Hydraulic conductivity prediction for sandy soils

The potential for petroleum-contaminated soils to impact ground water is evaluated using the soil leachability model in South Carolina and Georgia. In this model, the Green and Ampt (1911) equation is used to estimate unsaturated flow with saturated hydraulic conductivity (K(S)) values obtained using the Rawls and Brakensiek (1989) equation from inputs of percent sand- and clay-sized particles. However, many soils have > 70% sand-sized particles, which is the maximum amount for which the Rawls and Brakensiek equation is valid. Therefore, 70 sets of K(S) and particle-size data from the literature for southeastern United States sandy soils were analyzed to develop a new equation for estimating K(S). A multiple linear regression model with an adjusted R2 = 0.65 (p < 0.0001) was developed from percent clay- and sand-sized particle data. Eight additional sets of data were used to validate the model. The root mean square deviation and maximum squared difference, (yi - yi(2)), values for the new model are smaller than those obtained using the Rawls and Brakensiek equation, or Rosetta, a neural network-based model (Schaap 1999). The new model provides estimates that are within an order of magnitude for all but one of the test data sets. Rosetta predicts K(S) within one order of magnitude of measured values for six test data, and predicts K(S) within two orders of magnitude for the other two. The Rawls and Brakensiek predictions are within one order of magnitude for four test data, but are two or more orders of magnitude too high for the remaining four points. The new equation is recommended for estimating K(S) for sandy southeastern United States soils for which inputs are limited to percent sand and clay.     

Geospatial Ontology Development and Semantic Analytics

Geospatial ontology development and semantic knowledge discovery addresses the need for modeling, analyzing and visualizing multimodal information, and is unique in offering integrated analytics that encompasses spatial, temporal and thematic dimensions of information and knowledge. The comprehensive ability to provide integrated analysis from multiple forms of information and use of explicit knowledge make this approach unique. This also involves specification of spatiotemporal thematic ontologies and populating such ontologies with high quality knowledge. Such ontologies form the basis for defining the meaning of important relations terms, such as near or surrounded by, and enable computation of spatiotemporal thematic proximity measures we define. SWETO (Semantic Web Technology Evaluation Ontology) and geospatial extension SWETO‐GS are examples of these ontologies. The Geospatial Semantics Analytics (GSA) framework incorporates: (1) the ability to automatically and semi‐automatically tract metadata from syntactically (including unstructured, semi‐structured and structured data) and semantically heterogeneous and multimodal data from diverse sources; and (2) analytical processing that exploits these ontologies and associated knowledge bases, with integral support for what we term spatiotemporal thematic proximity (STTP) reasoning and interactive visualization capabilities. This paper discusses the results of our geospatial ontology development efforts as well as some new semantic analytics methods on this ontology such as STTP.