Blockchain and MCDM Framework for Secure Geospatial Data in Landslide Risk Mitigation

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.