GeoComputational Intelligence and High-Performance Geospatial Computing

Assistant Professor, School of Natural Resources. Center for Advanced Land Management Information Technologies, University of Nebraska – LincolnPlatinum Sponsors Coca-Cola Gold Sponsors KU Department of Geography KU Institute for Policy & Social Research KU Libraries GIS and Data Services State of Kansas Data Access and Support Center (DASC) Wilson & Company Engineers and Architects Silver Sponsors Bartlett & West Kansas Applied Remote Sensing Program KansasView Bronze Sponsors Garmin KU Biodiversity Institut

Analysis of Data of Different Spatial Support: A Multivariate Process Approach

Inherent to a spatial variable is the unit of support at which it is measured. In many studies, variables are observed at different support. For example, disease rates might be measured at an aggregated level while temperature is usually measured at specific points. It is still an interesting problem to study the relationship of variables having different support. However, it may be a different problem to statistically model the relationship of variables of different support, particularly when the supports do not have a hierarchical structure. Currently, cokriging, the use of one or more spatial variables to predict another variable, is applied to variables of the same support. In this work, I extend cokriging for use with variables of different support by constructing a nonparametric cross-covariance matrix. This method is flexible as it applies to any marginal spatial model and is suited to large datasets because it uses latent variables which can assist with dimension reduction. The proposed nonparametric method is demonstrated with two correlated variables which are measured at different spatial units. In addition, the method is implemented using two algorithms; one which yields an optimized matrix (Wang, 2011) and the other which produces an approximately optimized matrix but is computationally more efficient (Hu 2013). The results show that the method is appropriate for predicting data of different support and that it outperforms some competing methods with respect to predictive performance. Furthermore, as expected, the approximately optimized matrix does not perform as well as the alternative algorithm, but it performs better than the comparative methods

Studying Complex Aquifer Systems from Large-Scale Stratigraphy Development to Local Aquifer Storage and Recovery

Hydrostratigraphy model is an essential component of building valid groundwater models. Many challenges are associated with constructing hydrostratigraphy models which include geological complexities such as faults, domes, and angular unconformities. Developing a method with an emphasis on capturing big data to thoroughly inform large-scale models is one of the challenges addressed in the first part of this study. The method is predicated upon discretization of the study domain into tiles based on the geological dip direction and faults. The application of the method in the state of Louisiana with the utilization of more than 114000 well logs demonstrates promising results including identification of hydrostratigraphic characteristics for different aquifers, connections between the Mississippi River and the Red River and their alluviums, connections between state\u27s surface waters and aquifers, and identification of recharge zones. The Louisiana model also demonstrated two different sand patterns in southeast Louisiana which might have been caused by two depositional environments. Employment of the method in a groundwater flow modeling framework to build a flow model for the Chicot aquifer system in southwest Louisiana revealed the complexity of the aquifer system that contains highly interconnected aquifer sands. The groundwater flow analysis of the Chicot aquifer is of great importance because it is the most heavily pumped aquifer in the state as a part of the Coastal Lowland Aquifer System. The modeling results show that the storage loss due to groundwater pumping is offset by inflows from surficial recharge, rivers, and boundaries. The two large cones of depression created by the agricultural pumping in the east and by the industrial pumping in the west represent the key feature in the Chicot aquifer system. As the final goal of this study, an aquifer storage and recovery operation in south of the Chicot aquifer was studied. The focus of this part was on optimal scheduling of an aquifer storage and recovery (ASR) operation while addressing parameter uncertainty for one cycle where an injection season is followed by a pumping season. This end was achieved via utilization of a supervised learning method for surrogated modeling and use of an evolutionary optimization method. The results indicate that artificial neural network (ANN) is a promising tool for evaluation of ASR efficiency. The hydraulic conductivity and longitudinal dispersivity were found to be the most significant parameters which affect ASR