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      From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?

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          Long Short-Term Memory

          Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
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            Standard Methods for the Examination of Water and Wastewater

            "The Twenty-First Edition has continued the trend to revise methods as issues are identified and contains further refined quality assurance requirements in a number of Parts [sic] and new data on precision and bias. New methods have been added in Parts 2000, 4000, 5000, 6000, 7000, 8000, and 9000, and numerous methods have been revised. Details of these changes appear on the reverse of the title page for each part."--Pref. p. iv.
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              Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations

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                Author and article information

                Contributors
                Journal
                Environmental Science & Technology
                Environ. Sci. Technol.
                American Chemical Society (ACS)
                0013-936X
                1520-5851
                February 16 2021
                February 03 2021
                February 16 2021
                : 55
                : 4
                : 2357-2368
                Affiliations
                [1 ]Department of Civil and Environmental Engineering, The Pennsylvania State University, State College, Pennsylvania 16802, United States
                [2 ]Department of Natural Resources & Environmental Science, The University of Nevada, Reno, Nevada 89557, United States
                Article
                10.1021/acs.est.0c06783
                33533608
                38a9f2ec-4106-44ba-949d-6e8985fa7357
                © 2021
                History

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