How ICT can advance reservoir water level predictions using machine learning techniques

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ADS bibcode: 2018AGUFM.H53P1810K
year: 2018
Listed Authors: Kim, T.; Nam, W. H.; Hong, E. M.; Smith, T. M.; Ha, T. H.; Do, J. W.; Lee, S. I.; Lee, K. Y.
Listed Institutions: University of Minnesota Twin Cities, Minneapolis, MN, United States; Department of Bioresources and Rural Systems Engineering, Hankyong National University, Anseong, South Korea; School of Natural Resources and Environmental Science, Kangwon National University, Chuncheon, South Korea; Institute on the Environment, University of Minnesota Twin Cities, Minneapolis, MN, United States; Korea Rural Community Corporation, Agricultural Drought Mitigation Center, Daejeon, South Korea; Korea Rural Community Corporation, Agricultural Drought Mitigation Center, Daejeon, South Korea; Korea Rural Community Corporation, Agricultural Drought Mitigation Center, Daejeon, South Korea; Korea Rural Community Corporation, Agricultural Drought Mitigation Center, Daejeon, South Korea

How ICT can advance reservoir water level predictions using machine learning techniques

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