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Forecasting of Seasonal Inflow to Reservoir Using Multiple Linear Regression

다중선형회귀분석에 의한 계절별 저수지 유입량 예측

  • Kang, Jaewon (International Water Resources Research Institute, Chungnam National University)
  • 강재원 (충남대학교 국제수자원연구소)
  • Received : 2012.12.28
  • Accepted : 2013.04.17
  • Published : 2013.08.31

Abstract

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.

Keywords

References

  1. Ahn, J. B., Park, J. Y., 2000, A Study on a Statistical Long-term Prediction Model Using Global Sea-Surface Temperature Anomalies, Asia-Pacific Journal of Atmospheric Sciences, 36(2), pp. 179-188.
  2. Awadallah, A.G., Rousselle, J., 2000, Improving Forecasts of Nile Flood Using SST Inputs in TFN Model, Journal of Hydrologic Engineering, 5(4), pp. 371-379.
  3. Berri, G.J., Flamenco, E.A., 1999, Seasonal Volume Forecast of the Diamante River, Argentina, Based on El Nino Observations and Predictions, Water Resources Research, 35(12), pp. 3803-3810. https://doi.org/10.1029/1999WR900260
  4. Chiew, F.H.S., Piechota, T.C., Dracup, J.A., McMaho, T.A., 1998, El Nino/Southern Oscillation and Australian Rainfall, Streamflow and Drought: Links and Potential for Forecasting, Journal of Hydrology, 204, pp. 138-149. https://doi.org/10.1016/S0022-1694(97)00121-2
  5. Climate Prediction Center: http://www.cpc.ncep.noaa.gov/
  6. Cover, T.M., Thomas, J.A., 1991, Elements of Information Theory, John Wiley & Sons, Inc.
  7. Draper, N.R., Smith, H., 1998, Applied Regression Analysis, 3rd Edition, John Wiley & Sons.
  8. Efron, B., Tibshirani, R.J., 1993, An Introduction to the Bootstrap, Chapman & Hall.
  9. Hipel, K.W., McLeod, A.I., 1994, Time Series Modelling of Water Resources and Environmental Systems, Elservier.
  10. Hocking, R.R., 1996, Methods and Applications of Linear Models: Regression and the Analysis of Variance, John Wiley & Sons.
  11. Liu, Z., Valdes, J.B., Entekhabi, D., 1998, Merging and Error Analysis of Regional Hydrometeorologic Anomaly Forecast Conditioned on Climatic Precursors, Water Resources Research, 34(8), pp. 1959-1969. https://doi.org/10.1029/98WR01376
  12. Piechota, T.C., Chiew, F.H.S., Dracup, J.A., McMahon, T.A., 1998, Seasonal Streamflow Forecasting in Eastern Australia and the El Nino-Southern Oscillation, Water Resources Research, 34(11), pp. 3035-3044. https://doi.org/10.1029/98WR02406
  13. Piechota, T.C., Chiew, F.H.S., Dracup, J.A., McMahon, T.A., 2001, Development of Exceedance Probability Streamflow Forecast, Journal of Hydrologic Engineering, 6(1), pp. 20-28. https://doi.org/10.1061/(ASCE)1084-0699(2001)6:1(20)
  14. Sharma, A., 2000, Seasonal to Interannual Rainfall Probabilistic Forecasts for Improved Water Supply Management: Part 1 - A Strategy for System Predictor Identification, Journal of Hydrology, 239, pp. 232-239. https://doi.org/10.1016/S0022-1694(00)00346-2
  15. Sharma, A. Luk, K.C., Cordery, I., Lall, U., 2000, Seasonal to Interannual Rainfall Probabilistic Forecasts for Improved Water Supply Management: Part 2 - Predictor Identification of Quarterly Rainfall Using Ocean-Atmosphere Information, Journal of Hydrology, 239, pp. 232-239. https://doi.org/10.1016/S0022-1694(00)00346-2
  16. Simpson, H.J., Cane, M.A., Herczeg, A.L., Zebiak, S.E., Simpson, J.H., 1993, Annual River Discharge in Southeastern Australia Related to El Nino-Southern Oscillation Forecasts of Sea Surface Temperatures, Water Resources Research, 34(11), pp. 3035-3044.
  17. Simpson, H.J., Colodner, D.C., 1999, Arizona Precipitation Response to the Southern Oscillation: A Potential Water Management Tool, Water Resources Research, 35(12), pp. 3761-3769. https://doi.org/10.1029/1999WR900199
  18. Uvo, C.B., Graham, N.E., 1998, Seasonal Runoff Forecast for Northern South America: A Statistical Model, Water Resources Research, 34(12), pp. 3515-3524. https://doi.org/10.1029/98WR02854
  19. Wei, W.W.S., 1990, Time Series Analysis: Univariate and Multivariate Methods, Addison-Wesley.
  20. Wilks, D.S., 1995, Statistical Methods in the Atmospheric Sciences: An Introduction, Academic Press.

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