支撐向量迴歸及高階頻譜分析於流量預測之研究

在水資源運用、河川治理與防洪決策問題方面，如何快速建立準確且穩定之流量預測模式、快速準確決定稽延時間以及流量資料缺漏之推衍，一直以來都是研究的重點。為達前述目標，本研究以高效能的支撐向量迴歸及高階頻譜分析方法，發展月、旬及小時等三種不同時間尺度之流量預測模式。另外，以逕流係數、水位–流量率定曲線及HEC-HMS模式對颱洪流量歷線進行修正及模擬。首先採用高階頻譜分析方法中三階積累的原理，藉奇異值分解及統計假設檢定三階積累漸進方差的方法，可快速與準確求得自迴歸移動平均模式之最大階數，此方法可克服傳統上以自相關函數及部分自相關函數判別階數繁複之過程與不確定性之缺點。接著利用高階頻譜分析決定之階數，作為預測模式稽延時間，並應用支撐向量迴歸方法，建立流量預測模式。而為克服傳統上以試誤法推估支撐向量迴歸參數之缺點，本研究以模擬退火演算法搜尋最佳參數，結果顯示模擬退火演算法結合支撐向量迴歸建立之模式，具有良好的流量預測效果，且證實高階頻譜分析決定之階數，作為稽延時間的快速性與合理性。最後，本研究建構之流量預測模式，成功地應用於荖濃溪新發大橋集水區、石門水庫集水區及旗山溪集水區。另颱洪流量資料低估及缺漏部分，則改進傳統以水位–流量率定曲線外插得颱洪流量不確定性之缺點，藉由逕流係數修正颱洪流量歷線，以符合實際之水理現象，並驗證流量修正之適當性。本研究有別於傳統以歷史流量為單一輸入因子，且假設資料為常態分布之方式。主要係採用支撐向量迴歸、高階頻譜分析、逕流係數及HEC-HMS模式分別結合之方法，建構流量預測模式。證實發展之流量預測模式，具實際應用價值，其結果可提供發電廠取水決策、水庫操作及水資源長期管理、防洪決策及河川治理計畫擬定之參考。

關鍵字

支撐向量迴歸；高階頻譜分析；預測模式；率定曲線；模擬退火

並列摘要

In this study, high efficient support vector regression (SVR) and higher order spectral analysis (HOSA) for developing streamflow prediction models. Furthermore, runoff coefficient, stage-discharge rating curve and HEC-HMS are also utilized to simulate and adjust storm hydrograph. First of all, the principle of third-order cumulants is introduced. The largest order of the autoregressive moving average (ARMA) model can be rapidly and accurately solved using singular value decomposition and hypothesis testing. This method could overcome complex calculations and errors resulted by determining orders using autocorrelation function and partial autocorrelation function. Secondly, establish a lag time using the determined order and a streamflow prediction model using SVR. To avoid drawbacks of SVR estimation using trial-and-error method, simulated annealing (SA) is utilized to seek out the optimal parameters. As results indicated SA, coupled with SVR, predict streamflow effectively, and prove the advantages of calculating orders using HOSA. Lastly, the streamflow prediction model developed from this study has been successfully applied to three actual watersheds in Taiwan. Meanwhile to deal with underestimate and missing data, traditional stage-discharge rating curve method has been improved by adjusting storm discharge using runoff coefficient, so as to represent the actual hydrological state and examine the reliability of streamflow adjustment. The results are proven to be realistic and can be utilized as a reference for water resources policy, flood prevention and decision-making.

並列關鍵字

support vector regression ； higher order spectral analysis ； prediction model ； rating curve ； simulated annealing

參考文獻

8. Asefa, T., Kemblowski, M., McKee, M., and Khalil, A. (2006), “Multi-time Scale Stream Flow Predictions: The Support Vector Machines Approach,” Journal of Hydrology, 318(1-4), 7-16.

9. Box, G. E. P., and Jenkins, G. M. (1976), “Time Series Analysis: Forecasting and Control,” Holden-Day, San Francisco.

13. Feldman, A. D. (2000), “Hydrologic Modeling System HEC-HMS Technical Reference Manual,” Davis, California.

15. Giannakis, G. B., and Mendel, J. M. (1990), “Cummulant-based Order Determination of Non-Gaussian ARMA Models,” IEEE Transactions on Acoustics, Speech, & Signal Processing, 38(8), 1411-1423.

16. Guo, J., Zhou, J., Qin, H., Zou, Q., and Li, Q. (2011), “Monthly Streamflow Forecasting Based on Improved Support Vector Machine Model,” Expert Systems with Applications, 38(10), 13073-13081.

國際替代計量

支撐向量迴歸及高階頻譜分析於流量預測之研究

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