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

• URL: http://arxiv.org/abs/2412.12113v2
• Date: Wed, 05 Feb 2025 14:25:23 GMT
• Title: Application of Analytical Hierarchical Process and its Variants on Remote Sensing Datasets
• Authors: Sarthak Arora, Michael Warner, Ariel Chamberlain, James C. Smoot, Nikhil Raj Deep, Claire Gorman, Anthony Acciavatti,
• Abstract summary: The river Ganga is one of the Earth's most critically important river basins.<n>It faces significant pollution challenges, making it crucial to evaluate its vulnerability for effective and targeted remediation efforts.
• Score: 0.16532031170453743
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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