AUB ScholarWorks

Stochastic modeling of saltwater intrusion in highly heterogeneous coastal aquifers.

Show simple item record

dc.contributor.author Safi, Amir Mohammadreza
dc.date.accessioned 2020-03-27T21:10:12Z
dc.date.available 2020-03-27T21:10:12Z
dc.date.issued 2019
dc.date.submitted 2019
dc.identifier.other b23507408
dc.identifier.uri http://hdl.handle.net/10938/21623
dc.description Dissertation. Ph.D. American University of Beirut. Department of Civil and Environmental Engineering, 2019. ED:117
dc.description Advisor : Dr. Mutasem El-Fadel, Professor, Civil and Environmental Engineering ; Committee Chair : Dr. Mounir Mabsout, Professor, Civil and Environmental Engineering ; Members of Committee : Dr. Joanna Doummar, Assistant Professor, Geology ; Dr. Ibrahim Alameddine, Assistant Professor, Civil and Environmental Engineering ; Dr. Steen Christensen, Associate Professor, Geosciences, Aarhus University (External) ; Dr. Elie Bou-Zeid, Professor, Civil and Environmental Engineering, Princeton University (External) ; Dr. Majdi Abou Najm, Assistant Professor, Land, Air and Water Resources, University of California Davis (External)
dc.description Includes bibliographical references (leaves 199-219)
dc.description.abstract This research presents a novel method for a reliable quantification of uncertainties with the prediction of saltwater intrusion (SWI) in poorly field-characterized heterogeneous coastal aquifers. The method uses crude prior information about hydrogeological input parameters, and simultaneously incorporates parameter uncertainty and imprecision in prior information to infer the required statistics for a Monte Carlo (MC) simulation into the uncertainty analysis. Compared with other methods, it is a computationally effective method for uncertainty analysis in highly heterogeneous coastal aquifers. In this method, the Sequential Gaussian Simulation (SGS) is used to create random parameter fields while fuzzy set theory accounts for the imprecision pertaining to prior information. Prediction uncertainties are evaluated by generating a large number of calibration-constrained models using the Null Space Monte Carlo (NSMC) method. This research presents another novel method that is used to design additional hydrogeological field-investigations towards reducing the prediction uncertainties in a SWI system. The method provides flexibility concerning model dimensionality, allows for any desired task-oriented formulation, targets any measurement type, accounts for various sources of uncertainty while also ensuring that it is cost-effective. It expands on the existing linear data-worth analysis method through the incorporation of Bayesian model averaging (BMA) and genetic algorithms (GA) when conducting a three-dimensional location search for sampling new data in non-linear systems. Both methods can use any model independent tools for parameter estimation and any variable-density simulation codes. The efficiencies of these methods in quantifying and reducing prediction uncertainties were demonstrated using the SEAWAT model and data from an actual heterogeneous coastal aquifer with limited hydrogeological field-data. Located along the Eastern Mediterranean (Beirut), the pilot aquifer consists of karstified limestone of Cre
dc.format.extent 1 online resource (xvii, 238 leaves) : illustrations
dc.language.iso eng
dc.subject.classification ED:000117
dc.subject.lcsh Saltwater encroachment -- Lebanon -- Beirut.
dc.subject.lcsh Aquifers -- Lebanon -- Beirut.
dc.subject.lcsh Stochastic models.
dc.subject.lcsh Coastal zone management -- Lebanon -- Beirut.
dc.subject.lcsh Monte Carlo method.
dc.subject.lcsh Groundwater -- Quality -- Lebanon -- Beirut.
dc.subject.lcsh Climatic changes.
dc.title Stochastic modeling of saltwater intrusion in highly heterogeneous coastal aquifers.
dc.type Dissertation
dc.contributor.department Department of Civil and Environmental Engineering
dc.contributor.faculty Maroun Semaan Faculty of Engineering and Architecture
dc.contributor.institution American University of Beirut


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search AUB ScholarWorks


Browse

My Account