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

TL;DR

The paper tackles pollution vulnerability assessment of the Ganga river using remote sensing by applying AHP and several diagnostic variants (Nested AHP, ANP, 1-N AHP, Fuzzy AHP, and Fuzzy 1-N AHP). It demonstrates how factor weights and vulnerability scores change under nonlinear, fuzzy, and unknown-factor conditions, and introduces a composite variable that aggregates variant outcomes for robust decision support. Key findings show substantial differences in vulnerability distributions across methods, with urban belts (Tier-2 cities) typically identifying higher risk, and reveal that incorporating unknown factors or interdependencies can alter spatial prioritization for remediation. The work advances environmental decision support by providing a robust, ensemble-based vulnerability metric adaptable to other landscapes and enabling improved planning for pollution mitigation and river restoration, potentially informing digital-twin frameworks. The analysis leverages remote sensing data (e.g., Landsat-8) and GIS tools (e.g., Google Earth Engine) to produce spatially explicit vulnerability maps and diagnostic comparisons across methodologies. $$V(p)=W\cdot X(p)^T$$ with $W$ as the criterion weight vector and $X(p)$ the pixel-wise factor values, illustrating the core quantitative mechanism of the approach.

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.

Figures (22)

• Figure :
• Figure :
• Figure :
• Figure :
• Figure :