Inference Engine for Sulis

1. Inference Engine • The Sulis Framework • Extrapolation Testing • Testing the Inference Engine • Sulis Informatics Services includes an architecture to collect, store, and present environmental data, so that resource managers can make informed decisions. A portion of these data come from sophisticated, multi-dimensional numerical models that predict time-and-space-varying hydrologic and hydrodynamic behavior. • The Inference Engine is targeted toward three use cases: interpolation within a data set, extrapolation within a data set, and extrapolation from multiple data sets (see below for more information). Each of these cases naturally builds upon the previous case; if interpolation within a data set is inaccurate or implausible, it follows that extrapolation will most likely also be inaccurate or implausible. If either of the first two cases are not successful, it is likely that extrapolation between multiple data sets will also not be successful. Thus, each of these cases must be examined in order to determine if the Inference Engine can achieve appropriate levels of accuracy and efficiency. • Inference Engine for Sulis • Unfortunately, using sophisticated numerical models can require significant time and effort and, thus, the number of simulations must be limited. The Sulis Inference Engine leverages a limited number of necessary model simulations by generating pseudo-simulations so that new information can be generated quickly and easily. The Inference Engine offers three tasks: interpolation within a data set or model simulation, extrapolation from a data set or model simulation, and interpolation between multiple model simulations. • Extrapolation between Multiple Data Sets • This test is based on EFDC Model outputs at a section of Fish River, in Alabama. In addition to the original data, a flow variable was added through a quadratic interpolation. This test predicts the flow at a certain point on the Fish River, based on the discharge water depth. • Methodology • The Inference Engine takes in some input data, generated by computational models, and uses them to predict additional data. The Inference Engine specifically uses support vector regression and spline regression to make its predictions. • While the Inference Engine is designed to avoid the steep time costs of additional model runs, it must also have a sufficient level of accuracy to meet the needs of the project. As such, the results are compared to linear regression, to provide a baseline of minimum acceptable accuracy. • Both the interpolation and extrapolation tests are based on EFDC Model outputs for the Mobile Bay area in Alabama. Both tests predict the temperature at the bottom of Mobile Bay, based on time step and position (x, y, and z ) and input boundary conditions of water elevation, flow, salinity, and temperature. The x and y positions for each data point are distributed irregularly across a rectangular grid, but remain the same between time steps. • Support Vector Regression • Interpolation Testing • Analysis Results • Support vector regression is implemented using the LIBSVM software package. This implementation contains kernel parameters that, when optimized, increase prediction accuracy. • Both support vector regression and spline regression are more accurate than linear regression, under current conditions. However, spline regression is vastly more accurate than support vector regression in predicting the comparatively simple data of the third test. Most likely, this is due to spline regression being a more effective predictor of simple data than either linear regression or unoptimized SVR. However, additional testing is needed to more accurately and completely determine the relative limitations of both prediction methods; for these reasons, simulation experiments are ongoing. • This analysis does indicate that interpolation, extrapolation, and extrapolation between multiple data sets are feasible, and, thus, that the Inference Engine will be able to effectively predict model data. Future tests will focus on determining the limitations of the Inference Engine and further clarifying what data it can and can not effectively predict. • Spline Regression • Spline regression is implemented using the earth software package, from the R programming language. This package is based on the MARSPLINES techniques created by Thomas Friedman. • Nate Phillips • ncp38@msstate.edu • Mississippi State University • CI Strategy 2