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Generalized regression neural network (GRNN)-based approach for colored dissolved organic matter (CDOM) retrieval: case study of Connecticut River at Middle Haddam Station, USA

Environmental Monitoring and Assessment

Springer Nature America, Inc

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      Most cited references 42

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      Light and photosynthesis in aquatic ecosystems

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        Some Comments on the Evaluation of Model Performance

         Cort Willmott (1982)
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          A general regression neural network.

           D.F. Specht (1991)
          A memory-based network that provides estimates of continuous variables and converges to the underlying (linear or nonlinear) regression surface is described. The general regression neural network (GRNN) is a one-pass learning algorithm with a highly parallel structure. It is shown that, even with sparse data in a multidimensional measurement space, the algorithm provides smooth transitions from one observed value to another. The algorithmic form can be used for any regression problem in which an assumption of linearity is not justified.
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            Author and article information

            Journal
            Environmental Monitoring and Assessment
            Environ Monit Assess
            Springer Nature America, Inc
            0167-6369
            1573-2959
            November 2014
            August 12 2014
            November 2014
            : 186
            : 11
            : 7837-7848
            10.1007/s10661-014-3971-7
            © 2014
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