• Login
    View Item 
    •   Home
    • University of Alaska Fairbanks
    • UAF Graduate School
    • Engineering
    • View Item
    •   Home
    • University of Alaska Fairbanks
    • UAF Graduate School
    • Engineering
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Scholarworks@UACommunitiesPublication DateAuthorsTitlesSubjectsTypeThis CollectionPublication DateAuthorsTitlesSubjectsType

    My Account

    Login

    First Time Submitters, Register Here

    Register

    Statistics

    Display statistics

    Predictive Performance Of Machine Learning Algorithms For Ore Reserve Estimation In Sparse And Imprecise Data

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Dutta_S_2006.pdf
    Size:
    4.624Mb
    Format:
    PDF
    Download
    Author
    Dutta, Sridhar
    Chair
    Bandopadhyay, Sukumar
    Keyword
    Mining engineering
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/11122/8873
    Abstract
    Traditional geostatistical estimation techniques have been used predominantly in the mining industry for the purpose of ore reserve estimation. Determination of mineral reserve has always posed considerable challenge to mining engineers due to geological complexities that are generally associated with the phenomenon of ore body formation. Considerable research over the years has resulted in the development of a number of state-of-the-art methods for the task of predictive spatial mapping such as ore reserve estimation. Recent advances in the use of the machine learning algorithms (MLA) have provided a new approach to solve the age-old problem. Therefore, this thesis is focused on the use of two MLA, viz. the neural network (NN) and support vector machine (SVM), for the purpose of ore reserve estimation. Application of the MLA have been elaborated with two complex drill hole datasets. The first dataset is a placer gold drill hole data characterized by high degree of spatial variability, sparseness and noise while the second dataset is obtained from a continuous lode deposit. The application and success of the models developed using these MLA for the purpose of ore reserve estimation depends to a large extent on the data subsets on which they are trained and subsequently on the selection of the appropriate model parameters. The model data subsets obtained by random data division are not desirable in sparse data conditions as it usually results in statistically dissimilar subsets, thereby reducing their applicability. Therefore, an ideal technique for data subdivision has been suggested in the thesis. Additionally, issues pertaining to the optimum model development have also been discussed. To investigate the accuracy and the applicability of the MLA for ore reserve estimation, their generalization ability was compared with the geostatistical ordinary kriging (OK) method. The analysis of Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Error (ME) and the coefficient of determination (R2) as the indices of the model performance indicated that they may significantly improve the predictive ability and thereby reduce the inherent risk in ore reserve estimation.
    Description
    Dissertation (Ph.D.) University of Alaska Fairbanks, 2006
    Date
    2006
    Type
    Dissertation
    Collections
    Engineering

    entitlement

     
    ABOUT US|HELP|BROWSE|ADVANCED SEARCH

    The University of Alaska Fairbanks is an affirmative action/equal opportunity employer and educational institution and is a part of the University of Alaska system.

    ©UAF 2013 - 2023 | Questions? ua-scholarworks@alaska.edu | Last modified: September 25, 2019

    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.