SAYANTAN MAJUMDAR

GEOLOGICAL ENGINEERING 

GROUNDWATER WITHDRAWAL ESTIMATION USING INTEGRATED REMOTE SENSING PRODUCTS AND MACHINE LEARNING

Advisor:     Dr. Ryan Smith  

Date & Time:
   Friday, July 1, 2022 at 8:00 AM
Location:   
 https://umsystem.zoom.us/j/99960626026?pwd=bmxBMlhPbFBFejd4OEc1YTBpUVBjQT09   

Abstract:     The rising demands for water, food, and energy primarily driven by the increasing global population constitute a pressing issue worldwide. Therefore, the water-food-energy nexus plays a substantial role in developing globally applicable sustainable solutions. Recent technological advancements including the earth observation programs using spaceborne remote sensing platforms have enabled us to monitor various critical components affecting the entire globe. Groundwater which comprises of the world's 30% freshwater is one such key component of the global water resources and supplies nearly half of the global drinking water.
       Despite groundwater overdraft in many parts of the world, including the United States (US), there are limited efforts to monitor groundwater withdrawals at scales suitable for addressing water security issues. In this research, different passive and active satellite sensor data are utilized for predicting annual groundwater withdrawals at various spatial resolutions ranging from 1 km to 5 km. The goal of this thesis is to develop an integrated approach combining various remote sensing, modeled, and gridded hydrometeorological data sets with a machine learning model. This framework automatically learns the inter-relationships among these variables and groundwater withdrawals and is tested in three different regions (with substantially varying climate, aquifer characteristics, irrigation demands, and in-situ data sets) of the Conterminous US -- Kansas, Arizona, and the Mississippi Alluvial Plain. The results show good agreement with the in-situ groundwater pumping data available over these regions with the coefficient of determination (R^2) varying from 0.5 to 0.8.           

JAVIER VALENTIN-SIVICO

ENGINEERING MANAGEMENT 

EVALUATING BARRIERS TO AND IMPACTS OF RURAL BROADBAND ACCESS=

Advisor:     Dr. Casey Canfield    

Date & Time:
   Friday, July 1, 2022 at 10:30 AM
Location:   
 https://umsystem.zoom.us/j/93533100253?pwd=ekNVbVEvZU9CZGlrVjZROGdyYThOUT09&from=ad    

Abstract:     The lack of adequate broadband infrastructure persists in many rural communities. Funding programs administered by the state and federal government seek to incentivize the deployment of broadband infrastructure in areas where internet service providers cannot justify investment without governmental intervention. However, additional barriers persist, such as digital literacy and community-level self-efficacy. As a result, the first contribution articulates barriers at the organizational level. Regional Planning Commissions (RPCs) are intergovernmental organizations supporting their region’s economic development and infrastructure needs, but their involvement with rural broadband has been limited in Missouri pre-COVID. This work proposes a framework based on the Theory of Planned Behavior to highlight stakeholder dynamics that have constrained RPCs from making a more significant contribution to the deployment of broadband infrastructure in rural areas. One approach to address these barriers is to provide local stakeholders with data and analytical tools to evaluate the benefits and costs of various broadband options for their community, since there is not a one-size-fits-all solution. To this end, there are three contributions that provide guidance for evaluating the benefits and costs of improved broadband access. The first solution proposes a benefit-cost analysis (BCA) at the county-level where changes in tax revenue are used to monetize the impact of rural broadband for a hypothetical Midwest county. The proposed model incorporates the cost associated with treating problematic internet uses to monetize the negative impact of the technology. The second solution demonstrates a method for evaluating the benefit of broadband in terms of social impact for education, employment and healthcare in a small under-served community in northwest Missouri. Pre- and post-survey data were used to conduct comparisons between the targeted community, which received faster internet, and control communities. Results suggest that increased use for education is the first measurable outcome of faster internet access. The third solution describes a socio-technical reference architecture to support the development of community-driven broadband projects. By providing data and analytical tools for designing and evaluating the impact of broadband solutions for rural communities, this research increases the capability of local communities to identify and advocate for broadband solutions that fit their needs.       

SARA FAYEK

CIVIL ENGINEERING

EXPERIMENTAL DETERMINATION OF THE YIELD CURVE FOR UNSATURATED SOILS

Advisor:     Dr. Xiong Zhang
                  Dr. Jeffrey Cawlfield    

Date & Time:
   Friday, July 1, 2022 at 2:00 PM
Location:   
 Butler-Carlton Hall Room 312   

Abstract:    An important step to developing a good understanding of the behavior of unsaturated soils is to adopt a formal and simple constitutive framework and to conduct high-quality and short-time experiments. The first elastoplastic model for unsaturated soils, known as the Barcelona Basic Model (BBM), has received extensive acceptance as it could explain many features of unsaturated soils under drained conditions. The yield curve, which separates the elastic region from the elastoplastic region, is an important aspect of constitutive modeling for unsaturated soils. Most concepts for unsaturated soils were developed based upon the results from suction-controlled triaxial (SCTX) tests on soil specimens with “identical stress histories”. However, the SCTX tests on unsaturated soils are costly, time-consuming, and laborious, and their results are questionable. In addition, it is almost impossible to have soil specimens with identical stress histories due to imperfect preparation processes and improper experimental design. Consequently, the existing analysis methods based on the SCTX tests to develop the BBM and other constitutive models for unsaturated soils may not be feasible.
       This study intends to develop an alternative method to overcome the limitations of the SCTX tests for the constitutive modeling of unsaturated soils and to determine correctly the shape and the evolution of the Loading/Collapse (LC) yield curves during yielding. A photogrammetry-based method is used to determine the absolute soil volume during triaxial testing. Further evaluation of triaxial testing is presented by determining the soil specimen misalignment (tilting and eccentricity) and disturbance at different testing stages. The implication of misalignment on the stress-strain relationship of soils during triaxial testing is investigated. In addition, a study of the requirement of point density in image-analysis methods during triaxial testing is presented. Finally, the Modified State Surface Approach (MSSA) is adopted to calibrate unsaturated soil parameters on constant water content (undrained) tests and determine the shapes of yield curves for unsaturated soils. A modification to BBM is also proposed to handle the dilatancy observed in the undrained triaxial tests and modified unconfined compression tests on silty soil specimens. 

PRISCILLA CODJOE

MATHEMATICS (STATISTICS EMPHASIS) 

SURVIVOR BOND MODELS FOR SECURITIZING LONGEVITY RISK

Advisor:     Dr. Akim Adekpedjou  

Date & Time:
   Tuesday, July 5, 2022 at 10:00 AM
Location:     
 Rolla Building, G4       

 

Abstract:     Longevity risk is the risk that a reference population's mortality rates deviate from what is projected from prior life tables. The deviation is due to development in biomedical studies which have dramatically increased life expectancy over time. Longevity risk raises life insurers' liability, increase product costs and reserves. Securitization through longevity derivatives is a way of dealing with this risk.
       To enhance the pricing of life contingent products, we present an additive type mortality model in the spirit of Lee-Carter model. This model incorporates policyholder covariates that have a multiplicative effect on a baseline mortality rate. Using counting processes and martingale machinery, we obtain close form expression for the model's parameters. We use the bond pricing approach to price longevity bonds via tranches. Numerical studies suggest that asymptotic properties of model parameter estimators provide a close approximation of the true.
       Pricing longevity derivatives can also be done via a no-arbitrage approach by risk-adjusting the mortality and/or interest rate risks. There are various ways to calibrate the risk-adjusted probability measure. The risk neutral approach and the Wang transform are among the popular methods. In this work, we employ a mean-reverting Hull-White model with a moving target which was recently proposed in the literature of mortality model and the Vasicek model for interest rate. We detail how to develop the risk-neutral measure in pricing longevity bonds using the powerful Girsanov theorem.