Enhancing flood prediction through remote sensing, machine learning, and Google Earth Engine
Bibliographic record
Abstract
Floods are the most common natural hazard, causing major economic losses and severely affecting people’s lives. Therefore, accurately identifying vulnerable areas is crucial for saving lives and resources, particularly in regions with restricted access and insufficient data. The aim of this study was to automate the identification of flood-prone areas within a data-scarce, mountainous watershed using remote sensing (RS) and machine learning (ML) models. In this study, we integrate the Normalized Difference Flood Index (NDFI), using Google Earth Engine to generate flood inventory, which is considered a crucial step in flood susceptibility mapping. Seventeen determining factors, namely, elevation, slope, aspect, curvature, the Stream Power Index (SPI), the Topographic Wetness Index (TWI), the Topographic Ruggedness Index (TRI), the Topographic Position Index (TPI), distance from roads, distance from rivers, stream density, rainfall, lithology, the Normalized Difference Vegetation Index (NDVI), land use, length slope (LS) factor, and the Convergence Index were used to map the flood vulnerability. This study aimed to assess the predictive performance of gradient boosting, AdaBoost, and random forest. The model performance was evaluated using the area under the curve (AUC). The performance assessment results showed that random forest (RF) achieved the highest accuracy (1), followed by random forest and gradient boosting ensemble (RF-GB) (0.96), gradient boosting (GB) (0.95), and AdaBoost (AdaB) (0.83). Additionally, in this research study, we employed the Shapely Additive Explanations (SHAP) method, to explain machine learning model predictions and determine the most contributing factor in each model. This study introduces a novel approach to generate flood inventory, providing significant insights into flood susceptibility mapping, and offering potential pathways for future research and practical applications. Overall, the research emphasizes the need to integrate urban planning with emergency preparedness to build safer and more resilient communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".