Enhancing flood-prone area mapping: fine-tuning the K-nearest neighbors (KNN) algorithm for spatial modelling
Bibliographic record
Abstract
This study focuses on determining the optimal distance metric in the K-Nearest Neighbors (KNN) algorithm for spatial modelling of floods. Four distance metrics of the KNN algorithm, namely KNN-Manhattan, KNN-Minkowski, KNN-Euclidean, and KNN-Chebyshev, were utilized for flood susceptibility mapping (FSM) in Estahban, Iran. A spatial database comprising 509 flood occurrence points extracted from satellite images and 12 factors influencing floods was created for analysis. The particle swarm optimization (PSO) algorithm was employed for hyperparameter optimization and feature selection, considering eight influential factors as modelling inputs. The modelling results revealed that the KNN-Manhattan algorithm exhibited superior accuracy (root mean squared error (RMSE) = 0.169, mean absolute error (MAE) = 0.051, coefficient of determination (R2) = 0.884, and area under the curve (AUC) = 0.94) compared with the other algorithms for identifying flood-prone areas. The KNN-Minkowski algorithm followed closely, with an RMSE of 0.175, MAE of 0.056, R2 of 0.876, and AUC of 0.939. The KNN-Euclidean algorithm achieved an RMSE of 0.183, MAE of 0.061, R2 of 0.842, and AUC of 0.929, whereas the KNN-Chebyshev algorithm achieved an RMSE of 0.198, MAE of 0.075, R2 of 0.842, and AUC of 0.924.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".