The Prediction of Nonlinear Polar Motion Based on Artificial Neural Network (ANN) and Fuzzy Inference System (FIS)

Date

2018-01-01

Author

Kuçak, Ramazan Alper
Uluğ, Raşit
Akyılmaz, Orhan

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The Earth rotation movement characterizes the situation of the whole Earth movement, as well as the interaction between the Earth’s various layers such as the Earth’s core, mantle, crust, and atmosphere. Prediction of the Earth rotation parameters (ERPs) is important for near real-time applications including navigation, precise positioning, remote sensing and landslide monitoring, etc. In such studies, the analysis of time series is also important for highly accurate and reliable predictions. Therefore, prediction of ERPs at least over a few days in the future is necessary. At present, there are two major forecasting methods for ERP: linear and nonlinear models. The nonlinear models include: sequence of artificial neural network (ANN), fuzzy inference system, and other methods. Fuzzy inference system (FIS) and traditional artificial neural networks (ANN) provide good predictions of polar motion (PM). In this study, for the prediction of Earth rotation parameters, International Earth Rotation and Reference System Service (IERS) C04 daily time series data from 1990 to 2015 was used for training. From 1 to 120 days in future of ERPs values were predicted by using the data of 5, 15, and 25 years in ANN. The results of ANN and ANFIS were compared with observed values. The results indicate that the longer training data are used in ANN and ANFIS, the more accurate prediction can be obtained.

Subject Keywords

Polar motion, Artificial neural network, Fuzzy inference system, Earth rotation parameters

URI

http://www.springer.com/gp/book/9783319588940
https://hdl.handle.net/11511/95042

Relation

Advances in Nonlinear Geosciences

Collections

Graduate School of Natural and Applied Sciences, Book / Book chapter

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Citation Formats

R. A. Kuçak, R. Uluğ, and O. Akyılmaz, The Prediction of Nonlinear Polar Motion Based on Artificial Neural Network (ANN) and Fuzzy Inference System (FIS). 2018.