Scholarlink

<p>In the realm of mineral prospectivity mapping, a novel hybrid approach for optimizing hyperparameters of the&#xa0;support vector machine (SVM) algorithm is proposed here. The concept of ant colony optimization (ACO) algorithm, inspired by collective intelligence of ant colonies, and grid search (GS) that systematically evaluate all hyperparameter combinations to find the optimal model configuration are leveraged to fine-tune SVM parameters, enhancing its predictive capabilities. A dataset comprising geophysical, geochemical, geological, tectonic, and remote sensing evidence layers from the Sardouyeh region in Kerman province, Iran, is utilized for model development aimed the prediction of areas favorable for porphyry-Cu mineralization. After generating the regular and tuned predictive models, a comparison was carried out using quantitative performance metrics such as confusion matrix and success rate curves. The results demonstrated that the optimized versions of SVM using ACO (ACO–SVM) and GS (GS–SVM) models exhibit superior performance, achieving better accuracy and predictive capability in identifying locations favorable for porphyry-Cu mineralization. The study highlights the potential of incorporating optimization algorithms, especially ACO, into SVM, leading to the development of more effective predictive models for mineral prospectivity mapping.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Novel Framework for Optimizing the Prediction of Areas Favorable to Porphyry-Cu Mineralization: Combination of Ant Colony and Grid Search Optimization Algorithms with Support Vector Machines

  • 期刊论文

  • 发表日期：

    2025年1月11日

查看全文
  • Sarina Akbari,
  • Hamidreza Ramazi,
  • Reza Ghezelbash

摘要

In the realm of mineral prospectivity mapping, a novel hybrid approach for optimizing hyperparameters of the support vector machine (SVM) algorithm is proposed here. The concept of ant colony optimization (ACO) algorithm, inspired by collective intelligence of ant colonies, and grid search (GS) that systematically evaluate all hyperparameter combinations to find the optimal model configuration are leveraged to fine-tune SVM parameters, enhancing its predictive capabilities. A dataset comprising geophysical, geochemical, geological, tectonic, and remote sensing evidence layers from the Sardouyeh region in Kerman province, Iran, is utilized for model development aimed the prediction of areas favorable for porphyry-Cu mineralization. After generating the regular and tuned predictive models, a comparison was carried out using quantitative performance metrics such as confusion matrix and success rate curves. The results demonstrated that the optimized versions of SVM using ACO (ACO–SVM) and GS (GS–SVM) models exhibit superior performance, achieving better accuracy and predictive capability in identifying locations favorable for porphyry-Cu mineralization. The study highlights the potential of incorporating optimization algorithms, especially ACO, into SVM, leading to the development of more effective predictive models for mineral prospectivity mapping.