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Transfer Function Model Forecasting of Sea Surface Temperature at Yeosu in Korean Coastal Waters

전이함수모형에 의한 여수연안 표면수온 예측

  • 성기탁 (국립수산과학원 남서해수산연구소) ;
  • 최양호 (국립수산과학원 남서해수산연구소) ;
  • 구준호 (국립수산과학원 남서해수산연구소) ;
  • 이미진 (국립수산과학원 남서해수산연구소)
  • Received : 2014.10.01
  • Accepted : 2014.10.28
  • Published : 2014.10.31

Abstract

In this study, single-input transfer function model is applied to forecast monthly mean sea surface temperature(SST) in 2010 at Yeosu in Korean coastal waters. As input series, monthly mean air temperature series for ten years(2000-2009) at Yeosu in Korea is used, and Monthly mean SST at Yeosu station in Korean coastal waters is used as output series(the same period of input). To build transfer function model, first, input time series is prewhitened, and then cross-correlation functions between prewhitened input and output series are determined. The cross-correlation functions have just two significant values at time lag at 0 and 1. The lag between input and output series, the order of denominator and the order of numerator of transfer function, (b, r, s) are identified as (0, 1, 0). The selected transfer function model shows that there does not exist the lag between monthly mean air temperature and monthly mean SST, and that transfer function has a first-order autoregressive component for monthly mean SST, and that noise model was identified as $ARIMA(1,0,1)(2,0,0)_{12}$. The forecasted values by the selected transfer function model are generally $0.3-1.3^{\circ}C$ higher than actual SST in 2010 and have 6.4 % mean absolute percentage error(MAPE). The error is 2 % lower than MAPE by ARIMA model. This implies that transfer function model could be more available than ARIMA model in terms of forecasting performance of SST.

본 연구는 단일 입력 전이함수모형(Single-input transfer function model)을 적용하여 여수연안 2010년의 월평균 표면수온의 예측을 시도하였다. 전이함수모형을 수립하기 위한 입력시계열과 출력시계열은 각각 여수지방의 10년(2000-2009년)간의 월평균 기온자료와 표면수온자료를 이용하였다. 전이함수모형을 수립하기 위하여 입 출력 시계열을 사전백색화하고, 입 출력 시계열간의 각 시차에 대한 교차상관함수를 결정하였다. 교차상관함수는 음의 모든 시차에서 유의한 값을 갖지 않아 기온과 표면수온사이는 일방적 인과관계를 보였다. 또한 교차상관함수의 시차 0과 1에서 유의한 값을 보였다. 이러한 교차상관함수의 특징에 따라 입 출력시계열간 전이함수의 시차와 분모 및 분자의 차수(b, r, s)는 (0, 1, 0)으로 식별되었다. 구축된 전이함수모형에 따르면 기온과 표면수온 사이의 시차는 존재하지 않았다. 여기서 현재의 표면수온은 1개월 전의 표면수온과 선형관계가 있음을 보였으며, 잡음모형은 $ARIMA(1,0,1)(2,0,0)_{12}$로 식별되었다. 전이함수모형에 의한 월평균 표면수온의 예측치는 실측치에 비하여 전반적으로 $0.3-1.3^{\circ}C$ 높은 경향을 보였으며, 6.4 %의 평균절대백분율 오차를 포함하였다. 이러한 결과는 8.3 %의 평균절대백분율오차를 보인 ARIMA 모형에 비하여 향상된 예측성능을 보이는 것이며, 표면수온의 시계열적 예측을 시도할 경우, ARIMA 모형보다 전이함수모형의 적용을 통하여 그 예측성능의 개선 가능성을 기대할 수 있음을 시사하고 있다.

Keywords

References

  1. Box, G. E. P., G. M. Jenkins and G. C. Reinsel(2008), Time series analysis : Forecasting and control, fourth edition, John Wiley & Sons, Hoboken, New Jersey, pp. 473-484.
  2. Choi, Y. M.(1998), Forecasting accuracy of tourism demand : An evaluation of time series methods, Ph.D Thesis, University of Kyonggi, Suwon, Korea, pp. 21-23.
  3. Deser, C., M. A. Alexander, S. P. Xie and A. S. Phillips (2010), Sea surface temperature variability : Patterns and Mechanisms, Annual Review of Marine Science, Vol. 2, pp. 115-143. https://doi.org/10.1146/annurev-marine-120408-151453
  4. Gyles, A. F.(1991), A time-domain transfer function model of Wagner's Law : the case of the United Kingdom economy, Applied Economics, Vol. 23, No. 2, pp. 327-330. https://doi.org/10.1080/00036849100000140
  5. Hussian, M. A., S. Abbas, M. R. K. Ansari and A. Zaffar (2013), Perturbations of modeling and forecast of Karachi coastal region seawater, Proceedings of the Pakistan Academy of Science, Vol. 50, No. 3, pp. 235-245.
  6. Kang, Y. Q.(2000), Warming trend of coastal waters of Korea during recent 60 years(1936-1995), Journal of Fisheries Science and Technology, Vol. 3, No. 3, pp. 173-179.
  7. Kang, Y. Q.(1984), One-dimensional model of the oceanic and continental seasons, Journal of the Korean Meteorological Society, Vol. 20, No. 1, pp. 60-66.
  8. Kang, Y. Q., B. K. Kim and Y. H. Seung(1991), Time series forecasting of the SST in the neighbouring seas of Korea, Yellow sea research, Vol. 4, pp. 1-14.
  9. Kang, Y. Q. and M. S. Jin(1984), Seasonal variation of surface temperature in the neighbouring seas of Korea, Journal of Oceanological Society of Korea, Vol. 19, pp. 31-35.
  10. Karim, R.(2013), Season for forecasting sea surface temperature of the north zone of the Bay of Bengal, Research & Reviews : Journal of Statistics, Vol. 2, Issue 2, pp. 2278-2273.
  11. Kim, S. H.(2013), Transfer function modelling using soil moisture measurements at a steep forest hillslope, Journal of the Environmental Sceiences, Vol. 22, No. 4, pp. 415-424. https://doi.org/10.5322/JESI.2013.22.4.415
  12. Laepple, T., S. Jewson, J. Meaghe, A. O'Shay and J. Penzer (2007), Five year prediction of sea surface temperature in the Tropical Atlantic : a comparison of simple statistical methods, http://arxiv/org/abs/physics/0701162.
  13. Lee, J. H. and C. H. Kim(2013), Long-term variability of sea surface temperature in the East China Sea: A review, Ocean and Polar Research, Vol. 35, No. 2, pp. 171-179. https://doi.org/10.4217/OPR.2013.35.2.171
  14. Lemke, K. A.(1991), Transfer function models of suspended concentration, Water Resources Research, Vol. 27, No. 3, pp. 293-305. https://doi.org/10.1029/90WR01607
  15. Lewis, C. D.(1982), Industrial and business forecasting method, Butterworths, London, p. 42.
  16. Lewis, P. A. W. and B. K. Ray(1997), Modeling longrange dependence, non-linearity, and periodic phenomena in sea surface temperatures using TSMARS, Journal of American Statistical Association, Vol. 92, No. 439, pp 881-893. https://doi.org/10.1080/01621459.1997.10474043
  17. Liu, F. C., J. T. Liu, W. Su and Y. Y. Guo(2009), Time series of coastal sea surface temperature : Simulation and prediction based on seasonal model, Journal of Huaihai Institute of Technology(Natural Sciences Edition), Vol. 15, pp. 3709-3718.
  18. Lohani, A. K. T., N. K. Goel and K. K. S. Bhatia(2011), Comparative study of neural network, fuzzy logic and linear transfer function techniques in daily rainfall-runoff modelling under different input domains, Hydrological Process, Vol. 25, pp. 175-193. https://doi.org/10.1002/hyp.7831
  19. Mateos, V. L., J. A. Garcia, A. Serrano and C. De La(2002), Transfer function modelling of the monthly accumulated rainfall series over the Iberian Peninsula, Atmosfera, Vol. 15, pp. 237-256.
  20. Otok, B. W. and Suhartono(2009), Development of rainfall forecasting model in Indonesia by using ASTAR, transfer function, and ARIMA methods, European Journal of Scientific Research, Vol. 38, No. 3, pp. 386-395.
  21. Seong, K. T., J. D. Hwang, I. S. Han, W. J. Go and Y. S. Suh(2010), Characteristic for long-term trend of temperature in the Korean waters, Journal of the Korean Society of Marine Environment and Safety, Vol. 16, No. 4, pp. 353-360.
  22. Seong, K. T., Y. H. Choi, J. H. Koo and S. B. Jeon (2014), Fluctuations and time series forecasting of sea surface temperature at Yeosu coast in Korea, Journal of the Korea Society for Marine Environment and Energy, Vol. 17, No. 2, pp. 122-130. https://doi.org/10.7846/JKOSMEE.2014.17.2.122
  23. Vandaele, W.(1983), Applied time series and Box-Jenkins models, Academic, San Diego, Calif., pp. 272-281.

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