thiên tai đang gia tăng trên toàn cầu. Chủ đề Role Of Technology In Disaster Management không chỉ xuất hiện trong các đề thi gần đây mà còn phản ánh xu hướng đánh giá khả năng đọc hiểu các văn bản khoa học công nghệ và xã hội của thí sinh.

Trong bài viết này, bạn sẽ được luyện tập với một đề thi IELTS Reading hoàn chỉnh gồm 3 passages với độ khó tăng dần từ Easy đến Hard. Bạn sẽ trải nghiệm đầy đủ các dạng câu hỏi phổ biến nhất trong kỳ thi thật, từ Multiple Choice, True/False/Not Given đến Matching Headings và Summary Completion. Mỗi passage được thiết kế cẩn thận để mô phỏng chính xác cấu trúc và độ khó của đề thi Cambridge IELTS.

Đặc biệt, đề thi này cung cấp đáp án chi tiết kèm giải thích cụ thể về vị trí thông tin, kỹ thuật paraphrase và các từ vựng quan trọng được phân loại theo từng passage. Bài viết này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn làm quen với các văn bản học thuật về công nghệ và phát triển kỹ năng làm bài hiệu quả.

1. Hướng dẫn làm bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test là một phần thi quan trọng trong kỳ thi IELTS Academic, đánh giá khả năng đọc hiểu và phân tích thông tin của thí sinh. Bạn sẽ có 60 phút để hoàn thành 3 passages với tổng cộng 40 câu hỏi. Điều quan trọng là không có thời gian chuyển đáp án bổ sung, vì vậy bạn cần quản lý thời gian hiệu quả ngay từ đầu.

Phân bổ thời gian khuyến nghị:

• Passage 1: 15-17 phút (độ khó Easy, giúp bạn khởi động tốt)
• Passage 2: 18-20 phút (độ khó Medium, yêu cầu tập trung cao hơn)
• Passage 3: 23-25 phút (độ khó Hard, cần thời gian suy luận và phân tích)

Lưu ý rằng độ dài các passage tăng dần và mức độ phức tạp của từ vựng cũng như cấu trúc câu cũng nâng cao theo từng passage. Điều này đòi hỏi bạn phải điều chỉnh tốc độ đọc và chiến lược làm bài phù hợp với từng phần.

Các Dạng Câu Hỏi Trong Đề Này

Đề thi mẫu này bao gồm đầy đủ các dạng câu hỏi phổ biến nhất trong IELTS Reading:

1. Multiple Choice – Câu hỏi trắc nghiệm với nhiều lựa chọn
2. True/False/Not Given – Xác định thông tin đúng, sai hoặc không được đề cập
3. Matching Information – Ghép thông tin với đoạn văn tương ứng
4. Sentence Completion – Hoàn thành câu với từ trong bài đọc
5. Matching Headings – Ghép tiêu đề phù hợp với các đoạn văn
6. Summary Completion – Hoàn thành đoạn tóm tắt
7. Matching Features – Ghép đặc điểm với các nhân vật hoặc yếu tố trong bài
8. Short-answer Questions – Trả lời câu hỏi ngắn với từ trong bài

Mỗi dạng câu hỏi yêu cầu một kỹ năng đọc hiểu cụ thể, từ scanning (đọc lướt tìm thông tin chi tiết) đến skimming (đọc nhanh nắm ý chính) và critical reading (đọc phân tích).

2. IELTS Reading Practice Test

PASSAGE 1 – Early Warning Systems: Technology’s First Line of Defense

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

Natural disasters have claimed millions of lives throughout human history, but modern technology is dramatically changing our ability to prepare for and respond to these catastrophic events. Early warning systems represent one of the most significant technological advances in disaster management, providing communities with crucial time to evacuate, secure property, and mobilize emergency services before disaster strikes.

The concept of early warning is not new. For centuries, people have observed natural signs to predict impending dangers – unusual animal behavior before earthquakes, changes in ocean patterns before tsunamis, or distinctive cloud formations before severe storms. However, these traditional methods were often unreliable and provided little advance notice. Today’s technologically advanced systems combine multiple data sources to create accurate and timely alerts that can save countless lives.

Seismic monitoring networks form the backbone of earthquake early warning systems. These networks consist of hundreds or even thousands of sensors placed strategically across seismically active regions. When an earthquake begins, the sensors detect the initial P-waves (primary waves) which travel faster than the more destructive S-waves (secondary waves). The system can calculate the earthquake’s location and magnitude within seconds and send alerts to areas that will soon be affected. Although this warning period may only be seconds to minutes, it provides enough time for people to take protective actions such as moving away from windows, stopping trains, or shutting down critical infrastructure like gas lines and nuclear reactors.

Tsunami warning systems operate on a similar principle but offer longer warning times. After detecting an undersea earthquake, oceanographic sensors and deep-ocean tsunami detection buoys monitor sea level changes. These systems can provide warnings ranging from minutes to several hours before a tsunami reaches coastal areas. The Pacific Tsunami Warning Center in Hawaii, established in 1949, monitors seismic activity across the Pacific Ocean and issues alerts to 46 member nations. This international cooperation has proven invaluable in protecting coastal populations throughout the region.

Meteorological technology has revolutionized how we monitor and predict weather-related disasters. Weather satellites equipped with advanced imaging technology orbit the Earth, providing real-time data on atmospheric conditions. Doppler radar systems can detect the rotation within thunderstorms that may produce tornadoes, often providing 10-15 minutes of warning time. For hurricanes and cyclones, satellite tracking and computer modeling can predict the storm’s path and intensity days in advance, allowing for large-scale evacuations and preparations.

The success of early warning systems depends not only on technology but also on effective communication infrastructure and community preparedness. Even the most sophisticated detection system is useless if warnings cannot reach people quickly or if communities do not know how to respond. Modern systems use multiple communication channels including television, radio, mobile phone alerts, sirens, and social media to ensure messages reach as many people as possible. Many countries have developed standardized alert systems like the Emergency Alert System in the United States or the J-Alert system in Japan, which can automatically interrupt regular programming to broadcast emergency information.

Mobile technology has emerged as a particularly powerful tool for disaster warnings. Smartphone applications can send location-specific alerts directly to individuals in threatened areas. These apps can provide detailed information about the type of danger, recommended actions, and evacuation routes. In countries with high mobile phone penetration, this technology has become a primary channel for reaching people quickly, especially younger generations who may not regularly watch television or listen to radio.

However, challenges remain in making early warning systems truly effective. In many developing countries, the infrastructure required for comprehensive monitoring networks is prohibitively expensive. Even where technology exists, maintenance costs and the need for technical expertise can strain limited resources. Additionally, the “warning fatigue” phenomenon occurs when people receive frequent warnings that do not result in disasters, leading them to ignore future alerts. Balancing sensitivity to ensure no real threat is missed while minimizing false alarms remains an ongoing challenge.

Despite these challenges, the impact of early warning systems on disaster mortality has been profound. Studies show that countries with well-developed warning systems and disaster preparedness programs experience significantly fewer deaths from natural disasters compared to those without such systems. As climate change increases the frequency and intensity of extreme weather events, the role of early warning technology will become even more critical in protecting vulnerable populations worldwide.

Questions 1-13

Questions 1-5

Do the following statements agree with the information given in the reading passage?

Write:

• TRUE if the statement agrees with the information
• FALSE if the statement contradicts the information
• NOT GIVEN if there is no information on this

1. Traditional methods of predicting disasters were completely accurate but slow.
2. Earthquake early warning systems can provide several minutes of advance notice before major shaking occurs.
3. The Pacific Tsunami Warning Center was established after a devastating tsunami in Hawaii.
4. Weather satellites provide information about atmospheric conditions in real time.
5. All developing countries lack the technology for early warning systems.

Questions 6-9

Complete the sentences below.

Choose NO MORE THAN TWO WORDS from the passage for each answer.

6. Seismic sensors detect __ which move faster than the waves that cause most earthquake damage.
7. Tsunami detection systems use buoys in the deep ocean along with __ to monitor changes in sea levels.
8. Doppler radar can identify rotation in storms that might create __.
9. The phenomenon of __ occurs when people stop responding to warnings because previous alerts did not lead to actual disasters.

Questions 10-13

Choose the correct letter, A, B, C or D.

10. According to the passage, what is the main advantage of P-waves in earthquake detection?

• A) They cause more damage than S-waves
• B) They travel faster than destructive waves
• C) They are easier to measure accurately
• D) They occur less frequently than other waves

11. Which factor is mentioned as essential for early warning systems to be effective?

• A) International funding agreements
• B) Advanced computer algorithms
• C) Community preparedness and communication
• D) Government legislation

12. What does the passage say about mobile phone alerts?

• A) They have replaced all other warning methods
• B) They are most effective for older populations
• C) They can provide location-specific information
• D) They are too expensive for most countries

13. The passage suggests that early warning systems have:

• A) Eliminated deaths from natural disasters
• B) Significantly reduced disaster mortality rates
• C) Had little impact on disaster outcomes
• D) Only worked in developed countries

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PASSAGE 2 – Remote Sensing and Real-Time Monitoring in Disaster Response

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

When disaster strikes, the first 72 hours are critical for saving lives and minimizing damage. During this crucial window, emergency responders require accurate, up-to-date information about the extent of destruction, the location of survivors, and the accessibility of affected areas. Remote sensing technologies have revolutionized disaster response by providing this vital information quickly and comprehensively, even in situations where ground access is impossible or too dangerous. How artificial intelligence is improving disaster relief efforts represents a parallel advancement in emergency management capabilities.

Satellite imagery has become an indispensable tool in modern disaster management. Both optical satellites and synthetic aperture radar (SAR) satellites orbit the Earth continuously, capable of capturing detailed images of any location on the planet within hours of a request. Optical satellites, similar to digital cameras in space, provide high-resolution photographs that allow analysts to assess structural damage, identify collapsed buildings, and evaluate the extent of flooding or fire damage. These images can be compared with pre-disaster baseline data to quantify destruction with remarkable precision. The resolution of modern commercial satellites has improved dramatically, with some systems capable of distinguishing objects as small as 30 centimeters across.

SAR technology offers unique advantages that complement optical imaging. Unlike optical systems that require sunlight and clear weather, SAR satellites use radio waves that can penetrate clouds, smoke, and darkness, making them particularly valuable during adverse conditions that often accompany disasters. SAR systems are especially effective for detecting surface deformation caused by earthquakes or volcanic activity, measuring the extent of flooding, and identifying subtle changes in landscape that might indicate landslide risk or structural instability. Following the 2011 Tohoku earthquake and tsunami in Japan, SAR imagery was crucial in assessing damage to coastal areas while thick clouds prevented optical observation.

The democratization of satellite technology has accelerated disaster response capabilities globally. Commercial satellite operators increasingly provide rapid tasking services, where clients can request specific imagery within hours of an event. International initiatives like the International Charter on Space and Major Disasters coordinate satellite resources from multiple countries, making imagery freely available to emergency responders during major disasters. This collaborative approach ensures that even countries without their own satellite programs can access critical geospatial information when needed.

Công nghệ viễn thám vệ tinh trong quản lý thảm họa và ứng phó khẩn cấp

Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as a game-changing technology for disaster assessment and response. Drones can be deployed rapidly to survey affected areas, providing detailed imagery at much higher resolution than satellites. While satellites might distinguish a collapsed building from an intact one, drones can identify specific hazards like unstable walls, blocked exits, or individuals requiring rescue. Their ability to fly at low altitudes and maneuver around obstacles makes them ideal for urban search and rescue operations.

The versatility of drone technology extends beyond visual inspection. Specialized drones equipped with thermal imaging cameras can detect body heat signatures of survivors trapped beneath rubble, even in complete darkness or through smoke. This capability proved invaluable during the 2015 Nepal earthquake, where thermal drones helped locate survivors in collapsed buildings. Other drones carry gas sensors to detect hazardous materials or structural integrity scanners that use LIDAR (Light Detection and Ranging) technology to create precise 3D maps of damaged structures, helping engineers assess safety and plan demolition or stabilization efforts.

Real-time monitoring systems integrate data from multiple sources to provide emergency managers with a comprehensive operational picture. Geographic Information Systems (GIS) platforms serve as the foundation for these systems, allowing responders to visualize and analyze spatial data from satellites, drones, ground sensors, and social media reports simultaneously. Modern GIS platforms can automatically detect changes between successive images, flagging areas of concern for human analysts. They can also overlay disaster data with information about infrastructure, population density, medical facilities, and transportation networks to support strategic decision-making.

The integration of crowdsourced data has added a valuable dimension to disaster monitoring. During major disasters, affected individuals increasingly use social media to report their situation and needs. Machine learning algorithms can analyze millions of social media posts to identify patterns and extract actionable information. For instance, natural language processing can categorize requests for help by type and urgency, while geotagged posts help pinpoint areas requiring assistance. The Humanitarian OpenStreetMap Team mobilizes volunteers worldwide to rapidly update digital maps of disaster-affected areas, often adding crucial details about local geography that may not appear on official maps.

However, the application of these technologies faces several challenges. The sheer volume of data generated can overwhelm analysts, leading to information overload. Effective disaster response requires not just collecting data but processing and disseminating it quickly in formats that field responders can use. Bandwidth limitations in disaster-affected areas often make it difficult to transmit large files, necessitating data compression techniques and prioritization protocols. Privacy concerns also arise when surveillance technologies are deployed, requiring clear policies about appropriate use and data protection.

Additionally, the technological divide between developed and developing nations remains significant. While wealthy countries deploy fleets of drones and access multiple satellite systems, resource-constrained nations may struggle to afford even basic monitoring equipment. International cooperation and technology transfer programs are essential to ensure that the benefits of remote sensing and monitoring technologies reach all vulnerable populations, regardless of their country’s economic status. The true potential of these technologies will only be realized when they become accessible to those who need them most.

Questions 14-26

Questions 14-18

The reading passage has nine paragraphs.

Which paragraph contains the following information?

Write the correct letter, A-I.

NB: You may use any letter more than once.

14. A description of how social media contributes to disaster response
15. Information about satellites that can function in poor weather conditions
16. The importance of the initial period following a disaster
17. Challenges related to processing large amounts of information
18. How international cooperation provides satellite access to all nations

Questions 19-23

Complete the summary below.

Choose NO MORE THAN TWO WORDS from the passage for each answer.

Remote sensing technologies have transformed disaster response by providing critical information during emergencies. Satellite imagery comes in two main types: optical satellites, which function like cameras in space, and SAR satellites, which use (19)____ that can work through clouds and darkness. SAR is particularly useful for detecting (20)____ caused by earthquakes and measuring flood extent. The (21)____ of satellite technology has made these tools more widely available, with services like the International Charter providing free imagery during disasters.

Drones offer advantages over satellites by providing higher resolution images and greater (22)__. Some drones use (23)____** to detect survivors through rubble by identifying body heat, while others employ LIDAR technology to create three-dimensional structural maps.

Questions 24-26

Choose the correct letter, A, B, C or D.

24. According to the passage, what is a key advantage of drone technology over satellite imagery?

• A) Drones are less expensive to operate
• B) Drones can provide more detailed, close-range observations
• C) Drones can cover larger geographic areas
• D) Drones require less technical expertise to use

25. The passage suggests that Geographic Information Systems (GIS):

• A) Have replaced traditional emergency response methods
• B) Only work with satellite data
• C) Combine multiple data sources for comprehensive analysis
• D) Are too complex for field responders to use

26. What challenge regarding technology access does the passage identify?

• A) Wealthy countries refuse to share technology
• B) Remote sensing equipment is becoming obsolete
• C) Significant disparities exist between rich and poor nations
• D) Most countries lack trained personnel to use monitoring systems

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PASSAGE 3 – Artificial Intelligence and Predictive Analytics: The Future of Proactive Disaster Management

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The paradigm of disaster management is undergoing a fundamental transformation, shifting from primarily reactive responses to increasingly proactive and predictive approaches. At the forefront of this evolution stands artificial intelligence (AI) and advanced analytics, technologies that are not merely enhancing existing disaster management practices but fundamentally reconceptualizing our relationship with natural hazards. These sophisticated systems analyze vast datasets, identify subtle patterns imperceptible to human observers, and generate predictive models with unprecedented accuracy, offering the tantalizing possibility of anticipating disasters before they occur and mitigating their impacts through preemptive action.

The effects of climate change on global food security demonstrates another domain where predictive analytics are becoming crucial for anticipating and addressing global challenges. The application of machine learning algorithms to disaster prediction represents a quantum leap beyond traditional statistical models. Conventional forecasting methods typically rely on linear relationships and historical precedents, operating under the assumption that future patterns will resemble past ones. However, the non-linear dynamics of natural systems and the confounding effects of climate change are rendering these assumptions increasingly tenuous. Machine learning, by contrast, can identify complex, multidimensional relationships within data without requiring predetermined assumptions about how variables interact. Deep neural networks, inspired by the architecture of the human brain, can process millions of data points simultaneously, detecting nuanced correlations that traditional methods would miss entirely.

The application of AI to seismic hazard assessment exemplifies this transformative potential. Researchers at Los Alamos National Laboratory have developed machine learning systems that analyze continuous seismic signals to identify precursory patterns that may indicate an impending earthquake. While predicting the precise timing and location of earthquakes remains an elusive goal, these AI systems have demonstrated remarkable success in identifying periods of elevated seismic risk. The algorithms examine subtle changes in seismic noise—the constant, low-level vibrations in the Earth’s crust—that precede larger events. By training on decades of seismic data from laboratory experiments and natural earthquake sequences, these systems have learned to recognize patterns that human seismologists cannot readily discern. Similar approaches are being applied to volcanic activity monitoring, where AI systems analyze geochemical data, ground deformation measurements, and seismic tremors to assess eruption probability with increasing sophistication.

Hydrological forecasting has been revolutionized by the integration of AI with climate models and real-time sensor networks. Flash floods, which account for the majority of flood-related fatalities worldwide, have historically been difficult to predict due to the rapid onset and localized nature of the causative rainfall. Advanced AI systems now integrate meteorological forecasts, topographical data, soil moisture levels, vegetation indices, and urban development patterns to predict flooding with remarkable spatial and temporal resolution. The Google Flood Forecasting Initiative, operational in India and Bangladesh, employs ensemble learning techniques—combining multiple AI models to improve prediction accuracy—to provide street-level flood warnings up to 48 hours in advance. This system processes data from thousands of sources, including weather stations, satellite imagery, and historical flood records, updating its predictions in real-time as conditions evolve.

The computational demands of these AI systems are prodigious. Training a sophisticated deep learning model for disaster prediction may require processing petabytes of data and thousands of hours of computing time on specialized high-performance computing clusters. The environmental cost of this computation—primarily the electrical power consumed by data centers—raises important questions about sustainability. However, proponents argue that the energy invested in developing these predictive systems is infinitesimal compared to the economic and human costs of disasters they may help prevent. Furthermore, ongoing advances in computational efficiency and the increasing use of renewable energy to power data centers are progressively reducing the carbon footprint of AI operations.

Trí tuệ nhân tạo phân tích dữ liệu dự báo thiên tai và quản lý rủi ro

Beyond prediction, AI is transforming disaster response optimization. When disasters strike, emergency managers face complex resource allocation decisions under conditions of incomplete information and extreme time pressure. How many ambulances should be dispatched to each affected neighborhood? Which roads are likely to remain passable? Where should temporary shelters be established? Which hospitals have capacity to receive additional patients? AI-powered decision support systems analyze real-time data from multiple sources to generate optimized resource deployment strategies. These systems employ operations research techniques such as linear programming and network flow optimization, mathematically determining the most efficient allocation of limited resources to maximize lives saved and suffering reduced.

Natural language processing (NLP), a branch of AI focused on understanding human language, has become invaluable for extracting information from unstructured textual data during disasters. Emergency call centers may receive thousands of calls during major events, overwhelming human operators’ capacity to process and triage requests. NLP systems can automatically transcribe calls, identify the nature and urgency of each request, extract location information, and prioritize dispatch accordingly. These systems can also monitor social media platforms, analyzing millions of posts to identify emerging threats, locate individuals in need of rescue, and assess public sentiment and information needs. During Hurricane Harvey in 2017, AI-powered social media analysis identified specific addresses where people were trapped by flooding, information that was relayed to rescue teams and resulted in numerous successful rescues.

Impact of urban development on public health shares similar challenges in analyzing complex data patterns to inform critical decision-making processes. The integration of AI into disaster management raises significant ethical and societal considerations. Predictive algorithms, trained on historical data, may perpetuate existing biases if that data reflects systemic inequalities. If past disaster responses prioritized certain neighborhoods over others based on socioeconomic factors, an AI system trained on this data might replicate these discriminatory patterns, potentially exacerbating vulnerabilities in already marginalized communities. Ensuring that AI systems promote equitable outcomes requires careful algorithm design, diverse training data, and ongoing bias auditing. The opacity of some AI systems—particularly deep neural networks, which function as “black boxes” whose internal decision-making processes are difficult to interpret—poses challenges for accountability. When an AI system makes a consequential decision, such as allocating emergency resources, stakeholders need to understand the rationale behind that decision.

Data privacy represents another critical concern. Effective disaster prediction and response increasingly rely on granular personal data—location tracking, health information, social media activity, and consumer behavior patterns. The collection and analysis of such data for disaster management purposes must be balanced against individuals’ rights to privacy and protection from surveillance. Establishing robust governance frameworks that define appropriate data usage, ensure informed consent where feasible, and prevent mission creep into unrelated surveillance activities is essential for maintaining public trust.

The ultimate realization of AI’s potential in disaster management depends not merely on technological sophistication but on institutional capacity and human expertise. AI systems are tools that augment rather than replace human decision-making. Emergency managers must understand both the capabilities and limitations of these technologies, interpreting AI-generated insights within the context of local knowledge and experience. Investment in AI technology must be accompanied by corresponding investment in training, ensuring that disaster management professionals can effectively leverage these powerful tools. As climate change intensifies the frequency and severity of natural disasters, the imperative to develop and deploy advanced predictive and response technologies becomes ever more pressing, making AI not merely an enhancement to disaster management but an increasingly indispensable component of humanity’s adaptive capacity in an era of mounting environmental challenges.

Questions 27-40

Questions 27-30

Choose the correct letter, A, B, C or D.

27. According to the passage, how do machine learning algorithms differ from traditional forecasting methods?

• A) They require more historical data to function
• B) They can identify complex relationships without predetermined assumptions
• C) They are less accurate but faster to implement
• D) They only work with linear relationships

28. The Los Alamos National Laboratory AI system for earthquake prediction:

• A) Can precisely predict when and where earthquakes will occur
• B) Analyzes patterns in low-level seismic vibrations
• C) Replaces human seismologists entirely
• D) Only works in laboratory settings

29. What does the passage suggest about the environmental cost of AI systems?

• A) It is too high to justify their use in disaster management
• B) It exceeds the economic costs of the disasters prevented
• C) It is minimal compared to the costs of disasters they help prevent
• D) It has not been studied or considered by researchers

30. According to the passage, what is a key challenge with “black box” AI systems?

• A) They are too expensive to operate
• B) Their decision-making processes are difficult to interpret
• C) They cannot process real-time data
• D) They require constant human supervision

Questions 31-35

Complete the summary below.

Choose NO MORE THAN THREE WORDS from the passage for each answer.

AI is transforming disaster management from reactive to proactive approaches. Machine learning can identify (31)____ in data that traditional methods miss. For flood prediction, AI systems integrate various data sources including (32)____, topographical information, and urban development patterns. The Google Flood Forecasting Initiative uses (33)____, which combines multiple AI models to improve accuracy and provides warnings at the (34)____ up to 48 hours ahead. During disaster response, (35)____ can automatically process emergency calls, identify urgent requests, and extract location information to help prioritize rescue efforts.

Questions 36-40

Do the following statements agree with the claims of the writer in the reading passage?

Write:

• YES if the statement agrees with the claims of the writer
• NO if the statement contradicts the claims of the writer
• NOT GIVEN if it is impossible to say what the writer thinks about this

36. AI systems trained on biased historical data may reproduce discriminatory patterns in disaster response.
37. Deep neural networks are more transparent in their decision-making than traditional algorithms.
38. Most emergency managers currently have adequate training to use AI systems effectively.
39. Data privacy concerns must be balanced with the need for information in disaster management.
40. AI technology will eventually eliminate the need for human decision-makers in emergency situations.

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3. Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. FALSE
2. TRUE
3. NOT GIVEN
4. TRUE
5. FALSE
6. P-waves
7. oceanographic sensors
8. tornadoes
9. warning fatigue
10. B
11. C
12. C
13. B

PASSAGE 2: Questions 14-26

14. H (Paragraph 8)
15. C (Paragraph 3)
16. A (Paragraph 1)
17. I (Paragraph 9)
18. D (Paragraph 4)
19. radio waves
20. surface deformation
21. democratization
22. maneuverability / ability to maneuver
23. thermal imaging cameras
24. B
25. C
26. C

PASSAGE 3: Questions 27-40

27. B
28. B
29. C
30. B
31. complex, multidimensional relationships
32. meteorological forecasts
33. ensemble learning techniques
34. street level
35. Natural language processing / NLP
36. YES
37. NO
38. NOT GIVEN
39. YES
40. NO

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4. Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: Traditional methods, completely accurate, slow
• Vị trí trong bài: Đoạn 2, dòng 3-4
• Giải thích: Bài đọc nói rằng “these traditional methods were often unreliable” (các phương pháp truyền thống thường không đáng tin cậy), điều này mâu thuẫn trực tiếp với câu khẳng định “completely accurate” trong câu hỏi.

Câu 2: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: Earthquake early warning systems, several minutes, advance notice
• Vị trí trong bài: Đoạn 3, dòng 6-7
• Giải thích: Bài viết nói “this warning period may only be seconds to minutes”, khớp với thông tin “several minutes” trong câu hỏi. Câu hỏi paraphrase “advance notice” từ “warning period”.

Câu 4: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: Weather satellites, atmospheric conditions, real time
• Vị trí trong bài: Đoạn 5, dòng 2-3
• Giải thích: Câu “Weather satellites equipped with advanced imaging technology orbit the Earth, providing real-time data on atmospheric conditions” khớp chính xác với thông tin trong câu hỏi.

Câu 6: P-waves

• Dạng câu hỏi: Sentence Completion
• Từ khóa: Seismic sensors, detect, move faster
• Vị trí trong bài: Đoạn 3, dòng 3-4
• Giải thích: Bài viết nói “sensors detect the initial P-waves (primary waves) which travel faster than the more destructive S-waves”. Đáp án phải là “P-waves” theo yêu cầu không quá 2 từ.

Câu 10: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Main advantage, P-waves, earthquake detection
• Vị trí trong bài: Đoạn 3, dòng 3-5
• Giải thích: Bài viết giải thích P-waves “travel faster than the more destructive S-waves” và điều này cho phép hệ thống gửi cảnh báo trước khi sóng phá hoại đến. Đáp án B “They travel faster than destructive waves” là chính xác.

Câu 13: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Early warning systems, impact
• Vị trí trong bài: Đoạn 9, dòng 2-4
• Giải thích: Câu “Studies show that countries with well-developed warning systems… experience significantly fewer deaths from natural disasters” cho thấy các hệ thống đã giảm đáng kể tỷ lệ tử vong (significantly reduced mortality rates). Đáp án A quá tuyệt đối (“eliminated”), C và D mâu thuẫn với thông tin.

Passage 2 – Giải Thích

Câu 14: H (Paragraph 8)

• Dạng câu hỏi: Matching Information
• Từ khóa: Social media, disaster response
• Vị trí trong bài: Đoạn 8
• Giải thích: Đoạn 8 thảo luận về “crowdsourced data” và cách “social media posts” được sử dụng để xác định các mô hình và trích xuất thông tin hữu ích trong phản ứng thảm họa.

Câu 15: C (Paragraph 3)

• Dạng câu hỏi: Matching Information
• Từ khóa: Satellites, poor weather conditions
• Vị trí trong bài: Đoạn 3, dòng 2-4
• Giải thích: Đoạn này mô tả SAR satellites có thể “penetrate clouds, smoke, and darkness”, cho phép chúng hoạt động trong điều kiện thời tiết xấu khi vệ tinh quang học không thể.

Câu 19: radio waves

• Dạng câu hỏi: Summary Completion
• Từ khóa: SAR satellites use
• Vị trí trong bài: Đoạn 3, dòng 2
• Giải thích: Bài viết nói “SAR satellites use radio waves that can penetrate clouds, smoke, and darkness”.

Câu 24: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Drone technology advantage, satellite imagery
• Vị trí trong bài: Đoạn 5, dòng 2-5
• Giải thích: Đoạn văn giải thích drones “providing detailed imagery at much higher resolution than satellites” và có thể “identify specific hazards” mà vệ tinh không thể. Đáp án B “provide more detailed, close-range observations” chính xác tóm tắt lợi thế này.

Câu 26: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Challenge, technology access
• Vị trí trong bài: Đoạn 10, dòng 2-4
• Giải thích: Đoạn cuối nói về “technological divide between developed and developing nations” và “resource-constrained nations may struggle to afford even basic monitoring equipment”. Đáp án C “Significant disparities exist between rich and poor nations” chính xác phản ánh thách thức này.

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Machine learning, differ, traditional forecasting
• Vị trí trong bài: Đoạn 2, dòng 5-8
• Giải thích: Bài viết giải thích “Machine learning, by contrast, can identify complex, multidimensional relationships within data without requiring predetermined assumptions about how variables interact”. Đáp án B chính xác paraphrase thông tin này.

Câu 28: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Los Alamos, earthquake prediction
• Vị trí trong bài: Đoạn 3, dòng 5-7
• Giải thích: Đoạn văn nói hệ thống “examine subtle changes in seismic noise—the constant, low-level vibrations in the Earth’s crust”. Đáp án B “Analyzes patterns in low-level seismic vibrations” chính xác mô tả chức năng này.

Câu 31: complex, multidimensional relationships

• Dạng câu hỏi: Summary Completion
• Từ khóa: Machine learning, identify, data
• Vị trí trong bài: Đoạn 2, dòng 7-8
• Giải thích: Câu gốc: “Machine learning… can identify complex, multidimensional relationships within data”. Đáp án phải không quá 3 từ.

Câu 36: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: AI systems, biased historical data, discriminatory patterns
• Vị trí trong bài: Đoạn 8, dòng 2-5
• Giải thích: Tác giả rõ ràng nói “Predictive algorithms, trained on historical data, may perpetuate existing biases if that data reflects systemic inequalities” và “an AI system trained on this data might replicate these discriminatory patterns”. Điều này khớp với khẳng định trong câu hỏi.

Câu 37: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: Deep neural networks, transparent, decision-making
• Vị trí trong bài: Đoạn 8, dòng 8-10
• Giải thích: Tác giả nói deep neural networks là “black boxes” với “internal decision-making processes are difficult to interpret”, mâu thuẫn với khẳng định về tính minh bạch trong câu hỏi.

Câu 40: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: AI, eliminate need, human decision-makers
• Vị trí trong bài: Đoạn 10, dòng 2-3
• Giải thích: Tác giả rõ ràng nói “AI systems are tools that augment rather than replace human decision-making”, mâu thuẫn với ý kiến AI sẽ loại bỏ nhu cầu con người trong câu hỏi.

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5. Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
catastrophic	adj	/ˌkætəˈstrɒfɪk/	thảm khóa, tai hại	catastrophic events	catastrophic failure, catastrophic damage
evacuate	v	/ɪˈvækjueɪt/	sơ tán, di tản	time to evacuate	evacuate the building, evacuate residents
impending	adj	/ɪmˈpendɪŋ/	sắp xảy ra, sắp đến	predict impending dangers	impending disaster, impending threat
seismically active	adj phrase	/ˈsaɪzmɪkli ˈæktɪv/	hoạt động địa chấn	seismically active regions	seismically active zone, seismically active area
infrastructure	n	/ˈɪnfrəstrʌktʃə(r)/	cơ sở hạ tầng	critical infrastructure	public infrastructure, transport infrastructure
invaluable	adj	/ɪnˈvæljuəbl/	vô giá, cực kỳ quý	proven invaluable	invaluable experience, invaluable resource
meteorological	adj	/ˌmiːtiərəˈlɒdʒɪkl/	thuộc khí tượng học	meteorological technology	meteorological data, meteorological conditions
evacuation	n	/ɪˌvækjuˈeɪʃn/	sự sơ tán	large-scale evacuations	emergency evacuation, evacuation route
prohibitively	adv	/prəˈhɪbətɪvli/	một cách cấm đoán, quá đắt	prohibitively expensive	prohibitively high, prohibitively costly
mortality	n	/mɔːˈtæləti/	tỷ lệ tử vong	disaster mortality	mortality rate, infant mortality
vulnerability	n	/ˌvʌlnərəˈbɪləti/	tính dễ bị tổn thương	vulnerable populations	vulnerability assessment, social vulnerability

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
indispensable	adj	/ˌɪndɪˈspensəbl/	không thể thiếu	indispensable tool	indispensable part, indispensable component
synthetic aperture radar	n phrase	/sɪnˈθetɪk ˈæpətʃə(r) ˈreɪdɑː(r)/	radar tổng hợp khẩu độ	SAR satellites	SAR technology, SAR imagery
quantify	v	/ˈkwɒntɪfaɪ/	định lượng	quantify destruction	quantify the impact, quantify results
complement	v	/ˈkɒmplɪment/	bổ sung, bổ trợ	complement optical imaging	complement each other, complement the data
penetrate	v	/ˈpenɪtreɪt/	xuyên qua, thấm vào	penetrate clouds	penetrate the surface, penetrate barriers
deformation	n	/ˌdiːfɔːˈmeɪʃn/	sự biến dạng	surface deformation	ground deformation, structural deformation
democratization	n	/dɪˌmɒkrətaɪˈzeɪʃn/	dân chủ hóa	democratization of satellite technology	democratization of access, democratization process
geospatial	adj	/ˌdʒiːəʊˈspeɪʃl/	thuộc không gian địa lý	geospatial information	geospatial data, geospatial analysis
game-changing	adj	/ˈɡeɪm tʃeɪndʒɪŋ/	thay đổi cuộc chơi	game-changing technology	game-changing innovation, game-changing development
maneuver	v	/məˈnuːvə(r)/	cơ động, điều khiển	maneuver around obstacles	maneuver through, maneuver into position
thermal imaging	n phrase	/ˈθɜːml ˈɪmɪdʒɪŋ/	chụp ảnh nhiệt	thermal imaging cameras	thermal imaging technology, thermal imaging device
comprehensive	adj	/ˌkɒmprɪˈhensɪv/	toàn diện	comprehensive operational picture	comprehensive analysis, comprehensive coverage
crowdsourced	adj	/ˈkraʊdsɔːst/	huy động từ cộng đồng	crowdsourced data	crowdsourced information, crowdsourced content
bandwidth	n	/ˈbændwɪdθ/	băng thông	bandwidth limitations	limited bandwidth, bandwidth capacity
technological divide	n phrase	/ˌteknəˈlɒdʒɪkl dɪˈvaɪd/	khoảng cách công nghệ	technological divide between nations	digital divide, technological gap

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
paradigm	n	/ˈpærədaɪm/	mô hình, khuôn mẫu	paradigm of disaster management	paradigm shift, new paradigm
reconceptualizing	v	/ˌriːkənˈseptʃuəlaɪzɪŋ/	tái khái niệm hóa	reconceptualizing our relationship	reconceptualizing the approach, reconceptualizing theory
mitigating	v	/ˈmɪtɪɡeɪtɪŋ/	giảm thiểu	mitigating their impacts	mitigating risks, mitigating effects
preemptive	adj	/priˈemptɪv/	phòng ngừa	preemptive action	preemptive measures, preemptive strike
quantum leap	n phrase	/ˈkwɒntəm liːp/	bước nhảy vọt	quantum leap beyond traditional models	quantum leap forward, represent a quantum leap
non-linear dynamics	n phrase	/nɒn ˈlɪniə(r) daɪˈnæmɪks/	động lực phi tuyến	non-linear dynamics of natural systems	complex dynamics, system dynamics
confounding	adj	/kənˈfaʊndɪŋ/	gây rối, làm nhiễu	confounding effects	confounding factors, confounding variables
tenuous	adj	/ˈtenjuəs/	mong manh, yếu ớt	increasingly tenuous	tenuous connection, tenuous link
nuanced	adj	/ˈnjuːɑːnst/	tinh tế, sắc thái	nuanced correlations	nuanced understanding, nuanced approach
precursory	adj	/prɪˈkɜːsəri/	báo hiệu, điềm báo	precursory patterns	precursory signal, precursory activity
elusive	adj	/ɪˈluːsɪv/	khó nắm bắt	elusive goal	elusive target, remain elusive
prodigious	adj	/prəˈdɪdʒəs/	đồ sộ, khổng lồ	prodigious computational demands	prodigious talent, prodigious amount
petabytes	n	/ˈpetəbaɪts/	petabyte (đơn vị dữ liệu)	petabytes of data	store petabytes, process petabytes
infinitesimal	adj	/ˌɪnfɪnɪˈtesɪml/	vô cùng nhỏ	infinitesimal compared to	infinitesimal amount, infinitesimal change
triage	v	/ˈtriːɑːʒ/	phân loại ưu tiên	triage requests	triage patients, triage system
perpetuate	v	/pəˈpetʃueɪt/	duy trì, làm tồn tại	perpetuate existing biases	perpetuate stereotypes, perpetuate inequality
opacity	n	/əʊˈpæsəti/	sự mờ đục, không rõ	opacity of AI systems	opacity of processes, lack of opacity
granular	adj	/ˈɡrænjələ(r)/	chi tiết, tỉ mỉ	granular personal data	granular level, granular detail
governance framework	n phrase	/ˈɡʌvənəns ˈfreɪmwɜːk/	khung quản trị	robust governance frameworks	establish framework, governance structure
imperative	n	/ɪmˈperətɪv/	nhu cầu cấp thiết	imperative to develop	moral imperative, strategic imperative
indispensable	adj	/ˌɪndɪˈspensəbl/	không thể thiếu	indispensable component	indispensable tool, indispensable resource

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Kết bài

Chủ đề role of technology in disaster management không chỉ phản ánh xu hướng phát triển công nghệ trong thế giới hiện đại mà còn là một topic có tính ứng dụng cao trong IELTS Reading. Qua bộ đề thi mẫu này, bạn đã được trải nghiệm đầy đủ ba mức độ khó từ Easy đến Hard, tương tự như cấu trúc của bài thi IELTS thực tế.

Ba passages đã cung cấp góc nhìn toàn diện về vai trò của công nghệ trong quản lý thảm họa: từ các hệ thống cảnh báo sớm cơ bản (Passage 1), đến công nghệ viễn thám và giám sát thời gian thực (Passage 2), và cuối cùng là ứng dụng trí tuệ nhân tạo trong dự đoán và phân tích (Passage 3). Mỗi passage không chỉ giúp bạn luyện tập kỹ năng đọc hiểu mà còn mở rộng vốn hiểu biết về một lĩnh vực quan trọng của xã hội đương đại.

Phần đáp án chi tiết kèm giải thích đã chỉ ra cách xác định thông tin trong bài, kỹ thuật paraphrase và các “bẫy” thường gặp trong từng dạng câu hỏi. Đây là kiến thức thực chiến giúp bạn tự đánh giá năng lực và cải thiện phương pháp làm bài. Đặc biệt, bảng từ vựng được phân loại theo độ khó giúp bạn tích lũy vốn từ học thuật cần thiết cho kỳ thi.

Hãy sử dụng bộ đề này như một công cụ luyện tập thực chiến. Làm bài trong điều kiện như thi thật, sau đó đối chiếu đáp án và phân tích kỹ những câu sai để hiểu rõ lý do. Việc lặp lại quá trình này với nhiều đề thi khác nhau sẽ giúp bạn xây dựng được phản xạ làm bài tốt và tự tin hơn khi bước vào phòng thi IELTS Reading thực sự.

Chúc bạn ôn tập hiệu quả và đạt được band điểm mong muốn trong kỳ thi IELTS sắp tới!