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      A Comparative Evaluation of Artificial Neural Network and Sunshine Based models in prediction of Daily Global Solar Radiation of Lalibela, Ethiopia

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          Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation

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            Solar radiation prediction using Artificial Neural Network techniques: A review

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              An ANN application for water quality forecasting.

              Rapid urban and coastal developments often witness deterioration of regional seawater quality. As part of the management process, it is important to assess the baseline characteristics of the marine environment so that sustainable development can be pursued. In this study, artificial neural networks (ANNs) were used to predict and forecast quantitative characteristics of water bodies. The true power and advantage of this method lie in its ability to (1) represent both linear and non-linear relationships and (2) learn these relationships directly from the data being modeled. The study focuses on Singapore coastal waters. The ANN model is built for quick assessment and forecasting of selected water quality variables at any location in the domain of interest. Respective variables measured at other locations serve as the input parameters. The variables of interest are salinity, temperature, dissolved oxygen, and chlorophyll-alpha. A time lag up to 2Delta(t) appeared to suffice to yield good simulation results. To validate the performance of the trained ANN, it was applied to an unseen data set from a station in the region. The results show the ANN's great potential to simulate water quality variables. Simulation accuracy, measured in the Nash-Sutcliffe coefficient of efficiency (R(2)), ranged from 0.8 to 0.9 for the training and overfitting test data. Thus, a trained ANN model may potentially provide simulated values for desired locations at which measured data are unavailable yet required for water quality models.
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                Author and article information

                Journal
                Cogent Engineering
                Cogent Engineering
                Informa UK Limited
                2331-1916
                December 31 2022
                December 27 2021
                December 31 2022
                : 9
                : 1
                Affiliations
                [1 ]Department of Physics, College of Natural Science, Wollo University, Dessie, Ethiopia
                [2 ]Department of Physics, College of Natural and Computational Science, Wolaita University, Sodo, Ethiopia
                [3 ]School of Engineering, Edith Cowan University, Perth, AUSTRALIA
                Article
                10.1080/23311916.2021.1996871
                3ecd1d6f-40da-4290-bb41-c18ee23c8871
                © 2022

                http://creativecommons.org/licenses/by/4.0/

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