Jared Smith

Background

About Jared Smith

My background is in environmental systems engineering, spatial data analysis, and statistics. My research interests include coupling physical and mathematical models with statistical analyses to inform planning and management decisions for environmental, earth-energy, and water resources systems. Water and environmental systems: watershed modeling, stormwater management, green infrastructure, stochastic hydrology, and flood frequency analysis. Model diagnostics and decision support: exploratory spatial data analysis, global sensitivity analysis, Bayesian inference, multi-objective evolutionary algorithms, stochastic optimization, and robust decision making. Geothermal energy systems: geostatistical resource assessment, stochastic reservoir thermal-hydraulic modeling, sedimentary basin hydrogeology, site suitability analysis, techno-economic analysis, and direct-use heating. My current research uses machine learning models to predict flood flow characteristics across the U.S, and uses process-guided deep learning to predict specific conductance in rivers in the Delaware River Basin. My previous water resources systems research addressed using Bayesian methods to calibrate a spatially distributed ecohydrological watershed model to inform spatial water quality risk assessments and the subsequent optimization of green infrastructure locations that are robust to parametric model uncertainty. This work was applied to a Baltimore Ecosystem Study watershed in the larger Chesapeake Bay watershed. My previous geothermal energy systems research has addressed exploratory spatial data analysis for geothermal datasets, stochastic methods for low-temperature geothermal resource assessment models, and stochastic methods for probabilistic assessment of geothermal reservoir models. These methods were applied in several research projects that evaluated the feasibility of geothermal direct-use heat utilization systems in the Appalachian Basin.