Urban flood susceptibility mapping using deep and machine learning algorithms as a management tool: A case study of Sanandaj City, Iran
Bibliographic record
Abstract
Urban flooding is a complex natural hazard event that incorporates climate change impacts with urban planning and developing challenges, requiring comprehensive strategies for mitigation and adaptation. Flood susceptibility mapping is one of the first steps in an appropriate strategy to reduce economic disruption and damage to urban environments due to flooding. This paper proposes a family of new deep neural networks, namely “deep abstract networks” (DANet) algorithm, which has not been conducted earlier on the susceptibility assessment worldwide, to be trained for producing reliable urban flood susceptibility maps, using Sanandaj City, Iran, as an example. In this procedure, 174 urban and 174 non-urban flood locations are considered in tandem with 19 flood factors prioritized using the reliefF attribute evaluation (RAE) feature selection technique. We determine the goodness-of-fit and prediction accuracy of our models using sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), mean absolute error (MAE), and area under the curve (AUC). Furthermore, the new proposed deep learning algorithm is compared to the five state-of-the-art benchmark learning algorithms, i.e., Convolutional Neural Network (CNN), Support Vector Machine with Linear (SVM-Linear) and with radial basis function (SVM-RBF), Artificial Neural Network-Multi-Layer Perceptron (ANN-MLP), and Logistic Regression (LR). Here, land use, building density, distances to buildings, rainfall, and distances to passages are the five most influential factors in urban flood occurrence in the study area. The DANet algorithm achieves RMSE = 0.535, AUC model = 0.811, and AUC map = 0.840, and thus outperforms the ANN-MLP, SVM-RBF, SVM-Linear, LR and CNN algorithms as an excellent alternative algorithm for managing areas prone to urban flooding with caution.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".