Paper Title :Nonlinear Autoregressive Neural Network Analysis of Global Mean Absolute Sea Level Change, 1880 – 2014
Author :Yeong Nain Chi
Article Citation :Yeong Nain Chi ,
(2023 ) " Nonlinear Autoregressive Neural Network Analysis of Global Mean Absolute Sea Level Change, 1880 – 2014 " ,
International Journal of Soft Computing And Artificial Intelligence (IJSCAI) ,
pp. 1-6,
Volume-11,Issue-2
Abstract : This study tried to pursue analysis of time series data using long-term records of global mean absolute sea level
change from 1880 to 2014. Using the LM algorithm, the results revealed that the nonlinear autoregressive neural network
model with 7 neurons in the hidden layer and 7 time delays provided the best performance in the nonlinear autoregressive
neural network models at its smaller MSE value. The findings in this study may be able to bridge an important gap in time
series forecasting by combining the best statistical and machine learning methods. In order to sustain these observations,
research programs utilizing the resulting data should be able to significantly improve our understanding and narrow
projections of future sea level rise and variability.
Keywords- Global Mean Absolute Sea Level Change, Time Series, Nonlinear Autoregressive Neural Network Model,
Levenberg-Marquardt Algorithm, Bayesian Regularization Algorithm, Scaled Conjugate Gradient Algorithm.
Type : Research paper
Published : Volume-11,Issue-2
DOIONLINE NO - IJSCAI-IRAJ-DOIONLINE-20101
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Copyright: © Institute of Research and Journals
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Published on 2023-11-27 |
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