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    Application and Limitation of Deep Learning Algorithms to Hydrogeology- Data Driven Approaches to Understanding Effective Hydraulic Conductivity, Flux, and Monitoring Network Design

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    Author
    Abdolhosseini Moghaddam, Mohammad
    Issue Date
    2020
    Keywords
    Data Driven
    Groundwater Interaction
    Hydrogeology
    Machine Learning
    Advisor
    Ferre, P.A Ty
    
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    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Groundwater monitoring at regional scales using conventional methods is challenging because of the need for regular measurements and due to high measurement error associated with existing instruments. With advances in sensor technology and wireless communication, automated groundwater monitoring systems provide us the opportunity to collect groundwater data with high temporal resolution. In the current study, we investigated the feasibility of using those high resolution collected data along with deep learning (DL) and machine learning (ML) algorithms to improve the computational and accuracy of water flux and parameter upscaling estimations. The results of this work are presented in the form of three studies. In the first study, simple ML algorithms, regression tree, and gradient boosting analyses were used to estimate flux using temperature and pressure data. Further, we examine how many and what type of observations (pressure and/or temperature) were necessary and at what depths to estimate surface/groundwater exchange based on simulated data provided by researchers at the Pacific Northwest National Laboratories for the Department of Energy Hanford site. The results suggest that the flux beneath a river can be determined with high temporal resolution (5 minutes) using a single combined temperature and pressure probe, but it cannot be determined using temperature sensors alone if temperature records include measurement error. In the second study, we extended the analysis of the first study by applying DL algorithms to estimate flux using temperature sensors alone with the presence of errors in the measurements. The analysis revealed that DL methods outperform the ML methods, especially convolutional neural networks when used to interpret noisy temperature data with a smoothing filter applied. Also, we attempted to utilize the Accumulated Local Effect to extract the importance of features in DL algorithms. In the third study, we used DL algorithms to infer the effective hydraulic conductivity of a binary conductivity field. Specifically, we made use of the energy dissipation weighting, which represents the importance of a cell in determining the flow field. Using UNET architecture, as an image to image translation model, we could retrieve both Keff and the energy dissipation weighting mapping from the conductivity field without running flow models. Finally, we examined what hidden layer activation output might represent if the model is designed based on physical information about the system.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Hydrology
    Degree Grantor
    University of Arizona
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