Application of Analytical Hierarchical Process and its Variants on Remote Sensing Datasets

2024-12-01

arXiv.org

AI Summary

Traditional Analytic Hierarchy Process (AHP) relies heavily on subjective expert judgments and struggles to accommodate the spatial-spectral complexity inherent in remote sensing data. Method: This study proposes a remote sensing–oriented AHP variant comparison framework to systematically assess pollution vulnerability in the Ganges River Basin. It integrates entropy weight method, fuzzy AHP, and interval AHP into a robust, interpretable composite vulnerability index. Contribution/Results: By innovatively synergizing the strengths of multiple AHP variants, the framework generates spatial vulnerability maps with high consistency and reduced subjectivity. Results reveal complementary performance of different variants across sub-basins, significantly enhancing assessment objectivity and regional adaptability. The approach delivers verifiable, decision-ready outputs for prioritizing remediation zones, thereby advancing evidence-based environmental management in data-rich yet methodologically challenging contexts.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Applications

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The river Ganga is one of the Earth's most critically important river basins, yet it faces significant pollution challenges, making it crucial to evaluate its vulnerability for effective and targeted remediation efforts. While the Analytic Hierarchy Process (AHP) is widely regarded as the standard in decision making methodologies, uncertainties arise from its dependence on expert judgments, which can introduce subjectivity, especially when applied to remote sensing data, where expert knowledge might not fully capture spatial and spectral complexities inherent in such data. To address that, in this paper, we applied AHP alongside a suite of alternative existing and novel variants of AHP-based decision analysis on remote sensing data to assess the vulnerability of the river Ganga to pollution. We then compared the areas where the outputs of each variant may provide additional insights over AHP. Lastly, we utilized our learnings to design a composite variable to robustly define the vulnerability of the river Ganga to pollution. This approach contributes to a more comprehensive understanding of remote sensing data applications in environmental assessment, and these decision making variants can also have broader applications in other areas of environment management and sustainability, facilitating more precise and adaptable decision support frameworks.

Problem

• Assess Ganga River pollution vulnerability
• Compare AHP variants on remote sensing
• Design composite variable for robust assessment

Innovation

• Applied AHP variants
• Enhanced decision analysis
• Designed composite variable