• Login
    View Item 
    •   Home
    • Office of Sponsored Research (OSR)
    • KAUST Funded Research
    • Publications Acknowledging KAUST Support
    • View Item
    •   Home
    • Office of Sponsored Research (OSR)
    • KAUST Funded Research
    • Publications Acknowledging KAUST Support
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    Sample size reduction in groundwater surveys via sparse data assimilation

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Conference Paper
    Authors
    Hussain, Z.
    Muhammad, A.
    Date
    2013-04
    Permanent link to this record
    http://hdl.handle.net/10754/599550
    
    Metadata
    Show full item record
    Abstract
    In this paper, we focus on sparse signal recovery methods for data assimilation in groundwater models. The objective of this work is to exploit the commonly understood spatial sparsity in hydrodynamic models and thereby reduce the number of measurements to image a dynamic groundwater profile. To achieve this we employ a Bayesian compressive sensing framework that lets us adaptively select the next measurement to reduce the estimation error. An extension to the Bayesian compressive sensing framework is also proposed which incorporates the additional model information to estimate system states from even lesser measurements. Instead of using cumulative imaging-like measurements, such as those used in standard compressive sensing, we use sparse binary matrices. This choice of measurements can be interpreted as randomly sampling only a small subset of dug wells at each time step, instead of sampling the entire grid. Therefore, this framework offers groundwater surveyors a significant reduction in surveying effort without compromising the quality of the survey. © 2013 IEEE.
    Citation
    Hussain Z, Muhammad A (2013) Sample size reduction in groundwater surveys via sparse data assimilation. 2013 10th IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC). Available: http://dx.doi.org/10.1109/ICNSC.2013.6548732.
    Sponsors
    The authors would like to thank Dr. Ibrahim Hoteit andMr Mohamad El Gharamti at KAUST for providing us withthe code to simulate 2D contaminant flow in the groundwater.We also thank Hasan Arshad Nasir who helped us greatly inthe initial phase of the project. This work was carried outat the Laboratory of Cyber Physical Networks and Systems(CYPHYNETS) at LUMS under a project funded by theEnvironmental Protection Agency (EPA) of the Governmentof Punjab, Pakistan.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2013 10th IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC)
    DOI
    10.1109/ICNSC.2013.6548732
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICNSC.2013.6548732
    Scopus Count
    Collections
    Publications Acknowledging KAUST Support

    entitlement

     
    DSpace software copyright © 2002-2023  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted 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. For anonymous users the allowed maximum amount is 50 search results.

    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.