Title: A hybrid method to predict flood vulnerability using MCDM methods and Pareto analysis in GIS framework

Authors: Priya Mishra; Sanjeev Kr. Prasad

Addresses: School of Computer Science and Engineering, Galgotias University, Greater Noida, 203201, India ' School of Computer Science and Engineering, Galgotias University, Greater Noida, 203201, India

Abstract: The Rapti and Ghaghara rivers, most prone to flooding in northeastern Uttar Pradesh, India, have caused significant damage and loss of life. Due to flood damage, thorough and robust flood mitigation modelling methods are needed. Thus, this study uses multi-criteria decision making (MCDM) models (AHP, fuzzy-AHP, and Monte Carlo-AHP), geographic information systems (GIS), and remote sensing (RS) to create a regional flood susceptibility map. The study uses expert surveys and Pareto analysis to identify seven significant flood factors: drainage density, elevation, slope, land use/cover, average rainfall, topographic wetness index (TWI), and river proximity. Flood susceptibility maps are created using a weighted overlay approach and divided into five susceptibility zones. Fuzzy-AHP (FAHP) had the highest predictive accuracy, with AUC values of 0.92, 0.85 for Monte Carlo AHP (MC-AHP), and 0.75 for AHP. The flood susceptibility maps were verified using Uttar Pradesh official flood data from 2022, and 2023, bolstering their trustworthiness.

Keywords: GIS; geographic information systems; SRTM; Shuttle Radar Topography Mission; Google Earth engine; Pareto analysis; flood hazard mapping; MCDM; multi-criteria decision making; Ghaghara basin; AUC-ROC.

DOI: 10.1504/IJW.2024.146743

International Journal of Water, 2024 Vol.16 No.4, pp.321 - 348

Received: 12 Jun 2024
Accepted: 17 Nov 2024
Published online: 16 Jun 2025 *

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