Mở Bài
Trí tuệ nhân tạo (AI) đang cách mạng hóa cách con người ứng phó với các thảm họa tự nhiên và nhân tạo, trở thành một chủ đề nóng trong kỳ thi IELTS Reading những năm gần đây. Chủ đề “The Role Of AI In Disaster Management” thường xuất hiện trong các đề thi IELTS Academic, đặc biệt ở Passage 2 hoặc Passage 3, yêu cầu thí sinh hiểu biết về công nghệ, khoa học và ứng dụng thực tiễn.
Bài viết này cung cấp cho bạn một đề thi IELTS Reading hoàn chỉnh gồm 3 passages với độ khó tăng dần từ Easy đến Hard, bao gồm 40 câu hỏi đa dạng giống thi thật 100%. Bạn sẽ được luyện tập với các dạng câu hỏi phổ biến như Multiple Choice, True/False/Not Given, Matching Headings, Summary Completion và nhiều dạng khác. Mỗi câu hỏi đều có đáp án chi tiết kèm giải thích, giúp bạn hiểu rõ cách paraphrase và xác định thông tin trong bài.
Đề thi này phù hợp cho học viên từ band 5.0 trở lên, với hệ thống từ vựng được phân loại theo từng passage và các mẹo làm bài thực chiến từ kinh nghiệm giảng dạy hơn 20 năm. Hãy chuẩn bị đồng hồ, giấy nháp và bắt đầu làm bài trong 60 phút như thi thật!
1. Hướng Dẫn Làm Bài IELTS Reading
Tổng Quan Về IELTS Reading Test
IELTS Reading Test kéo dài 60 phút với 3 passages và tổng cộng 40 câu hỏi. Mỗi câu trả lời đúng được 1 điểm, không bị trừ điểm khi sai. Độ khó tăng dần từ Passage 1 đến Passage 3, do đó bạn cần phân bổ thời gian hợp lý:
- Passage 1: 15-17 phút (độ khó Easy, band 5.0-6.5)
- Passage 2: 18-20 phút (độ khó Medium, band 6.0-7.5)
- Passage 3: 23-25 phút (độ khó Hard, band 7.0-9.0)
Lưu ý quan trọng: Bạn phải tự chuyển đáp án vào Answer Sheet trong 60 phút, không có thời gian phụ. Do đó, tập thói quen viết đáp án ngay trong lúc làm bài hoặc dành 5 phút cuối để chuyển đáp án.
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:
- Multiple Choice – Câu hỏi trắc nghiệm (Passage 1 & 3)
- True/False/Not Given – Xác định thông tin đúng/sai/không được đề cập (Passage 1)
- Matching Information – Nối thông tin với đoạn văn (Passage 1)
- Yes/No/Not Given – Xác định quan điểm tác giả (Passage 2)
- Matching Headings – Nối tiêu đề với đoạn văn (Passage 2)
- Summary Completion – Hoàn thành đoạn tóm tắt (Passage 2)
- Matching Features – Nối đặc điểm với nhân vật/tổ chức (Passage 3)
- Short-answer Questions – Câu hỏi trả lời ngắn (Passage 3)
2. IELTS Reading Practice Test
PASSAGE 1 – Early Warning Systems: How AI Predicts Natural Disasters
Độ khó: Easy (Band 5.0-6.5)
Thời gian đề xuất: 15-17 phút
Natural disasters have plagued human civilization throughout history, causing immense loss of life and devastating economic damage. However, recent advances in artificial intelligence (AI) are transforming how we predict and respond to these catastrophic events. By analyzing vast amounts of data faster and more accurately than traditional methods, AI systems are becoming invaluable tools in disaster management.
One of the most significant applications of AI in this field is earthquake prediction. Traditional seismology relies on monitoring seismic activity and geological patterns, but AI can process information from thousands of sensors simultaneously. Machine learning algorithms examine historical earthquake data, ground movement patterns, and even atmospheric changes to identify precursors to earthquakes. In Japan, researchers have developed an AI system that analyzes data from over 1,000 seismometers across the country. The system can detect subtle patterns that human analysts might miss, potentially providing crucial minutes of warning before major tremors strike.
Flood prediction represents another area where AI has proven particularly effective. Meteorological agencies worldwide are now using AI-powered systems to forecast flooding with greater precision. These systems integrate data from multiple sources including weather satellites, river sensors, rainfall measurements, and even social media reports. In India, an AI platform developed by scientists at the Indian Institute of Technology can predict floods up to 48 hours in advance by analyzing rainfall patterns, soil moisture levels, and topographical data. This advance warning has already helped evacuate thousands of people from flood-prone areas, significantly reducing casualties.
Hurricane and typhoon tracking has also been revolutionized by AI technology. Traditional models often struggle to predict the intensity and path of these massive storms accurately. AI systems, however, can process satellite imagery, ocean temperature data, and atmospheric pressure readings in real-time, creating more accurate predictions. The National Oceanic and Atmospheric Administration (NOAA) in the United States has reported that AI-enhanced models have improved hurricane path prediction accuracy by up to 30% compared to previous methods.
Hệ thống cảnh báo sớm sử dụng AI dự báo thiên tai với màn hình hiển thị dữ liệu phân tích động đất và bão
Wildfire detection is another critical area benefiting from AI implementation. In regions prone to forest fires like California and Australia, AI systems monitor live camera feeds, satellite thermal imaging, and weather conditions to detect fires in their earliest stages. When smoke or heat signatures are identified, the system can alert emergency services within minutes, sometimes even before human observers notice the problem. This rapid response capability is essential because controlling wildfires in their initial phase is far easier and requires fewer resources than battling full-scale infernos.
The accessibility of AI technology is also improving. While early systems required expensive infrastructure and specialized expertise, newer platforms are becoming more user-friendly and cost-effective. Some developing nations are now implementing cloud-based AI solutions that don’t require significant local computing resources. These systems can be accessed via smartphones or basic computers, democratizing access to advanced disaster prediction tools.
However, experts emphasize that AI should not replace human judgment entirely. Dr. Maria Santos, a disaster management specialist at the United Nations, explains: “AI is an incredibly powerful tool, but it works best when combined with human expertise and local knowledge. The technology provides data and predictions, but experienced professionals must interpret this information within the broader context of each unique situation.” This human-AI collaboration represents the future of disaster preparedness, combining the computational power of machines with the wisdom and intuition of experienced professionals.
Despite its promise, AI-based early warning systems face several challenges. Data quality remains a critical issue – the systems are only as good as the information they receive. In many disaster-prone regions, monitoring infrastructure is inadequate, providing insufficient data for AI algorithms to work effectively. Additionally, there are concerns about false alarms. If systems generate too many incorrect warnings, public trust may erode, and people might ignore future alerts, even genuine ones.
Looking ahead, researchers are working on next-generation AI systems that will be even more sophisticated. These future platforms will incorporate climate change models, population movement patterns, and infrastructure vulnerability assessments to provide more comprehensive risk analysis. The goal is not just to predict disasters but to understand their potential impact and optimize evacuation and resource allocation strategies before catastrophe strikes.
Questions 1-13
Questions 1-5: Multiple Choice
Choose the correct letter, A, B, C, or D.
1. According to the passage, what is the main advantage of AI in earthquake prediction?
- A) It replaces all traditional seismologists
- B) It can process information from multiple sensors simultaneously
- C) It prevents earthquakes from occurring
- D) It is cheaper than traditional methods
2. The AI system in Japan provides:
- A) days of warning before earthquakes
- B) weeks of warning before earthquakes
- C) minutes of warning before earthquakes
- D) hours of warning before earthquakes
3. What does the AI flood prediction system in India analyze?
- A) Only rainfall patterns
- B) Only social media reports
- C) Multiple data sources including rainfall, soil moisture, and topography
- D) Only river sensor data
4. According to NOAA, AI-enhanced models have improved hurricane path prediction accuracy by:
- A) up to 20%
- B) up to 30%
- C) up to 40%
- D) up to 50%
5. Dr. Maria Santos believes that:
- A) AI should completely replace human disaster managers
- B) AI is not useful in disaster management
- C) AI works best when combined with human expertise
- D) Only humans should make disaster predictions
Questions 6-9: True/False/Not Given
Do the following statements agree with the information in the 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
6. AI systems for wildfire detection can sometimes identify fires before human observers notice them.
7. All developing nations have now implemented AI-based disaster warning systems.
8. False alarms from AI systems can reduce public trust in future warnings.
9. AI disaster prediction systems are more expensive now than they were in the past.
Questions 10-13: Matching Information
Match the following statements (10-13) with the correct disaster type (A-E). You may use any letter more than once.
A) Earthquakes
B) Floods
C) Hurricanes/Typhoons
D) Wildfires
E) All disaster types
10. AI systems monitor live camera feeds and thermal imaging.
11. The technology has helped evacuate thousands of people in advance.
12. AI analyzes atmospheric pressure readings in real-time.
13. Machine learning algorithms examine historical data and ground movement patterns.
PASSAGE 2 – AI-Powered Response Coordination During Disasters
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
A) When disaster strikes, the initial hours are critical. The coordination of emergency services, allocation of resources, and efficient communication between multiple agencies can mean the difference between life and death for thousands of people. Traditionally, this coordination relied heavily on manual processes, radio communications, and the experience of incident commanders. However, the complexity of modern disasters – often involving multiple hazards, widespread damage, and disrupted infrastructure – has overwhelmed conventional response methods. This is where artificial intelligence is making a revolutionary impact, transforming disaster response coordination from a largely reactive process into a more strategic and data-driven operation.
B) One of the most significant contributions of AI to disaster response is optimizing resource allocation. During large-scale emergencies, countless decisions must be made rapidly: Where should ambulances be dispatched? Which areas need evacuation priority? How should rescue teams be distributed? AI systems can process real-time data from emergency calls, social media, satellite imagery, and sensor networks to create a comprehensive picture of the situation. These platforms use optimization algorithms to suggest the most efficient deployment of limited resources. For instance, during the 2019 Australian bushfires, an AI system analyzed smoke density, population distribution, and road conditions to recommend optimal evacuation routes and identify communities at imminent risk.
C) Natural language processing (NLP), a branch of AI, has proven particularly valuable in managing the information overload that characterizes modern disasters. Emergency operations centers receive thousands of messages during crises – from emergency calls, text messages, social media posts, and official reports. Human operators cannot possibly review all this information quickly enough. NLP systems can automatically scan, categorize, and prioritize these communications, identifying urgent requests for help, tracking the spread of damage, and even detecting misinformation. After Hurricane Harvey in 2017, researchers found that an AI system analyzing social media could identify people in life-threatening situations up to three hours faster than traditional call center methods.
D) AI is also transforming how damage assessment is conducted after disasters. Traditionally, this process involved sending inspection teams to physically examine affected areas, a time-consuming task that could take weeks or months. Today, AI-powered image recognition systems can analyze aerial photographs and satellite imagery to assess damage within hours. These systems are trained to identify structural damage, road blockages, flooding extent, and other critical factors. The convolutional neural networks used for this purpose can distinguish between minor damage and complete destruction with accuracy comparable to human experts. This rapid assessment allows authorities to prioritize aid to the most severely affected areas and provide insurance companies with preliminary damage estimates much faster than previously possible.
Hệ thống AI phân tích dữ liệu ứng phó thảm họa thời gian thực với bản đồ số và trực quan hóa phân bổ nguồn lực
E) Perhaps most importantly, AI systems excel at predictive analytics during ongoing disasters. Rather than simply responding to current conditions, these systems can forecast how situations will evolve. For example, if a hurricane has damaged water treatment facilities, AI can predict the likely timing and spread of waterborne diseases based on epidemiological models, population density, and sanitation conditions. This foresight allows public health authorities to position medical supplies and personnel before outbreaks occur. Similarly, AI can predict secondary disasters – such as landslides following earthquakes or gas explosions after structural damage to pipelines – enabling preventive measures.
F) The integration of AI with drone technology has created powerful new capabilities for disaster response. AI-equipped drones can autonomously search large areas for survivors, using thermal imaging and pattern recognition to identify people in need of rescue. In Nepal, following the 2015 earthquake, experimental AI drones successfully located survivors in collapsed buildings by detecting heat signatures and movement patterns that human observers might miss. These drones can also deliver emergency supplies to isolated areas, establish temporary communication networks, and provide real-time video feeds to coordination centers.
G) However, the implementation of AI in disaster response is not without significant challenges. One critical issue is data privacy and security. AI systems require access to vast amounts of personal data – location information, health records, communication content – raising concerns about surveillance and civil liberties. Balancing the legitimate need for information during emergencies with privacy rights remains contentious. Additionally, there are questions about accountability when AI systems make errors. If an algorithm prioritizes one neighborhood over another for rescue resources and people die as a result, who bears responsibility – the developers, the agencies using the system, or the incident commanders who followed AI recommendations?
H) Another challenge involves technological infrastructure. AI systems typically require reliable internet connectivity, power supplies, and data networks – precisely the infrastructure most likely to be damaged during disasters. Engineers are developing resilient systems that can operate with intermittent connectivity and backup power, but this remains an ongoing concern. Moreover, smaller municipalities and developing nations often lack the financial resources and technical expertise to implement sophisticated AI systems, potentially creating a technology gap where wealthy regions benefit from AI-enhanced disaster response while poorer areas continue to rely on outdated methods.
I) Training and cultural adaptation represent yet another hurdle. Emergency responders, many of whom have decades of experience, can be skeptical of AI systems that seem to contradict their intuition. Building trust requires demonstrating that AI is a decision support tool rather than a replacement for human judgment. Several agencies have found success with gradual implementation, starting with AI assistance for routine tasks before expanding to critical decisions. Training programs that help responders understand how AI systems work – and importantly, their limitations – have proven essential for successful adoption.
J) Looking forward, experts envision increasingly sophisticated AI systems that learn from each disaster, continuously improving their performance. Federated learning approaches allow AI systems from different regions and agencies to share insights while keeping sensitive data local. Some researchers are exploring AI systems that can explain their reasoning processes, making their recommendations more transparent and trustworthy. As these technologies mature and the challenges are addressed, AI is poised to become an indispensable component of disaster response coordination, potentially saving thousands of lives and reducing the economic impact of future catastrophes.
Questions 14-26
Questions 14-18: Yes/No/Not Given
Do the following statements agree with the views of the writer in the passage? Write:
- YES if the statement agrees with the views of the writer
- NO if the statement contradicts the views of the writer
- NOT GIVEN if it is impossible to say what the writer thinks about this
14. Traditional disaster response methods are sufficient for handling modern complex disasters.
15. AI systems analyzing social media during Hurricane Harvey identified people in danger faster than traditional methods.
16. AI-powered damage assessment is more accurate than human expert assessment in all cases.
17. Data privacy concerns are important considerations when implementing AI in disaster response.
18. All emergency responders are enthusiastic about adopting AI technologies.
Questions 19-23: Matching Headings
Choose the correct heading for paragraphs B, D, E, F, and I from the list of headings below.
List of Headings:
i. The role of drones in rescue operations
ii. Optimizing the distribution of emergency resources
iii. Privacy concerns and ethical questions
iv. Forecasting future developments during ongoing crises
v. The human factor in technology adoption
vi. Rapid visual evaluation of disaster impact
vii. Historical development of disaster management
viii. International cooperation in disaster response
ix. Communication challenges during emergencies
19. Paragraph B
20. Paragraph D
21. Paragraph E
22. Paragraph F
23. Paragraph I
Questions 24-26: Summary Completion
Complete the summary below. Choose NO MORE THAN TWO WORDS from the passage for each answer.
AI systems face several implementation challenges in disaster response. They typically require reliable 24) ____, which is often damaged during disasters. Additionally, there is a 25) ____ between wealthy regions with advanced AI capabilities and poorer areas without them. Successful adoption also requires proper 26) ____ to help responders understand how the systems work and their limitations.
PASSAGE 3 – Machine Learning Algorithms and Predictive Modeling in Long-term Disaster Preparedness
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The paradigm shift from reactive disaster management to proactive preparedness planning represents one of the most significant developments in contemporary risk mitigation strategies. At the forefront of this transformation lies the sophisticated application of machine learning algorithms and predictive modeling techniques, which are fundamentally altering how governments, international organizations, and communities anticipate and prepare for potential disasters. Unlike the early warning systems that focus on imminent threats, these advanced AI applications operate on vastly different temporal and spatial scales, analyzing decades of historical data, climate projections, socioeconomic trends, and infrastructure vulnerabilities to construct comprehensive risk profiles that inform long-term policy decisions and investment priorities.
The methodological foundations of these systems rest upon several distinct but complementary machine learning approaches. Supervised learning algorithms, trained on labeled historical disaster data, excel at identifying patterns and correlations that presage specific types of catastrophic events. For instance, researchers at the Massachusetts Institute of Technology have developed ensemble models combining random forests, gradient boosting machines, and deep neural networks to predict earthquake susceptibility in regions with limited seismological monitoring. These models integrate geological features, tectonic plate movements, historical seismicity, and even anthropogenic factors such as groundwater extraction and reservoir-induced seismicity to generate probabilistic hazard assessments with unprecedented granularity.
Unsupervised learning techniques, particularly clustering algorithms and dimensionality reduction methods, have proven invaluable for discovering latent patterns in complex, multidimensional disaster datasets. These approaches have revealed previously unrecognized connections between seemingly disparate phenomena. A notable example involves the discovery of teleconnections between ocean temperature anomalies, atmospheric circulation patterns, and drought severity in agricultural regions thousands of kilometers away. By identifying these non-obvious relationships, scientists can now incorporate oceanographic data into drought prediction models, substantially extending forecast lead times from months to seasons or even years. This extended predictive horizon enables agricultural planners to make informed decisions about crop selection, irrigation investments, and food security reserves well before water stress becomes critical.
Mô hình học máy phân tích dữ liệu dự báo thiên tai dài hạn với biểu đồ trực quan hóa và mạng nơ-ron nhân tạo
The application of reinforcement learning to disaster preparedness planning represents a particularly innovative development. Unlike traditional optimization approaches that seek single optimal solutions, reinforcement learning algorithms explore vast decision spaces, learning through iterative experimentation which preparedness strategies yield the best outcomes across diverse disaster scenarios. The World Bank has employed such systems to optimize disaster risk financing strategies for climate-vulnerable nations. The AI explores thousands of possible combinations of insurance mechanisms, contingency funds, early action protocols, and international assistance agreements, simulating their performance across thousands of potential disaster sequences. The resulting strategies are robust – performing reasonably well across many possible futures rather than being optimized for a single projected scenario that may never materialize.
Geospatial machine learning has emerged as a critical subdomain, leveraging the exponential growth in satellite imagery availability and computational power. Convolutional neural networks, originally developed for image recognition, can now analyze multispectral satellite data to identify subtle environmental changes that indicate increasing disaster risk. In coastal regions, these systems monitor shoreline erosion, wetland degradation, and coral reef bleaching – all factors that reduce natural protective barriers against storm surges and tsunamis. In mountainous areas, algorithms detect glacier retreat, permafrost thawing, and vegetation changes that increase landslide and avalanche risk. The temporal resolution of modern satellite constellations, with some areas imaged daily or even multiple times per day, enables the detection of rapid changes that might indicate imminent hazards.
The integration of socioeconomic data into disaster prediction models represents a conceptual advancement of profound importance. Physical hazards become disasters only when they interact with vulnerable populations and fragile systems. Consequently, contemporary models incorporate demographic data, poverty indicators, infrastructure maps, governance metrics, and social network structures to assess not just where disasters might occur, but where their impacts will be most severe. This vulnerability-centric approach has revealed that disaster risk is often more strongly determined by social factors than physical hazards. Two communities facing identical flood probabilities may have vastly different actual risk levels depending on building codes, emergency preparedness, healthcare access, and community cohesion.
Ethical considerations surrounding the use of AI in long-term disaster planning have generated substantial scholarly debate and policy discussion. The algorithmic allocation of preparedness resources based on predicted risk inevitably involves distributive justice questions: Should resources flow primarily to areas with highest predicted losses, or should equity considerations direct support to the most vulnerable populations regardless of predicted impact magnitude? When AI models identify certain communities as “high-risk” and consequently insurance premiums rise or development restrictions are imposed, these predictions can become self-fulfilling prophecies, driving economic decline that increases actual vulnerability. The concept of “algorithmic determinism” – the concern that AI predictions might be treated as inevitable truths rather than probabilistic projections subject to uncertainty – poses risks to community agency and adaptive capacity.
The epistemological limitations of machine learning in disaster prediction warrant careful consideration. These systems identify statistical patterns in historical data but may struggle with unprecedented events or non-stationary processes where fundamental relationships are changing. Climate change introduces precisely this non-stationarity: the statistical properties of weather patterns are shifting, potentially rendering models trained on historical data less reliable for future predictions. Researchers are exploring transfer learning and domain adaptation techniques that might allow models to adjust as underlying conditions change, but this remains an area of active research. Moreover, rare catastrophic events – precisely those of greatest concern – provide limited training data, making it difficult for data-hungry machine learning algorithms to learn their characteristics reliably.
The computational requirements of sophisticated disaster prediction systems raise questions about technological equity and environmental sustainability. Training large-scale deep learning models requires substantial computational resources, consuming significant energy and generating considerable carbon emissions – ironically contributing to the climate change that increases disaster risk. Furthermore, the concentration of computational infrastructure and technical expertise in wealthy nations and large technology companies risks creating a neo-colonial dynamic where disaster predictions for developing nations are generated by systems they neither control nor fully understand. Initiatives promoting open-source models, capacity building, and distributed computing architectures aim to address these concerns, but progress remains uneven.
Looking toward the future, the convergence of AI with other emerging technologies promises even more sophisticated disaster preparedness capabilities. Quantum computing, when sufficiently mature, could enable the simulation of complex physical systems – atmospheric dynamics, seismic processes, hydrological cycles – with unprecedented detail, potentially revealing predictive signals currently obscured by computational limitations. Edge computing and 5G networks may enable real-time analysis of sensor data at scales currently impossible, creating dynamic risk models that update continuously as conditions change. Federated learning approaches could allow privacy-preserving collaboration, enabling global AI systems to learn from disaster data worldwide without requiring sensitive information to leave its original location.
Yet technology alone cannot ensure disaster resilience. The most sophisticated predictive models are valuable only if their insights inform actual decisions and actions. This requires institutional capacity, political will, community engagement, and sustained investment – factors that AI cannot provide. The role of artificial intelligence in long-term disaster preparedness is perhaps best understood not as a technological panacea but as a powerful tool that, when thoughtfully implemented within robust governance frameworks and complemented by human expertise and local knowledge, can significantly enhance our collective capacity to anticipate, prepare for, and ultimately reduce the devastating impacts of future disasters.
Questions 27-40
Questions 27-31: Multiple Choice
Choose the correct letter, A, B, C, or D.
27. According to the passage, what distinguishes long-term AI disaster preparedness systems from early warning systems?
- A) They are more accurate in predictions
- B) They operate on different temporal and spatial scales
- C) They are less expensive to implement
- D) They only focus on natural disasters
28. The MIT ensemble models for earthquake prediction incorporate all of the following EXCEPT:
- A) geological features
- B) historical seismicity
- C) groundwater extraction
- D) solar activity patterns
29. Unsupervised learning techniques have been valuable for:
- A) replacing human disaster managers
- B) discovering hidden patterns in complex datasets
- C) eliminating all prediction errors
- D) reducing the cost of satellites
30. According to the passage, the World Bank’s use of reinforcement learning focuses on:
- A) predicting exact dates of disasters
- B) replacing traditional insurance companies
- C) optimizing disaster risk financing strategies
- D) eliminating the need for international assistance
31. What does the passage suggest about the relationship between physical hazards and actual disasters?
- A) Physical hazards always cause disasters
- B) Disasters occur only when hazards interact with vulnerable populations
- C) Social factors are irrelevant to disaster impacts
- D) All communities face equal disaster risk
Questions 32-36: Matching Features
Match the following AI techniques (32-36) with the correct application (A-G). You may use any letter more than once.
AI Techniques:
32. Supervised learning algorithms
33. Clustering algorithms
34. Reinforcement learning
35. Convolutional neural networks
36. Transfer learning
Applications:
A) Adapting models when underlying conditions change
B) Identifying patterns in labeled historical data
C) Analyzing multispectral satellite imagery
D) Discovering non-obvious relationships between phenomena
E) Exploring vast decision spaces through iterative experimentation
F) Replacing all human decision-making
G) Predicting exact disaster dates
Questions 37-40: Short-answer Questions
Answer the questions below. Choose NO MORE THAN THREE WORDS from the passage for each answer.
37. What type of approach focuses on assessing where disaster impacts will be most severe rather than just where they might occur?
38. What concept describes the concern that AI predictions might be treated as inevitable truths rather than probabilistic projections?
39. What emerging technology could enable simulation of complex physical systems with unprecedented detail?
40. According to the passage, what must complement AI to ensure disaster resilience beyond just having sophisticated models?
3. Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- B
- C
- C
- B
- C
- TRUE
- NOT GIVEN
- TRUE
- FALSE
- D
- B
- C
- A
PASSAGE 2: Questions 14-26
- NO
- YES
- NOT GIVEN
- YES
- NO
- ii
- vi
- iv
- i
- v
- internet connectivity (hoặc power supplies/data networks)
- technology gap
- training programs
PASSAGE 3: Questions 27-40
- B
- D
- B
- C
- B
- B
- D
- E
- C
- A
- vulnerability-centric approach
- algorithmic determinism
- quantum computing
- human expertise (hoặc local knowledge/institutional capacity)
4. Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: main advantage, AI, earthquake prediction
- Vị trí trong bài: Đoạn 2, dòng 3-5
- Giải thích: Câu “AI can process information from thousands of sensors simultaneously” được paraphrase thành đáp án B. Đây là lợi thế chính của AI được nhấn mạnh trong đoạn văn. Đáp án A sai vì AI không thay thế hoàn toàn, đáp án C sai vì AI dự đoán chứ không ngăn chặn động đất, đáp án D không được đề cập.
Câu 2: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: Japan, AI system, warning time
- Vị trí trong bài: Đoạn 2, dòng cuối
- Giải thích: Cụm “crucial minutes of warning” trong bài tương ứng với đáp án C. Đây là thông tin cụ thể về hệ thống AI tại Nhật Bản.
Câu 3: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: India, AI platform, analyze
- Vị trí trong bài: Đoạn 3, dòng 6-8
- Giải thích: Bài viết liệt kê “analyzing rainfall patterns, soil moisture levels, and topographical data” – ba yếu tố được tổng hợp trong đáp án C. Các đáp án khác chỉ đề cập một phần thông tin.
Câu 6: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: wildfire detection, before human observers
- Vị trí trong bài: Đoạn 5, dòng 4-6
- Giải thích: Câu “sometimes even before human observers notice the problem” khẳng định rõ ràng nội dung của câu hỏi. Từ “sometimes” cho thấy điều này xảy ra nhưng không phải lúc nào cũng vậy, nhưng vẫn đủ để xác nhận câu phát biểu là đúng.
Câu 7: NOT GIVEN
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: all developing nations, implemented AI systems
- Vị trí trong bài: Đoạn 6
- Giải thích: Bài chỉ nói “Some developing nations are now implementing cloud-based AI solutions” (một số nước đang phát triển), không nói TẤT CẢ các nước, do đó không có đủ thông tin để xác nhận.
Câu 8: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: false alarms, reduce public trust
- Vị trí trong bài: Đoạn 8, dòng 4-6
- Giải thích: Câu “If systems generate too many incorrect warnings, public trust may erode, and people might ignore future alerts” paraphrase chính xác ý của câu hỏi.
Câu 10: D
- Dạng câu hỏi: Matching Information
- Từ khóa: monitor live camera feeds, thermal imaging
- Vị trí trong bài: Đoạn 5, dòng 2-3
- Giải thích: Thông tin này được đề cập cụ thể với wildfires (cháy rừng): “AI systems monitor live camera feeds, satellite thermal imaging, and weather conditions”.
Câu 11: B
- Dạng câu hỏi: Matching Information
- Từ khóa: evacuate thousands of people
- Vị trí trong bài: Đoạn 3, dòng cuối
- Giải thích: Câu “This advance warning has already helped evacuate thousands of people from flood-prone areas” liên kết trực tiếp với floods (lũ lụt).
Passage 2 – Giải Thích
Câu 14: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: traditional methods, sufficient, modern complex disasters
- Vị trí trong bài: Đoạn A, dòng 5-7
- Giải thích: Tác giả viết “the complexity of modern disasters… has overwhelmed conventional response methods”, nghĩa là phương pháp truyền thống KHÔNG đủ để xử lý thảm họa hiện đại phức tạp. Quan điểm tác giả rõ ràng mâu thuẫn với câu phát biểu.
Câu 15: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: Hurricane Harvey, social media, three hours faster
- Vị trí trong bài: Đoạn C, dòng cuối
- Giải thích: Câu “an AI system analyzing social media could identify people in life-threatening situations up to three hours faster than traditional call center methods” khẳng định chính xác nội dung câu hỏi. Tác giả đồng ý với quan điểm này.
Câu 16: NOT GIVEN
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: AI damage assessment, more accurate, all cases
- Vị trí trong bài: Đoạn D, dòng 6-8
- Giải thích: Bài chỉ nói AI có độ chính xác “comparable to human experts” (tương đương với chuyên gia), không nói “more accurate” (chính xác hơn) và không nói “in all cases” (trong mọi trường hợp). Không đủ thông tin để xác định quan điểm tác giả.
Câu 19: ii (Paragraph B)
- Dạng câu hỏi: Matching Headings
- Tiêu đề: Optimizing the distribution of emergency resources
- Vị trí trong bài: Đoạn B
- Giải thích: Câu chủ đề của đoạn B là “One of the most significant contributions of AI to disaster response is optimizing resource allocation”. Cả đoạn văn thảo luận về cách AI giúp phân bổ nguồn lực cứu trợ như xe cứu thương, đội cứu hộ, lộ trình sơ tán một cách tối ưu.
Câu 20: vi (Paragraph D)
- Dạng câu hỏi: Matching Headings
- Tiêu đề: Rapid visual evaluation of disaster impact
- Vị trí trong bài: Đoạn D
- Giải thích: Đoạn D nói về “damage assessment” – đánh giá thiệt hại qua phân tích hình ảnh vệ tinh và ảnh chụp từ trên cao, có thể hoàn thành “within hours” thay vì mất nhiều tuần. “Rapid visual evaluation” (đánh giá trực quan nhanh chóng) paraphrase chính xác nội dung này.
Câu 24: internet connectivity
- Dạng câu hỏi: Summary Completion
- Từ khóa: typically require, damaged during disasters
- Vị trí trong bài: Đoạn H, dòng 2-3
- Giải thích: Câu “AI systems typically require reliable internet connectivity, power supplies, and data networks – precisely the infrastructure most likely to be damaged during disasters” cung cấp ba đáp án có thể: internet connectivity, power supplies, hoặc data networks.
Câu 25: technology gap
- Dạng câu hỏi: Summary Completion
- Từ khóa: wealthy regions, poorer areas
- Vị trí trong bài: Đoạn H, dòng cuối
- Giải thích: Cụm “technology gap” xuất hiện nguyên văn trong câu “potentially creating a technology gap where wealthy regions benefit from AI-enhanced disaster response while poorer areas continue to rely on outdated methods”.
Passage 3 – Giải Thích
Câu 27: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: distinguishes, long-term systems, early warning systems
- Vị trí trong bài: Đoạn 1, dòng 3-6
- Giải thích: Câu “Unlike the early warning systems that focus on imminent threats, these advanced AI applications operate on vastly different temporal and spatial scales” chỉ ra sự khác biệt chính: quy mô về thời gian và không gian. Đáp án B paraphrase chính xác ý này.
Câu 28: D
- Dạng câu hỏi: Multiple Choice (EXCEPT)
- Từ khóa: MIT, ensemble models, incorporate
- Vị trí trong bài: Đoạn 2, dòng 5-9
- Giải thích: Bài liệt kê “geological features, tectonic plate movements, historical seismicity, and even anthropogenic factors such as groundwater extraction”. Solar activity patterns (hoạt động mặt trời) không được đề cập, do đó là đáp án đúng cho câu hỏi EXCEPT.
Câu 31: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: relationship, physical hazards, actual disasters
- Vị trí trong bài: Đoạn 6, dòng 2-3
- Giải thích: Câu “Physical hazards become disasters only when they interact with vulnerable populations and fragile systems” paraphrase thành đáp án B. Từ “only when” (chỉ khi) cho thấy mối quan hệ điều kiện cần thiết.
Câu 37: vulnerability-centric approach
- Dạng câu hỏi: Short-answer (không quá 3 từ)
- Từ khóa: approach, where impacts most severe
- Vị trí trong bài: Đoạn 6, dòng 7-9
- Giải thích: Câu “This vulnerability-centric approach has revealed that disaster risk…” xuất hiện sau khi tác giả giải thích về việc đánh giá nơi tác động nghiêm trọng nhất dựa trên yếu tố xã hội.
Câu 38: algorithmic determinism
- Dạng câu hỏi: Short-answer (không quá 3 từ)
- Từ khóa: concept, predictions treated as inevitable truths, probabilistic projections
- Vị trí trong bài: Đoạn 7, dòng 6-8
- Giải thích: Khái niệm “algorithmic determinism” được định nghĩa nguyên văn trong ngoặc kép: “the concern that AI predictions might be treated as inevitable truths rather than probabilistic projections subject to uncertainty”.
Câu 39: quantum computing
- Dạng câu hỏi: Short-answer (không quá 3 từ)
- Từ khóa: emerging technology, simulation, unprecedented detail
- Vị trí trong bài: Đoạn 9, dòng 2-4
- Giải thích: Câu “Quantum computing, when sufficiently mature, could enable the simulation of complex physical systems… with unprecedented detail” cung cấp trực tiếp đáp án.
Câu 40: human expertise / local knowledge / institutional capacity
- Dạng câu hỏi: Short-answer (không quá 3 từ)
- Từ khóa: complement AI, ensure disaster resilience, beyond models
- Vị trí trong bài: Đoạn 10, dòng 2-4
- Giải thích: Câu “This requires institutional capacity, political will, community engagement, and sustained investment” và câu sau “when thoughtfully implemented within robust governance frameworks and complemented by human expertise and local knowledge” cung cấp nhiều đáp án có thể. Bất kỳ cụm từ nào trong số này (không quá 3 từ) đều được chấp nhận.
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 |
|---|---|---|---|---|---|
| plagued | v | /pleɪɡd/ | gây khổ sở, gây thiệt hại liên tục | Natural disasters have plagued human civilization | be plagued by/with |
| devastating | adj | /ˈdevəsteɪtɪŋ/ | tàn phá, hủy hoại nghiêm trọng | causing devastating economic damage | devastating effect/impact |
| artificial intelligence | n | /ˌɑːtɪfɪʃl ɪnˈtelɪdʒəns/ | trí tuệ nhân tạo | advances in artificial intelligence | AI system/technology |
| seismic activity | n | /ˈsaɪzmɪk ækˈtɪvəti/ | hoạt động địa chấn | monitoring seismic activity | detect/measure seismic activity |
| precursors | n | /prɪˈkɜːsərz/ | dấu hiệu báo trước | identify precursors to earthquakes | early precursor/warning |
| meteorological | adj | /ˌmiːtiərəˈlɒdʒɪkl/ | thuộc khí tượng học | Meteorological agencies worldwide | meteorological data/forecast |
| evacuate | v | /ɪˈvækjueɪt/ | sơ tán | helped evacuate thousands of people | evacuate residents/areas |
| prone | adj | /prəʊn/ | dễ bị, có khả năng xảy ra | flood-prone areas | disaster-prone/prone to |
| accessibility | n | /əkˌsesəˈbɪləti/ | khả năng tiếp cận | The accessibility of AI technology | improve/increase accessibility |
| user-friendly | adj | /ˈjuːzə ˈfrendli/ | dễ sử dụng | more user-friendly platforms | user-friendly interface/design |
| infrastructure | n | /ˈɪnfrəstrʌktʃər/ | cơ sở hạ tầng | expensive infrastructure | IT/monitoring infrastructure |
| false alarms | n | /fɔːls əˈlɑːmz/ | cảnh báo giả | concerns about false alarms | trigger/generate false alarms |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| coordination | n | /kəʊˌɔːdɪˈneɪʃn/ | sự phối hợp, điều phối | coordination of emergency services | improve/require coordination |
| allocation | n | /ˌæləˈkeɪʃn/ | sự phân bổ, phân phối | allocation of resources | resource allocation/optimize allocation |
| overwhelmed | v | /ˌəʊvəˈwelmd/ | làm choáng ngợp, quá tải | has overwhelmed conventional methods | be/become overwhelmed |
| optimization | n | /ˌɒptɪmaɪˈzeɪʃn/ | tối ưu hóa | use optimization algorithms | cost/resource optimization |
| imminent | adj | /ˈɪmɪnənt/ | sắp xảy ra, cận kề | at imminent risk | imminent danger/threat |
| natural language processing | n | /ˈnætʃrəl ˈlæŋɡwɪdʒ ˈprəʊsesɪŋ/ | xử lý ngôn ngữ tự nhiên | NLP has proven valuable | NLP system/algorithm |
| misinformation | n | /ˌmɪsɪnfəˈmeɪʃn/ | thông tin sai lệch | detecting misinformation | spread/combat misinformation |
| convolutional neural networks | n | /ˌkɒnvəˈluːʃənl ˈnjʊərəl ˈnetwɜːks/ | mạng nơ-ron tích chập | convolutional neural networks can distinguish | train/develop CNN |
| predictive analytics | n | /prɪˈdɪktɪv ˌænəˈlɪtɪks/ | phân tích dự đoán | excel at predictive analytics | use/apply predictive analytics |
| epidemiological | adj | /ˌepɪˌdiːmiəˈlɒdʒɪkl/ | thuộc dịch tễ học | based on epidemiological models | epidemiological study/data |
| thermal imaging | n | /ˈθɜːml ˈɪmɪdʒɪŋ/ | hình ảnh nhiệt | using thermal imaging | thermal imaging camera |
| accountability | n | /əˌkaʊntəˈbɪləti/ | trách nhiệm giải trình | questions about accountability | ensure/lack accountability |
| resilient | adj | /rɪˈzɪliənt/ | kiên cường, có khả năng phục hồi | developing resilient systems | resilient infrastructure/system |
| intermittent | adj | /ˌɪntəˈmɪtənt/ | không liên tục, gián đoạn | operate with intermittent connectivity | intermittent connection/power |
| federated learning | n | /ˈfedəreɪtɪd ˈlɜːnɪŋ/ | học liên kết (phương pháp học máy) | Federated learning approaches | implement/use federated learning |
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| paradigm shift | n | /ˈpærədaɪm ʃɪft/ | sự chuyển đổi mô hình tư duy | The paradigm shift from reactive to proactive | represent/undergo paradigm shift |
| proactive | adj | /prəʊˈæktɪv/ | chủ động, tích cực | proactive preparedness planning | proactive approach/measures |
| mitigation | n | /ˌmɪtɪˈɡeɪʃn/ | giảm nhẹ, làm dịu bớt | risk mitigation strategies | disaster/climate mitigation |
| ensemble models | n | /ɒnˈsɒmbl ˈmɒdlz/ | mô hình tổ hợp | developed ensemble models | build/train ensemble models |
| susceptibility | n | /səˌseptəˈbɪləti/ | tính dễ bị tổn thương | predict earthquake susceptibility | vulnerability/disease susceptibility |
| granularity | n | /ˌɡrænjuˈlærəti/ | độ chi tiết, độ phân giải | with unprecedented granularity | high/fine granularity |
| unsupervised learning | n | /ˌʌnˈsuːpəvaɪzd ˈlɜːnɪŋ/ | học không giám sát | Unsupervised learning techniques | apply/use unsupervised learning |
| latent patterns | n | /ˈleɪtnt ˈpætənz/ | các mẫu tiềm ẩn | discovering latent patterns | reveal/identify latent patterns |
| disparate | adj | /ˈdɪspərət/ | khác biệt, không liên quan | seemingly disparate phenomena | disparate sources/elements |
| teleconnections | n | /ˌtelɪkəˈnekʃnz/ | các liên kết từ xa (khí tượng) | discovery of teleconnections | climate/atmospheric teleconnections |
| reinforcement learning | n | /ˌriːɪnˈfɔːsmənt ˈlɜːnɪŋ/ | học tăng cường | application of reinforcement learning | use/implement reinforcement learning |
| geospatial | adj | /ˌdʒiːəʊˈspeɪʃl/ | không gian địa lý | Geospatial machine learning | geospatial data/analysis |
| multispectral | adj | /ˌmʌltiˈspektrəl/ | đa phổ | analyze multispectral satellite data | multispectral imaging/sensor |
| vulnerability-centric | adj | /ˌvʌlnərəˈbɪləti ˈsentrɪk/ | lấy tính dễ bị tổn thương làm trung tâm | vulnerability-centric approach | vulnerability-centric analysis |
| distributive justice | n | /dɪˈstrɪbjətɪv ˈdʒʌstɪs/ | công bằng phân phối | involves distributive justice questions | principle of distributive justice |
| self-fulfilling prophecy | n | /self fʊlˈfɪlɪŋ ˈprɒfəsi/ | lời tiên tri tự ứng nghiệm | become self-fulfilling prophecies | create/avoid self-fulfilling prophecy |
| algorithmic determinism | n | /ˌælɡəˈrɪðmɪk dɪˈtɜːmɪnɪzəm/ | chủ nghĩa quyết định thuật toán | concept of algorithmic determinism | risk of algorithmic determinism |
| non-stationary | adj | /nɒn ˈsteɪʃənri/ | không ổn định, thay đổi theo thời gian | non-stationary processes | non-stationary data/patterns |
| transfer learning | n | /ˈtrænsfɜː ˈlɜːnɪŋ/ | học chuyển giao | exploring transfer learning | apply/use transfer learning |
| quantum computing | n | /ˈkwɒntəm kəmˈpjuːtɪŋ/ | điện toán lượng tử | Quantum computing could enable | develop/use quantum computing |
| edge computing | n | /edʒ kəmˈpjuːtɪŋ/ | điện toán biên | Edge computing may enable | implement/deploy edge computing |
| panacea | n | /ˌpænəˈsiːə/ | liều thuốc chữa bách bệnh | not as a technological panacea | universal/magic panacea |
Kết Bài
Chủ đề “The role of AI in disaster management” không chỉ phản ánh xu hướng phát triển công nghệ hiện đại mà còn là một chủ đề có tính ứng dụng cao, thường xuyên xuất hiện trong kỳ thi IELTS Reading. Qua bài tập này, bạn đã được trải nghiệm một đề thi hoàn chỉnh với ba passages độ khó tăng dần, từ Easy đến Medium và Hard, giống như cấu trúc thi thật 100%.
Passage 1 giới thiệu các ứng dụng cơ bản của AI trong hệ thống cảnh báo sớm, Passage 2 đi sâu vào phối hợp ứng phó thảm họa với độ phức tạp trung bình, và Passage 3 thách thức bạn với nội dung học thuật về machine learning và mô hình dự đoán dài hạn. Mỗi passage đều kèm theo đáp án chi tiết với giải thích cụ thể về vị trí thông tin, cách paraphrase và lý do tại sao các đáp án khác không chính xác.
Hệ thống từ vựng được phân loại theo từng passage giúp bạn không chỉ hiểu nghĩa mà còn biết cách sử dụng trong ngữ cảnh, với các collocations thường gặp. Đây là nền tảng quan trọng để nâng cao khả năng đọc hiểu và làm quen với từ vựng học thuật.
Hãy nhớ rằng, thành công trong IELTS Reading không chỉ đến từ việc làm nhiều bài tập mà còn từ việc phân tích kỹ lưỡng các câu trả lời, hiểu được cơ chế của từng dạng câu hỏi và rèn luyện kỹ năng quản lý thời gian. Luyện tập thường xuyên với các đề thi chất lượng như thế này sẽ giúp bạn tự tin đạt band điểm mục tiêu trong kỳ thi IELTS sắp tới!
