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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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