An Internet of Things and AI-Powered Framework for Long-Term Flood Risk Evaluation
Bibliographic record
Abstract
Integrating Internet of Things (IoT) and artificial intelligence (AI) techniques have found widespread application in various fields, including smart cities, agriculture, and environmental monitoring. With the increasing availability of satellite imagery and other remote sensing data, deep learning algorithms can be used and trained to detect, classify, and segment flood regions in real time. In addition, deep learning techniques, such as convolutional neural networks (CNNs), have been successful in this field, enabling the automated analysis of vast amounts of satellite imagery. By combining AI-based flood detection with other data sources, such as meteorological forecasts and ground-based sensors, comprehensive flood monitoring systems that provide early warning of flood events and facilitate effective emergency response can be developed. In this article, we developed an image-based flood segmentation system called DeepLab that uses a deep learning algorithm to detect and segment the presence and extent of floods with high accuracy and speed. The neural network was trained on an extensive collection of satellite images, which were complemented by ground truth labels that indicated the presence of flooded areas. The trained DeepLabv3 model is applied to new satellite images during inference to forecast the likelihood of each pixel belonging to a flooded area. To do this, a binary flood map was generated from the pixel-level forecasts by incorporating a threshold into the output probabilities. The proposed system’s accuracy was high compared to the state-of-the-art methods, as evidenced by segmentation and experimental results. The segmentation accuracy achieved an overall score of 87%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".