GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China

• Published in: Ore geology reviews Vol. 109; pp. 26 - 49
• Main Authors: Sun, Tao, Chen, Fei, Zhong, Lianxiang, Liu, Weiming, Wang, Yun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2019
• Subjects: Artificial neural networks; GIS; Machine learning; Mineral prospectivity mapping; Random forest; Support vector machine; GIS; Mineral prospectivity mapping; Random forest; Support vector machine; Machine learning; Artificial neural networks

[Display omitted] •Parameters used for training models strongly impact the predictions.•RF model outperforms ANN and SVM models in predictive accuracy and efficiency.•Prospective zones cover 14% of the study area and capture 81% of the known deposits.•Predictive model delineates targets for an integ...