Integrating remote sensing data and fully connected CNN for flood probability and risk assessment in the Port St Johns coastal town, South Africa
Bibliographic record
Abstract
The rising frequency and intensity of floods pose risks to human lives, infrastructure, and ecosystems, particularly in coastal regions, as traditional flood management systems struggle with uncertainties, complex environmental factors, and rapid urbanization, reducing decision-making accuracy. The study employs remote sensing data and a Convolutional Neural Network (CNN) to assess flood probability and risk in Port St Johns, South Africa, utilizing thirteen flood-influencing variables to minimize overfitting and extract robust features, addressing complex terrain and climate variability. The study uses data from ALOS DEM, CHIRPS, and Copernicus to analyze various factors such as Height Above the Nearest Drainage (HAND), TWI, MNDWI, TRI, distance to river, elevation, slope, aspect, curvature, flow accumulation and direction, precipitation, and land cover, using optimized kernel sizes, Rectified Linear Unit (ReLu), and regularization techniques. The results reveal significant correlations between terrain-related and hydrological factors, such as slope (3.98%), HAND (3.07%) and elevation (1.29%), affecting water movement, accumulation, and drainage potential, with land cover (0.42%) and precipitation (0.39%) playing a secondary role. The CNN model for flood probability prediction reveals high accuracy and predictive performance, with a mean absolute error of 0.007 and a precision of 0.988 for flood-affected and unaffected areas. The InaSAFE analysis reveals that 26% of Port St Johns’ population (870 people) and 34% of structures (896 buildings) are directly affected by flooding, with high-risk zones affecting 420 people, 5.3 km of roads, and 479 buildings. The findings of the model enhance community safety and resilience to climate-induced flooding by improving flood risk prediction, optimizing evacuation, resource allocation, and disaster management through early warning systems and damage assessments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".