Wind Wave Prediction by using Autoregressive Integrated Moving Average model : Case Study in Jakarta Bay

Didit Adytia, Alif Rizal Yonanta, Nugrahinggil Subasita

Abstract


Prediction of wind wave is highly needed to support safe navigation, especially for ship. Besides that, loading and unloading activities in a harbour, as well as for design purpose of coastal and offshore structures, data of prediction of wave height are needed. Based on its nature, the wind wave has random behaviour that is highly depending on behaviour of wind as the main driving force. In this paper, we propose a prediction method for wind wave by using Autoregressive Integrated Moving Average or ARIMA. To obtain historical data of wind wave, we perform  wave simulation by using a phase-averaged wave model SWAN (Simulating Wave Near Shore).  From the simulation, time series of wind wave is obtained. The prediction of wind wave is performed to calculate forecast of 24  hours ahead. Here, we perform wind wave prediction in a location in Jakarta Bay, Indonesia. We perform several combination of ARIMA model to obtain best fit model for wind wave prediction in the location in Jakarta Bay. Results of prediction show that ARIMA model give an accurate prediction especially for short term prediction.

Full Text:

PDF

References


Akpınar, A., van Vledder, G. P., Kömürcü, M. ˙I., & Özger, M. (2012). Evaluation of the numerical wave model (swan) for wave simulation in the black sea. Continental Shelf Research, 50, 80–99.

Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.

Cadenas, E., & Rivera, W. (2010). Wind speed forecasting in three different regions of mexico, using a hybrid arima–ann model. Renewable Energy, 35(12), 2732–2738.

Conejo, A. J., Plazas, M. A., Espinola, R., & Molina, A. B. (2005). Day-ahead electricity price forecasting using the wavelet transform and arima models. IEEE transactions on power systems, 20(2), 1035– 1042.

Deka, P. C., & Prahlada, R. (2012). Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time. Ocean Engineering, 43, 32–42.

French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected stock returns and volatility. Journal of financial Economics, 19(1), 3–29.

Huang, T. (2013). The box-jenkins methodology for time series models. America: Addison Wesley Publishing Company Inc.

Kohzadi, N., Boyd, M. S., Kermanshahi, B., & Kaastra, I. (1996). A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing, 10(2), 169– 181.

Mandal, S., & Prabaharan, N. (2010). Ocean wave prediction using numerical and neural network models.

Moeini, M., & Etemad-Shahidi, A. (2007). Application of two numerical models for wave hindcasting in lake erie. Applied Ocean Research, 29(3), 137–145.

Pai, P.-F., & Lin, C.-S. (2005). A hybrid arima and support vector machines model in stock price forecasting. Omega, 33(6), 497–505.

Radziukynas, V., & Klementavicius, A. (2014a). Short-term wind speed forecasting with arima model. In Power and electrical engineering of riga technical university (rtucon), 2014 55th international scientific conference on (pp. 145–149).

Radziukynas, V., & Klementavicius, A. (2014b). Short-term wind speed forecasting with arima model. In Power and electrical engineering of riga technical university (rtucon), 2014 55th international scientific conference on (pp. 145–149).

Salcedo-Sanz, S., Borge, J. N., Carro-Calvo, L., Cuadra, L., Hessner, K., & Alexandre, E. (2015). Significant wave height estimation using svr algorithms and shadowing information from simulated and real measured x-band radar images of the sea surface. Ocean Engineering, 101, 244–253.

Sorensen, R. M. (1993). Basic wave mechanics: for coastal and ocean engineers. John Wiley & Sons.

Sverdrup, H. U. (1947). Wind, sea and swell. theory of relations for forecasting. US Navy Hydrog. Office, Pub., 601, 44002E

Thomas, T. J., & Dwarakish, G. (2015). Numerical wave modelling–a review. Aquatic Procedia, 4, 443–448.

Tseng, F.-M., Tzeng, G.-H., Yu, H.-C., Yuan, B. J., et al. (2001). Fuzzy arima model for forecasting the foreign exchange market. Fuzzy sets and systems, 118(1), 9–19.

Weggel, J. R., & Sorensen, R. M. (1986). Ship wave prediction for port and channel design. In Ports’ 86 (pp. 797–814).

Wei, W. (2006). Time analysis univariate and multivariate methods. America: Addison Wesley Publishing Company Inc.

Weissman, D. (1973). Two frequency radar interferometry applied to the measurement of ocean wave height. IEEE Transactions on Antennas and Propagation, 21(5), 649–656.

Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance. Climate research, 30(1), 79–82.




DOI: http://dx.doi.org/10.21108/IJOICT.2018.42.300

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Didit Adytia, Alif Rizal Yonanta, Nugrahinggil Subasita

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.