AI-Driven Global Disaster Intelligence from News Media
Open-source disaster intelligence (OSDI) is crucial for improving situational awareness, disaster preparedness, and real-time decision-making. Traditional OSDI frameworks often rely on social media data, which are susceptible to misinformation and credibility issues. This study proposes a novel AI-d...
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MDPI AG
2025-03-01
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| Series: | Mathematics |
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| author | Fahim Sufi Musleh Alsulami |
| author_facet | Fahim Sufi Musleh Alsulami |
| author_sort | Fahim Sufi |
| collection | DOAJ |
| description | Open-source disaster intelligence (OSDI) is crucial for improving situational awareness, disaster preparedness, and real-time decision-making. Traditional OSDI frameworks often rely on social media data, which are susceptible to misinformation and credibility issues. This study proposes a novel AI-driven framework utilizing automated data collection from 444 large-scale online news portals, including CNN, BBC, CBS News, and The Guardian, to enhance data reliability. Over a 514-day period (27 September 2023 to 26 February 2025), 1.25 million news articles were collected, of which 17,884 were autonomously classified as disaster-related using Generative Pre-Trained Transformer (GPT) models. The analysis identified 185 distinct countries and 6068 unique locations, offering unprecedented geospatial and temporal intelligence. Advanced clustering and predictive analytics techniques, including K-means, DBSCAN, seasonal decomposition (STL), Fourier transform, and ARIMA, were employed to detect geographical hotspots, cyclical patterns, and temporal dependencies. The ARIMA (2, 1, 2) model achieved a mean squared error (MSE) of 823,761, demonstrating high predictive accuracy. Key findings highlight that the USA (6548 disasters), India (1393 disasters), and Australia (1260 disasters) are the most disaster-prone countries, while hurricanes/typhoons/cyclones (5227 occurrences), floods (3360 occurrences), and wildfires (2724 occurrences) are the most frequent disaster types. The framework establishes a comprehensive methodology for integrating geospatial clustering, temporal analysis, and multimodal data processing in OSDI. By leveraging AI automation and diverse news sources, this study provides a scalable, adaptable, and ethically robust solution for proactive disaster management, improving global resilience and preparedness. |
| format | Article |
| id | doaj-art-4bae84779f4b4ba0bc35d833df9fdbe1 |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-4bae84779f4b4ba0bc35d833df9fdbe12025-08-20T03:08:55ZengMDPI AGMathematics2227-73902025-03-01137108310.3390/math13071083AI-Driven Global Disaster Intelligence from News MediaFahim Sufi0Musleh Alsulami1School of Public Health and Preventive Medicine, Monash University, Australia, VIC 3004, AustraliaDepartment of Software Engineering, College of Computing, Umm Al-Qura University, Makkah 21961, Saudi ArabiaOpen-source disaster intelligence (OSDI) is crucial for improving situational awareness, disaster preparedness, and real-time decision-making. Traditional OSDI frameworks often rely on social media data, which are susceptible to misinformation and credibility issues. This study proposes a novel AI-driven framework utilizing automated data collection from 444 large-scale online news portals, including CNN, BBC, CBS News, and The Guardian, to enhance data reliability. Over a 514-day period (27 September 2023 to 26 February 2025), 1.25 million news articles were collected, of which 17,884 were autonomously classified as disaster-related using Generative Pre-Trained Transformer (GPT) models. The analysis identified 185 distinct countries and 6068 unique locations, offering unprecedented geospatial and temporal intelligence. Advanced clustering and predictive analytics techniques, including K-means, DBSCAN, seasonal decomposition (STL), Fourier transform, and ARIMA, were employed to detect geographical hotspots, cyclical patterns, and temporal dependencies. The ARIMA (2, 1, 2) model achieved a mean squared error (MSE) of 823,761, demonstrating high predictive accuracy. Key findings highlight that the USA (6548 disasters), India (1393 disasters), and Australia (1260 disasters) are the most disaster-prone countries, while hurricanes/typhoons/cyclones (5227 occurrences), floods (3360 occurrences), and wildfires (2724 occurrences) are the most frequent disaster types. The framework establishes a comprehensive methodology for integrating geospatial clustering, temporal analysis, and multimodal data processing in OSDI. By leveraging AI automation and diverse news sources, this study provides a scalable, adaptable, and ethically robust solution for proactive disaster management, improving global resilience and preparedness.https://www.mdpi.com/2227-7390/13/7/1083open-source disaster intelligencegeospatial and temporal intelligencenews media data miningpredictive disaster modelingdisaster intelligenceAI |
| spellingShingle | Fahim Sufi Musleh Alsulami AI-Driven Global Disaster Intelligence from News Media Mathematics open-source disaster intelligence geospatial and temporal intelligence news media data mining predictive disaster modeling disaster intelligence AI |
| title | AI-Driven Global Disaster Intelligence from News Media |
| title_full | AI-Driven Global Disaster Intelligence from News Media |
| title_fullStr | AI-Driven Global Disaster Intelligence from News Media |
| title_full_unstemmed | AI-Driven Global Disaster Intelligence from News Media |
| title_short | AI-Driven Global Disaster Intelligence from News Media |
| title_sort | ai driven global disaster intelligence from news media |
| topic | open-source disaster intelligence geospatial and temporal intelligence news media data mining predictive disaster modeling disaster intelligence AI |
| url | https://www.mdpi.com/2227-7390/13/7/1083 |
| work_keys_str_mv | AT fahimsufi aidrivenglobaldisasterintelligencefromnewsmedia AT muslehalsulami aidrivenglobaldisasterintelligencefromnewsmedia |