On Integrating Groundwater Transport Models with Wireless Sensor Networks |
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| Authors: | Kevin Barnhart Iñigo Urteaga Qi Han Anura Jayasumana Tissa Illangasekare |
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| Institution: | 1. Department of Mathematical and Computer Sciences, Colorado School of Mines, 1500 Illinois St., Golden, CO 80401.;2. Department of Electrical and Computer Engineering, Colorado State University, Engineering Room B104, 1373 Campus Delivery, Fort Collins, CO 80523.;3. Center for Experimental Study of Subsurface Environmental Processes, Division of Environmental Sciences and Engineering, Colorado School of Mines, 1500 Illinois St., Golden, CO 80401. |
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| Abstract: | The emerging technology of wireless sensor networks (WSNs) is an integrated, distributed, wireless network of sensing devices. It has the potential to monitor dynamic hydrological and environmental processes more effectively than traditional monitoring and data acquisition techniques by providing environmental information at greater spatial and temporal resolutions. Furthermore, due to continuing high-performance computing development, these data may be introduced into increasingly robust and complex numerical models; for instance, the parameters of subsurface transport simulators may be automatically updated. Early field deployments and laboratory experiments conducted using in situ sensor technology and WSNs indicated significant fundamental issues concerning sensor and network hardware reliability—suggesting that investigations should first be conducted in controlled environments before field deployment. A first step in this validation process involves evaluating the predictive capability of a computational advection-dispersion transport model when incorporating concentration data from a WSN simulation. Data quality is a major concern, especially when sensor readings are automatically fed into data assimilation procedures. The appropriate employment of an independent WSN fault detection service can ensure that erroneous data (e.g., missing or anomalous values) do not mislead the model. Parameter estimation regularization techniques may then deal with remaining data noise. The primary purpose of this study is to determine the suitability of WSNs (and other in situ data delivery technologies) for use in contaminant transport modeling applications by conducting research in a realistic simulative environment. |
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