Power Computing at Universal Engineering is not just number crunching and super computers. We have nationally recognized experts in the development statistical models for environmental analysis, engineering statistics and Optimization of nonlinear Engineering systems. We have extensive experience with time series forecasting models, frequency analysis, risk assessment analysis, model uncertainty analysis, stochastic modeling, data mining, and linear and nonlinear optimization . Our toolbox includes statistical language environments such as S+, R, SAS, Statistica and MATLAB. We have extensive experience in environmental statistics in general and in wetland statistics in particular.

Universal is a leading expert in Artificial Neural Network (ANN) applications to approximate complex systems by appropriate mapping of input/output data to quantify the cause effect relationship. ANN models are effective tools for environmental analysis and to model complex engineering systems when their physical representative models are laborious and computationally intensive. Our work experience ranges from ordinary ANNs to time delayed autoregressive ANNs in approximating rainfall runoff, rainfall stage and pumping groundwater decline relationships to help water managers and planners with water management plans and operations.  As a master in ANN applications in engineering statistics, Universal Engineering has pioneered breakthrough models in environmental statistics.

Universal Engineering Geostatistical expertise is a core competency with exceptional record of developed tools and analysis. Universal Engineering personnel have used spatial data analysis tools including Ordinary and Universal Kriging (and coKriging), Local polynomial, SPLINE and Kth Nearest Neighbor (k-NN) methods, and have developed other geostatistical tools for subsurface characterization of statistically nonstationary environments using borehole and geophysical data to map aquifer systems for preferential pathway identification. We have also applied Sequential Gaussian Simulations and Sequential Kernel Simulations to provide plausible realizations of alluvium deposit environments. In addition to subsurface applications, we have a record of spatial characterization for rainfall data and building the primary rainfall binary file as input to most of Central and South Florida hydrologic and environmental studies. Geostatistics software used by our staff include in house products, GSLIB, ArcGIS, GMS, R, and S+ and they were used extensively in ecosystem statistics related studies.