Data-driven and knowledge-driven MARCOS method to Cu porphyry prospectivity modelling, a case study, Shahr-e-Babak area, southeastern Iran
DOI:
https://doi.org/10.17794/rgn.2026.3.3Keywords:
Porphyry, MARCOS, MOORA, Shahr-e-Babak, Data-drivenAbstract
The present study aims to compare the performance of data-driven and knowledge-driven Multi-Criteria Decision-Making (MCDM) in producing a mineral potential model in the Shahr-e-Babak study area in south-eastern Iran. To achieve this goal, eight evidential layers, including geological, Cu signature, principal component analysis, argillaceous alteration, phyllic alteration, iron oxide alteration (Gossan), airborne geophysics layers, and linear structures, were preprocessed and produced. To produce the optimal model, first, all layers were scaled and shifted to the zero to one interval. To create the mineral potential model in the area, the Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) method was introduced. For the exploration control layer weighting, two methods were used: the area prediction rate (P-A) method and the Analytic Hierarchy Process (AHP) method. Then, the results were compared with the Multi-Objective Optimization by Ratio Analysis (MOORA) method, which is a proven method in mineral potential assessment. To compare these methods, two methods—area prediction rate and the area under the curve (AUC)—were used. The findings show that the data-driven MARCOS approach provides the best performance and displays the best mineral potential model. The normalized density for the data-driven MARCOS, data-driven MOORA, knowledge-driven MARCOS, and knowledge-driven MOORA methods is equal to 3.00, 2.84, 2.7, and 2.57, respectively. The AUC for the data-driven MARCOS, data-driven MOORA, knowledge-driven MARCOS, and knowledge-driven MOORA methods is equal to 0.939, 0.938, 0.933, and 0.932, respectively.
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Copyright (c) 2026 Moslem Jahantigh, Hamidreza Ramazi
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