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Genetic algorithm to optimize the SVM and K-means algorithms for mapping of mineral prospectivity

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Abstract

Unsupervised clustering (e.g., K-means) and supervised machine learning [e.g., support vector machines (SVMs)] methods can be used in data-driven classification and predictive mapping of mineral prospectivity. In this study, the conceptual model of porphyry-type Cu deposits in Varzaghan district, Northwest Iran, was utilized to translate mineralization-related processes into mappable targeting criteria, resulting in six evidence layers derived from geochemical, geological, structural and remote sensing data. Then, traditional K-means clustering (KMC) method and a supervised SVM were employed to construct GIS-based mineral prospectivity maps. One of the challenging issues for generation of mineral prospectivity maps using clustering algorithms is to select suitable cluster centers (called centroids) in order to circumscribe the similar spatial features into meaningful clusters. Machine learning methods are strongly sensitive to hyperparameter values that contribute to prediction, as the prediction accuracy can significantly enhance when the optimized hyperparameters are calibrated to training procedure. To reach these goals, a genetic algorithm was incorporated into K-means and SVM in order to automatically select the optimized cluster centroids and tuned hyperparameters, respectively, and two new prospectivity models namely GKMC and genetic-based SVM were then generated. To evaluate the performance accuracy in training procedures of SVM and genetic-based SVM, K-fold cross-validation and confusion matrix were employed. Moreover, success-rate curves were plotted to compare the overall performance of unsupervised clustering models comprising KMC and GKMC and also supervised machine learning models comprising SVM and genetic-based SVM for detection of favorable areas associated with porphyry-type Cu mineralization. The results suggested the superiority of genetic-based SVM model over other models.

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Code availability

The codes that support the findings of this study are available at figshare.com with the private link [https://figshare.com/s/3b36f43d714a2d6485d3].

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Author information

Authors and Affiliations

  1. Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran

    Reza Ghezelbash & Abbas Maghsoudi

  2. Department of Electrical and Computer Engineering, University of Zanjan, Zanjan, Iran

    Mehdi Shamekhi

  3. Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia

    Biswajeet Pradhan

  4. Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu‐gwan, 209 Neungdong‐ro, Gwangjin‐gu, Seoul, 05006, Korea

    Biswajeet Pradhan

  5. School of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran

    Mehrdad Daviran

Authors
  1. Reza Ghezelbash
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  2. Abbas Maghsoudi
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  3. Mehdi Shamekhi
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  4. Biswajeet Pradhan
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  5. Mehrdad Daviran
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Contributions

RG: Wrote and revised the context of manuscript and contributed to generate MATLAB and GIS-based models as well as interpretation of the results. AM: Contributed to the interpretation of the results and revised the manuscript. MS: Wrote the context of manuscript and contributed to provide the algorithms applied in MATLAB. BP: Revised the manuscript. MD: Contributed to provide the algorithms applied in MATLAB and interpretation of the results.

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Correspondence to Reza Ghezelbash.

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Ghezelbash, R., Maghsoudi, A., Shamekhi, M. et al. Genetic algorithm to optimize the SVM and K-means algorithms for mapping of mineral prospectivity. Neural Comput & Applic 35, 719–733 (2023). https://doi.org/10.1007/s00521-022-07766-5

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