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Herein, a recently developed methodology, Support Vector Machines (SVMs), is presented and applied to the challenge of soil moisture prediction. Support Vector Machines are derived from statistical learning theory and can be used to predict a quantity forward in time based on training that uses past data, hence providing a statistically sound approach to solving inverse problems. The principal strength of SVMs lies in the fact that they employ Structural Risk Minimization (SRM) instead of Empirical Risk Minimization (ERM). The SVMs formulate a quadratic optimization problem that ensures a global optimum, which makes them superior to traditional learning algorithms such as Artificial Neural Networks (ANNs). The resulting model is sparse and not characterized by the "curse of dimensionality." Soil moisture distribution and variation is helpful in predicting and understanding various hydrologic processes, including weather changes, energy and moisture fluxes, drought, irrigation scheduling, and rainfall/runoff generation. Soil moisture and meteorological data are used to generate SVM predictions for four and seven days ahead. Predictions show good agreement with actual soil moisture measurements. Results from the SVM modeling are compared with predictions obtained from ANN models and show that SVM models performed better for soil moisture forecasting than ANN models.

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[1] This study presents a methodology for designing long-term groundwater head monitoring networks in order to reduce spatial redundancy. A spatially redundant well does not change the potentiometric surface estimation error appreciably, if not sampled. This methodology, based on Support Vector Machines, makes use of a uniquely solvable quadratic optimization problem that minimizes the bound on generalized risk, rather than just the mean square error of differences between measured and ''predicted'' groundwater head values. The nature of the optimization problem results in sparse approximation of the function defining the potentiometric surface that was utilized to select the number and locations of long-term monitoring wells and guide future data collection efforts, which is a prerequisite in building and calibrating regional flow and transport models. The methodology is applied to the design of regional groundwater monitoring networks in the Water Resources Inventory Area (WRIA) 1, Whatcom County, northern Washington State, USA.INDEX TERMS: 1829 Hydrology: Groundwater hydrology; 1848 Hydrology: Networks; 9820 General or Miscellaneous: Techniques applicable in three or more fields;

[1] The reconstruction of low-order nonlinear dynamics from the time series of a state variable has been an active area of research in the last decade. The 154 year long, biweekly time series of the Great Salt Lake volume has been analyzed by many researchers from this perspective. In this study, we present the application of a powerful state space reconstruction methodology using the method of support vector machines (SVM) to this data set. SVM are machine learning systems that use a hypothesis space of linear functions in a kernel-induced higher-dimensional feature space. SVM are optimized by minimizing a bound on a generalized error (risk) measure rather than just the mean square error over a training set. Under Mercer's conditions on the kernels the corresponding optimization problems are convex; hence global optimal solutions can be readily computed. The SVM-based reconstruction is used to develop time series forecasts for multiple lead times ranging from 2 weeks to several months. Unlike previously reported methodologies, SVM are able to extract the dynamics using only a few past observed data points out of the training examples. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analysis, with a particular interest in forecasting extreme states. Efforts are also made to assess variations in predictability as a function of initial conditions and as a function of the degree of extrapolation from the state space used for learning the model.