Sustainable Flood Risk Identification and Assessment Using Artificial Intelligence: A Literature Review and Case Scenario
Bibliographic record
Abstract
The combination of Internet of Things (IoT) and Artificial Intelligence (AI) has been widely applied in various fields, including transportation, agriculture, tourism, smart cities, environmental monitoring, and security. The growing accessibility of satellite and drone imagery, as well as remote sensing data, such as drones, has enabled the efficient utilization and training of deep learning algorithms. These algorithms can accurately identify, categorize, and partition flood areas in real-time. By integrating artificial intelligence (Al)-powered flood detection algorithms with additional data sources, such as meteorological forecasts and ground-based sensors, it is possible to create robust flood monitoring systems. These systems can offer timely alerts for potential flood events and enable efficient emergency response measures. The paper outlines advanced functionalities and technologies that will be available by integrating the Internet of Things (IoT) and Artificial Intelligence (AI) for flood monitoring. The paper provides a comprehensive overview of AI-IoT visions, services, applications, and the networking infrastructure involved. Additionally, it includes a vast assortment of satellite images.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".