Integration of AHP and machine learning methods for flood susceptibility analysis in a Meandering River

Çağan Alevkayalı; Efekan Özkan

doi:10.51489/tuzal.1789569

Integration of AHP and machine learning methods for flood susceptibility analysis in a Meandering River

Abstract

Floods, caused by the overflow of water from natural channels, are among the most destructive natural hazards, affecting human life, property, and ecosystems. Their impact is increasingly significant due to climate change and human-induced land use changes. This study aims to evaluate flood susceptibility in the Sarayköy district of Denizli province using spatial approaches and to compare the predictive performance of different modeling techniques. Four models were applied: Analytic Hierarchy Process (AHP), Maximum Entropy (MaxEnt), Random Forest (RF), and Support Vector Machines (SVM). While AHP relies on expert judgment and hierarchical weighting of criteria, MaxEnt, RF, and SVM are machine learning-based approaches. Model performance was assessed using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Results show that MaxEnt achieved the highest accuracy (AUC = 0.86), followed by RF (0.82), SVM (0.79), and AHP (0.73), highlighting the superior predictive capability of machine learning methods compared to traditional techniques. Machine learning models demonstrated particularly high accuracy in river channels and low-gradient plains, indicating their applicability for disaster risk management. Although AHP produced broader and less sensitive classifications, it remains valuable for rapid preliminary assessments, especially in data-scarce regions. Overall, this study confirms that numerical and spatial analysis of flood risk can be effectively conducted using machine learning approaches, and future research should explore model application across diverse regions, integration of additional hydro-meteorological parameters, and combined modeling strategies to improve risk prediction. Such advances will support more effective, rapid, and spatially-informed decision-making in flood risk management.

Keywords

Supporting Institution

This study received no external funding

Ethical Statement

In the study, the authors declare that there is no violation of research and publication ethics and that the study does not require ethics committee approval.

Thanks

There is no acknowledgment.

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Details

Primary Language

English

Subjects

Physical Geography and Environmental Geology (Other)

Journal Section

Research Article

Authors

Çağan Alevkayalı ^*
0000-0001-7044-8183
Türkiye

Efekan Özkan
0000-0001-8551-0584
Türkiye

Publication Date

March 25, 2026

Submission Date

September 23, 2025

Acceptance Date

December 5, 2025

Published in Issue

Year 2026 Volume: 8

DOI

https://doi.org/10.51489/tuzal.1789569

IZ

https://izlik.org/JA66KF92NU

Cite

RIS / Bibtex

APA

Alevkayalı, Ç., & Özkan, E. (2026). Integration of AHP and machine learning methods for flood susceptibility analysis in a Meandering River. Turkish Journal of Remote Sensing, 8, 1-21. https://doi.org/10.51489/tuzal.1789569