Evaluation of remote sensing techniques-based water quality monitoring for sustainable hydrological applications: an integrated FWZIC-VIKOR modelling approach

Mohammed Talal (), A. H. Alamoodi (), O. S. Albahri (), A. S. Albahri () and Dragan Pamucar ()
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Mohammed Talal: Universiti Tun Hussein Onn Malaysia (UTHM)
A. H. Alamoodi: Universiti Pendidikan Sultan Idris (UPSI)
O. S. Albahri: La Trobe University
A. S. Albahri: Iraqi Commission for Computers and Informatics (ICCI)
Dragan Pamucar: University of Belgrade

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2024, vol. 26, issue 8, No 23, 19685-19729

Abstract: Abstract Evaluating remote sensing techniques (RST)-based water quality monitoring systems in hydrological applications is a complex multi-attribute decision-making (MADM) problem due to several integrated issues. These issues include the need to consider 15 evaluation criteria, the importance of these criteria in relation to their application and network infrastructure, data variation, and trade-offs and conflicts between them. Therefore, this study proposes an MADM integrated modelling approach to weight these criteria and rank the selected RSTs using the “Fuzzy Weighted with Zero Inconsistency” (FWZIC) method coupled with the “Vlse-kriterijumska Optimizcija I Kaompromisno Resenje” (VIKOR) method. First, a decision matrix was developed for the evaluation criteria and their intersection with various RSTs in different network categories. Next, the assessment criteria were weighted using FWZIC, followed by ranking the RSTs for each category using VIKOR. The findings reveal that the criteria weighting varied across categories, as assigned by specialists-based FWZIC. For example, the “data acquisition system complexity” criterion received the maximum weight (w = 0.081) for mesh-based sensing and the lowest (w = 0.057) for cellular-based sensing. Using the obtained weights and based on the ascending order of the performance score value (Q), various RSTs were benchmarked using VIKOR. This includes (n = 22) RSTs in fixed star, (n = 8) RSTs in mesh sensing, (n = 6) RSTs in cellular sensing, (n = 3) RSTs in fixed cable sensing, and (n = 2) RSTs in movable sensing. Finally, the evaluation process for the benchmarked RSTs involved systematic ranking, sensitivity analysis, and comparison analysis, which confirmed the robustness of the proposed approach across all RST categories.

Keywords: Data acquisition system; Multi attribute-decision making; Remote sensing; Sustainable hydrological environment; Water quality monitoring (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10668-023-03432-5

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