Abstract
In many practical field problems, it may not be possible to identify the actual characteristics (location, magnitude and duration of contamination) of the groundwater contaminant sources in a contaminated aquifer. Also, most of the time, very sparse information regarding spatiotemporal contaminant concentration is available initially, which is inadequate for reliable identification and simulation of the contaminant plume. Simulation of the contaminant plume movement is necessary to predict the future distribution of the contaminant in the groundwater aquifer. Reliable simulation and prediction are also essential for developing an efficient contamination monitoring strategy. To address this practical problem of data inadequacy, an interactive methodology is proposed, incorporating the sequential design of optimal monitoring networks. These sequentially developed and implemented monitoring networks provide feedback information on measured concentrations. This measurement information helps in progressively improving the prediction of the contaminant plume, starting with very sparse initial information about the contaminant sources and spatial distribution of concentration. The proposed methodology is based on an optimization model that utilizes feedback information obtained from sequentially designed contaminant monitoring sites to sequentially characterize the contaminant plume when adequate initial concentration measurements are not available, and the contaminant sources are unknown.
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Singh, D., Datta, B. (2021). Sequential Characterization of Contaminant Plumes Using Feedback Information. In: Chauhan, M.S., Ojha, C.S.P. (eds) The Ganga River Basin: A Hydrometeorological Approach. Society of Earth Scientists Series. Springer, Cham. https://doi.org/10.1007/978-3-030-60869-9_2
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