A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

• Published in: Minerals (Basel) Vol. 11; no. 2; p. 159
• Main Authors: Lin, Nan, Chen, Yongliang, Liu, Haiqi, Liu, Hanlin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2021
• Subjects: Accuracy; Algorithms; Artificial intelligence; Bias; Comparative analysis; Comparative studies; Fault tolerance; Geology; Heuristic methods; Intelligence; Lagrange multiplier; Learning algorithms; Machine learning; Mapping; Mathematical models; Metallogenesis; Mineral deposits; Mineral resources; Model accuracy; Multilayer perceptrons; Neural networks; Optimization; Optimization algorithms; Performance evaluation; Probability distribution; Remote sensing; Search algorithms; Spatial data; Support vector machines; Swarm intelligence
• ISSN: 2075-163X; 2075-163X
• DOI: 10.3390/min11020159

Selecting internal hyperparameters, which can be set by the automatic search algorithm, is important to improve the generalization performance of machine learning models. In this study, the geological, remote sensing and geochemical data of the Lalingzaohuo area in Qinghai province were researched. A multi-source metallogenic information spatial data set was constructed by calculating the Youden index for selecting potential evidence layers. The model for mapping mineral prospectivity of the study area was established by combining two swarm intelligence optimization algorithms, namely the bat algorithm (BA) and the firefly algorithm (FA), with different machine learning models. The receiver operating characteristic (ROC) and prediction-area (P-A) curves were used for performance evaluation and showed that the two algorithms had an obvious optimization effect. The BA and FA differentiated in improving multilayer perceptron (MLP), AdaBoost and one-class support vector machine (OCSVM) models; thus, there was no optimization algorithm that was consistently superior to the other. However, the accuracy of the machine learning models was significantly enhanced after optimizing the hyperparameters. The area under curve (AUC) values of the ROC curve of the optimized machine learning models were all higher than 0.8, indicating that the hyperparameter optimization calculation was effective. In terms of individual model improvement, the accuracy of the FA-AdaBoost model was improved the most significantly, with the AUC value increasing from 0.8173 to 0.9597 and the prediction/area (P/A) value increasing from 3.156 to 10.765, where the mineral targets predicted by the model occupied 8.63% of the study area and contained 92.86% of the known mineral deposits. The targets predicted by the improved machine learning models are consistent with the metallogenic geological characteristics, indicating that the swarm intelligence optimization algorithm combined with the machine learning model is an efficient method for mineral prospectivity mapping.