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Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines
Abstract Machine learning algorithms (MLAs) such us artificial neural networks (ANNs),
regression trees (RTs), random forest (RF) and support vector machines (SVMs) are …
regression trees (RTs), random forest (RF) and support vector machines (SVMs) are …
[HTML][HTML] GIS-based mineral prospectivity map** using machine learning methods: A case study from Tongling ore district, eastern China
Predictive modelling of mineral prospectivity using GIS is a valid and progressively more
accepted tool for delineating reproducible mineral exploration targets. In this study, machine …
accepted tool for delineating reproducible mineral exploration targets. In this study, machine …
Flood susceptibility map** using frequency ratio and weights-of-evidence models in the Golastan Province, Iran
Flood is one of the most devastating natural disasters with socio-economic and
environmental consequences. Thus, comprehensive flood management is essential to …
environmental consequences. Thus, comprehensive flood management is essential to …
Data analysis methods for prospectivity modelling as applied to mineral exploration targeting: State-of-the-art and outlook
Mineral exploration targeting is a highly complex decision-making task. Two key risk factors,
the quality of exploration data and robustness of the underlying conceptual targeting model …
the quality of exploration data and robustness of the underlying conceptual targeting model …
Translation of the function of hydrothermal mineralization-related focused fluid flux into a mappable exploration criterion for mineral exploration targeting
Formation of hydrothermal mineralization is related to the late stages of, or immediate
following, orogenic compressional regimes that have been superimposed on arc …
following, orogenic compressional regimes that have been superimposed on arc …
[PDF][PDF] GIS-based groundwater spring potential assessment and map** in the Birjand Township, southern Khorasan Province, Iran
Three statistical models—frequency ratio (FR), weights-of-evidence (WofE) and logistic
regression (LR)—produced groundwater-spring potential maps for the Birjand Township …
regression (LR)—produced groundwater-spring potential maps for the Birjand Township …
Support vector machine: A tool for map** mineral prospectivity
In this contribution, we describe an application of support vector machine (SVM), a
supervised learning algorithm, to mineral prospectivity map**. The free R package e1071 …
supervised learning algorithm, to mineral prospectivity map**. The free R package e1071 …
Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: A case study at the Putna river catchment of Romania
Flash-flood is considered to be one of the most destructive natural hazards in the world,
which is difficult to accurately model and predict. The objective of the present research is to …
which is difficult to accurately model and predict. The objective of the present research is to …
Optimized AI-MPM: Application of PSO for tuning the hyperparameters of SVM and RF algorithms
Modern computational techniques, particularly Support Vector Machines (SVM) and
Random Forest (RF) models, are revolutionizing predictive mineral prospectivity map** …
Random Forest (RF) models, are revolutionizing predictive mineral prospectivity map** …
An automated deep learning convolutional neural network algorithm applied for soil salinity distribution map** in Lake Urmia, Iran
Traditional soil salinity studies are time-consuming and expensive, especially over large
areas. This study proposed an innovative deep learning convolutional neural network (DL …
areas. This study proposed an innovative deep learning convolutional neural network (DL …