Enhancing flood-prone area mapping: fine-tuning the K-nearest neighbors (KNN) algorithm for spatial modelling

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.

KEYWORDS:

• Flood-prone areas
• optimal machine learning
• remote sensing
• spatial modelling
• metaheuristic algorithms

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All the derived data based on these original data sources are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was supported in part by the ITRC Support Program [grant number IITP-2023-RS-2022-00156354] and in part by the Metaverse Support Program to Nurture the Best Talents [grant number IITP-2023-RS-2023-00254529] funded by the Ministry of Science and ICT of Korea and the Institute of Information and Communications Technology Planning and Evaluation (IITP) and in part by the Ministry of Trade, Industry and Energy and Korea Institute for Advancement of Technology [grant number P0016038].