Year: 2025
Type: article
Abstract: Assessing landslide susceptibility is essential for reducing disaster risks and facilitating informed decisionmaking.However, securing geospatial data poses challenges due to potential data tampering and unauthorized access.This study presents a pioneering blockchain-based Multi-Criteria Decision-Making (MCDM) framework that combines the Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to enable automated, secure, and real-time geospatial data analysis.Unlike traditional methods that depend on centralized storage and manual calculations, this innovative approach employs blockchain smart contracts for immutable weight storage, automated AHP-TOPSIS computations, and transparent validation of geospatial data.These self-executing contracts guarantee unbiased decision-making without human oversight, thereby boosting trust and reliability.Landslide susceptibility data were derived from multiple sources: Shuttle Radar Topography Mission (SRTM) for elevation data, Digital Elevation Model (DEM) for terrain morphology, Tropical Rainfall Measuring Mission (TRMM) for rainfall intensity, and Sentinel-2 imagery for land cover classification.These datasets enable a comprehensive evaluation of topographic, climatic, and environmental factors influencing landslides.The experimental results demonstrate an F1-score of 90.7% and an accuracy of 92.5%, outperforming conventional methods in both precision and efficiency.This blockchain-integrated Multi-Criteria Decision Making (MCDM) framework presents an innovative approach to decision-making in disaster mitigation, facilitating real-time validation, securing geospatial data, and minimizing computational inefficiencies.
Language: English
Subfield: Management, Monitoring, Policy and Law
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409406870