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Application of Artificial Neural Network for Mineral Potential Mapping

https://doi.org/10.5772/16187
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-540-69496-0_11openalex-snapshot
Mineral Potential Modelling for the Greater Nahanni Ecosystem Using GIS Based Analytical Methods
inferredcites →https://doi.org/10.1007/978-94-009-4029-1_1openalex-snapshot
Application of Recent Developments of Regression Analysis in Regional Mineral Resource Evaluation
inferredcites →https://doi.org/10.1007/bf00894232openalex-snapshot
A preliminary application of weights of evidence in gold exploration in Xiong-er Mountain Region, He-Nan Province
inferredcites →https://doi.org/10.1007/bf01028881openalex-snapshot
Regression models for estimating mineral resources from geological map data
inferredcites →https://doi.org/10.1007/bf02068587openalex-snapshot
Application of a feedforward neural network in the search for Kuroko deposits in the Hokuroku district, Japan
inferredcites →https://doi.org/10.1007/bf02259070openalex-snapshot
Extended weights of evidence modeling for the pseudo-estimation of metal grades
inferredcites →https://doi.org/10.1007/s10346-007-0088-xopenalex-snapshot
Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea
inferredcites →https://doi.org/10.1007/s11053-005-4674-0openalex-snapshot
Measuring the Performance of Mineral-Potential Maps
inferredcites →https://doi.org/10.1007/s11053-005-4678-9openalex-snapshot
Application of Data-Driven Evidential Belief Functions to Prospectivity Mapping for Aquamarine-Bearing Pegmatites, Lundazi District, Zambia
inferredcites →https://doi.org/10.1007/s11053-005-6955-zopenalex-snapshot
Mapping Mineralization Probabilities using Multilayer Perceptrons
inferredcites →https://doi.org/10.1007/s11053-006-9005-6openalex-snapshot
Quantitative Prediction and Evaluation of Mineral Resources Based on GIS: A Case Study in Sanjiang Region, Southwestern China
inferredcites →https://doi.org/10.1007/s11053-006-9010-9openalex-snapshot
Mineral-Potential Mapping: A Comparison of Weights-of-Evidence and Fuzzy Methods
inferredcites →https://doi.org/10.1007/s11053-006-9012-7openalex-snapshot
A Hybrid Fuzzy Weights-of-Evidence Model for Mineral Potential Mapping
inferredcites →https://doi.org/10.1007/s11053-006-9023-4openalex-snapshot
Quantitative Analysis of Scale of Aeromagnetic Data Raises Questions About Geologic-Map Scale
inferredcites →https://doi.org/10.1007/s11053-007-9036-7openalex-snapshot
Application of Radial Basis Functional Link Networks to Exploration for Proterozoic Mineral Deposits in Central Iran
inferredcites →https://doi.org/10.1007/s11053-007-9042-9openalex-snapshot
Porphyry Copper Deposit Tract Definition – A Global Analysis Comparing Geologic Map Scales
inferredcites →https://doi.org/10.1007/s11053-007-9046-5openalex-snapshot
Spatial Analysis Techniques as Successful Mineral-Potential Mapping Tools for Orogenic Gold Deposits in the Northern Fennoscandian Shield, Finland
inferredcites →https://doi.org/10.1007/s11053-008-9062-0openalex-snapshot
Radial Basis Functional Link Nets Used as a Prospectivity Mapping Tool for Orogenic Gold Deposits Within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield
inferredcites →https://doi.org/10.1007/s11053-008-9069-6openalex-snapshot
Mineral Potential Modelling for the Greater Nahanni Ecosystem Using GIS Based Analytical Methods
inferredcites →https://doi.org/10.1007/s11806-009-0089-0openalex-snapshot
A predictive GIS model for potential mapping of copper, lead, and zinc in Langping area, China
inferredcites →https://doi.org/10.1016/0301-4207(90)90023-5openalex-snapshot
Mineral resource assessment
inferredcites →https://doi.org/10.1016/b978-0-08-037245-7.50006-8openalex-snapshot
Statistical Pattern Integration for Mineral Exploration
inferredcites →https://doi.org/10.1016/j.oregeorev.2005.10.002openalex-snapshot
Predictive mapping for copper–gold magmatic-hydrothermal systems in NW Argentina: Use of a regional-scale GIS, application of an expert-guided data-driven approach, and comparison with results from a continental-scale GIS
inferredcites →https://doi.org/10.1016/j.oregeorev.2007.07.001openalex-snapshot
Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping
inferredcites →https://doi.org/10.1016/j.oregeorev.2010.02.002openalex-snapshot
Developing models using GIS to assess geological and economic risk: An example from VMS copper gold mineral exploration in Oman
inferredcites →https://doi.org/10.1016/s0098-3004(02)00078-xopenalex-snapshot
Data-driven fuzzy analysis in quantitative mineral resource assessment
inferredcites →https://doi.org/10.1016/s0098-3004(97)00117-9openalex-snapshot
GENERALISED LINEAR MODELLING OF SUSCEPTIBILITY TO LANDSLIDING IN THE CENTRAL APENNINES, ITALY
inferredcites →https://doi.org/10.1016/s0893-6080(97)00079-8openalex-snapshot
Fuzzy and neural approaches in engineering
inferredcites →https://doi.org/10.1023/a:1010147818806openalex-snapshot
Geologically Constrained Probabilistic Mapping of Gold Potential, Baguio District, Philippines
inferredcites →https://doi.org/10.1023/a:1021602316101openalex-snapshot
Evaluation of Weights of Evidence to Predict Epithermal-Gold Deposits in the Great Basin of the Western United States
inferredcites →https://doi.org/10.1023/a:1021886501912openalex-snapshot
Mineral Favorability Mapping: A Comparison of Artificial Neural Networks, Logistic Regression, and Discriminant Analysis
inferredcites →https://doi.org/10.1023/a:1025171803637openalex-snapshot
Artificial Neural Networks for Mineral-Potential Mapping: A Case Study from Aravalli Province, Western India
inferredcites →https://doi.org/10.1023/a:1025175904545openalex-snapshot
Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping
inferredcites →https://doi.org/10.1023/b:narr.0000007804.27450.e8openalex-snapshot
A Comparative Analysis of Favorability Mappings by Weights of Evidence, Probabilistic Neural Networks, Discriminant Analysis, and Logistic Regression
inferredcites →https://doi.org/10.1023/b:narr.0000046919.87758.f5openalex-snapshot
Weights of Evidence Modeling of Mineral Potential: A Case Study Using Small Number of Prospects, Abra, Philippines
inferredcites →https://doi.org/10.1029/sc010p0015openalex-snapshot
Integration of geological datasets for gold exploration in Nova Scotia
inferredcites →https://doi.org/10.1046/j.1440-0952.2000.00807.xopenalex-snapshot
Artificial neural networks: A new method for mineral prospectivity mapping
inferredcites →https://doi.org/10.1046/j.1440-0952.2000.00816.xopenalex-snapshot
Vectorial fuzzy logic: A novel technique for enhanced mineral prospectivity mapping, with reference to the orogenic gold mineralisation potential of the Kalgoorlie Terrane, Western Australia
inferredcites →https://doi.org/10.1046/j.1440-0952.2000.00821.xopenalex-snapshot
Using fuzzy logic in a Geographic Information System environment to enhance conceptually based prospectivity analysis of Mississippi Valley‐type mineralisation
inferredcites →https://doi.org/10.1046/j.1440-0952.2001.00889.xopenalex-snapshot
Porphyry copper potential mapping using the weights-of- evidence model in a GIS, northern Shahr-e-Babak, Iran
inferredcites →https://doi.org/10.1080/0143116021000031791openalex-snapshot
Artificial neural networks as a tool for mineral potential mapping with GIS
inferredcites →https://doi.org/10.1080/01431169508954607openalex-snapshot
A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery
inferredcites →https://doi.org/10.1080/07038992.1993.10855151openalex-snapshot
On Mathematical Representation and Integration of Multiple Spatial Geoscience Data Sets
inferredcites →https://doi.org/10.1109/36.752193openalex-snapshot
Verification of the nonparametric characteristics of backpropagation neural networks for image classification
inferredcites →https://doi.org/10.1109/igarss.1993.322084openalex-snapshot
An evidential reasoning structure for integrating geophysical, geological and remote sensing data
inferredcites →https://doi.org/10.1109/igarss.1995.524130openalex-snapshot
Information representation and integration of multiple sets of spatial geoscience data
inferredcites →https://doi.org/10.1111/j.1365-2478.2008.00779.xopenalex-snapshot
Artificial neural networks applied to mineral potential mapping for copper‐gold mineralizations in the Carajás Mineral Province, Brazil
inferredcites →https://doi.org/10.1111/j.1751-3928.2008.00050.xopenalex-snapshot
Regional Probabilistic and Statistical Mineral Potential Mapping of Gold–Silver Deposits Using GIS in the Gangreung Area, Korea
inferredcites →https://doi.org/10.1111/j.1939-7445.2007.tb00215.xopenalex-snapshot
MODELING THE SPATIAL DISTRIBUTION OF MINERAL DEPOSITS USING NEURAL NETWORKS
inferredcites →https://doi.org/10.5860/choice.32-6239openalex-snapshot
Geographic information systems for geoscientists: modelling with GIS

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Multi-step-ahead Method for Wind Speed Prediction Correction Based on Numerical Weather Prediction and Historical Measurement Data
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Wind Speed Prediction in Non-Monitored Areas Based on Topographic Radial Basis Neural Network (T-RBNN)
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Analysis of Digital Tourist's Purchasing Decision Process Based on Feedback and Opinions
inferred← citeshttps://doi.org/10.3390/en14144079openalex-snapshot
Machine Learning—A Review of Applications in Mineral Resource Estimation
inferred← citeshttps://doi.org/10.3390/mining5010005openalex-snapshot
Envisioning Human–Machine Relationship Towards Mining of the Future: An Overview
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Applications, Promises and Challenges of Artificial Intelligence in Mining Industry: A Review
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Applications, Promises and Challenges of Artificial Intelligence in Mining Industry: A Review
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