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

Nie, Lin; Chen, Yongliang; Liu, Haiqi; Liu, Hanlin

doi:10.3390/min11020159

Cited by 15 publications

(3 citation statements)

References 78 publications

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“…2) Inclusion of additional machine learning methodologies [Support Vector Machines K-Nearest-Neighbors (KNN), and Perceptron (a neural network model)] can be utilized to improve efficiency with less hyperparameters, while still obtaining high prediction accuracy rates [55];…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Accuracy and Performance of Machine Learning Methodologies: Novel Assessments of Country Pandemic Vulnerability Based on Non-Pandemic Predictors

Vlajnic,

Simske

2023

IEEE Access

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The devastating effects of the COVID-19 pandemic created a need for sensitive and accurate machine learning methodologies for assessment of predictors of pandemic vulnerability. The performance of machine learning methodologies was assessed to correlate, predict, and rank selected demographic, health, and economic public health parameters, relative to COVID-19 case fatality rates in 26 countries. Random Forest Regressor (RFR) and Extreme Gradient Boosting models (XGBoost), both with distribution lags, a novel K-means-Coefficient of Variance (K-means-COV) sensitivity analysis approach and Ordinary Least Squares Multifactor Regression methodologies were used to evaluate correlation of predictive nonpandemic features, grouped into two novel public health indices, Population Health Index (PHI) and Country Health Index (CHI). A novel scoring model was developed for country level pandemic risk assessment. Multiple analyses demonstrated that XGBoost methodology had higher sensitivity and accuracy across all performance metrics relative to RFR, proving that cardiovascular death rate was the most dominant predictive feature for PHI for 46% of countries, and hospital beds per thousand people for CHI (46%). The novel K-means-COV sensitivity analysis approach performed with high accuracy and was successfully validated across all three methods, demonstrating that female smokers was the most common predictive feature across different analysis sets. All assessed machine learning methodologies performed with high accuracy and demonstrated strong predictive value. Only 42.3% of countries in the PHI and 15.4% in the CHI were identified to have a low pandemic vulnerability risk.

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Section: Conclusion and Future Researchmentioning

confidence: 99%

Accuracy and Performance of Machine Learning Methodologies: Novel Assessments of Country Pandemic Vulnerability Based on Non-Pandemic Predictors

Vlajnic,

Simske

2023

IEEE Access

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“…Sparse-PCA technique, kernel ELM, and kernel K-means clustering methods were used for mineral identification based on long-wave infrared data acquired through groundbased spectroscopy [20]. The swarm intelligence-based optimization algorithms and ML algorithms, namely multilayer perceptron, adaptive boosting (AdaBoost), and SVM, were employed for mineral mapping using remote sensing, geochemical, and geological datasets in Qinghai province [21]. A semi-supervised self-learning-based method was evaluated for lithological mapping using Hyperion HSI [22].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Performance of PRISMA Shortwave Infrared Imaging Sensor for Mapping Hydrothermally Altered and Weathered Minerals Using the Machine Learning Paradigm

et al. 2023

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Satellite images provide consistent and frequent information that can be used to estimate mineral resources over a large spatial extent. Advances in spaceborne hyperspectral remote sensing (HRS) and machine learning can help to support various remote-sensing-based applications, including mineral exploration. Leveraging these advances, the present study evaluates recently launched PRISMA spaceborne satellite images to map hydrothermally altered and weathered minerals using various machine-learning-based classification algorithms. The study was performed for the town of Jahazpur in Rajasthan, India (75°06′23.17″E, 25°25′23.37″N). The distribution map for minerals such as kaolinite, talc, and montmorillonite was generated using the spectral angle mapper technique. The resultant mineral distribution map was verified through an intensive field validation survey on surface exposures of the minerals. Furthermore, the obtained pixels of the end-members were used to develop the machine-learning-based classification models. Measures such as accuracy, kappa coefficient, F1 score, precision, recall, and ROC curve were employed to evaluate the performance of developed models. The results show that the stochastic gradient descent and artificial-neural-network-based multilayer perceptron classifiers were more accurate than other algorithms. Results confirm that the PRISMA dataset has enormous potential for mineral mapping in mountainous regions utilizing a machine-learning-based classification framework.

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“…There are 414 positive and negative samples, of which 207 positive samples are recognized from known drilling data (the 207 positive samples are the same as the data used for Apriori mining), and the negative samples are chosen from ore-free voxels identified in the drill holes and known ore-free places on the surface [29,30]. The fourth step ( 4) is divided into two aspects: in the first aspect, the optimal prediction model between the SVM and GNB models is determined based on the receiver operating characteristic (ROC) [31]. Then, Apriori and Chi-square are used to filter out the respective mineralization prediction elements.…”

Section: Introductionmentioning

confidence: 99%

Apriori Algorithm-Based Three-Dimensional Mineral Prospectivity Mapping—An Example from Meiling South Area, Xinjiang, China

Chang,

Zhang,

Zhou

et al. 2023

Minerals

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Mineral Prospectivity Mapping (MPM) is shifting toward intelligent deep mineralization searches in the era of big data and the increasing difficulties of surface deposit detection. Comparative analysis of two forms of mineralization prediction based on the Apriori algorithm was performed in the Meiling South mining area in the eastern Hami region of Xinjiang, China. In comparison 1, we use the Apriori algorithm to mine ore-forming information and determine the ore-forming voxel positions based on spatial distance and angle analysis. Then, we compare the ore-forming voxel positions determined by Apriori with the ore-forming voxel positions predicted by the mathematical model based on the conceptual model of mineralization, and these mathematical models include Gaussian Naive Bayesian (GNB) and Support Vector Machine (SVM). In comparison 2, the optimal prediction model is SVM, which is trained using the elements of mineralization prediction determined by the conceptual model of mineralization. Then, two sets of new elements of mineralization prediction are extracted from the original elements of mineralization prediction using the Apriori and Chi-square methods and then input into the SVM model for training. After we obtain the mineralization prediction results, we compare them with the original mineralization prediction results. The preceding comparison produced the following results. (1) Using the Apriori algorithm, the distribution characteristics of the high and low-grade ore bodies and the association rules between ore-bearing information were determined. (2) The prediction results of the GNB and SVM models displayed corresponding trends on the high and low-grade ore-bearing voxels identified by Apriori, which matched the rules mined by Apriori. (3) In comparison to the mineralization prediction elements screened by Chi-square and the original mineralization prediction elements based on the conceptual model of mineralization, the elements of mineralization prediction chosen based on Apriori have the best prediction effect in SVM when tested in new drill holes. Based on the mineralization prediction elements screened by Apriori, the number of accurate ore-bearing voxels (prediction probability greater than 0.5) predicted by the SVM model is 6, 5, and 1 in drill holes V1, V2, and V3, respectively. The collective results demonstrated that Apriori is explicit, intuitive, and interpretable for mineralization prediction and has a certain reference value for refining the determination of mineralization prediction elements and discovering mineralization mechanisms and laws.

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

Cited by 15 publications

References 78 publications

Accuracy and Performance of Machine Learning Methodologies: Novel Assessments of Country Pandemic Vulnerability Based on Non-Pandemic Predictors

Accuracy and Performance of Machine Learning Methodologies: Novel Assessments of Country Pandemic Vulnerability Based on Non-Pandemic Predictors

Evaluating the Performance of PRISMA Shortwave Infrared Imaging Sensor for Mapping Hydrothermally Altered and Weathered Minerals Using the Machine Learning Paradigm

Apriori Algorithm-Based Three-Dimensional Mineral Prospectivity Mapping—An Example from Meiling South Area, Xinjiang, China

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