本研究旨在發展權重型WRF系集預報模式，該模式為應用空間遙測雨量估計資訊於WRF系集預報以期能提升其預報能力，而後再以地面雨量資訊修正預報結果以發展研究區域之颱風時雨量預報。
本研究所使用之空間遙測雨量估計資訊為雷達雨量估計資訊QPESUMS及衛星遙測雨量估計資訊PERSIANN-CCS，首先對兩者於台灣本島及其周邊海域進行相似度分析評估，相似度指標結果發現:PERSIANN-CCS之雨量估計值較QPESUMS為低，但整體可合理估計雨量在空間上之分佈。接續本研究分別應用QPESUMS及PERSIANN-CCS配合各權重法比對WRF系集預報以發展權重型WRF系集預報模式。經權重法評估結果發現:以QPESUMS比對WRF系集預報並配合排序倒數法所發展之權重型WRF系集預報模式之表現結果最佳，但各方法間之結果差異不大，且其預報雨量仍有低估之情形。最後，本研究分別以隨機森林及支撐向量機建立誤差修正模式以改善雨量預報結果，修正結果顯示:隨機森林修正能力較支撐向量機為佳，可提升前置時間1至2小時之預報能力，且整體可改善雨量低估之情形；但在前置時間3至6小時則會降低預報能力且有高估低雨量之缺點。

This study aims at developing a weighted WRF ensemble model (W-WRFEM) based on remote sensing precipitation information to improve hourly typhoon rainfall forecasting by the WRF ensemble model. Based on gauged precipitation information, the W-WRFEM is further coupled with two error correction models developed by Random Forest (RF) and Support Vector Machine (SVM), respectively, to increase the forecasting accuracy of W-WRFEM. The result indicates that (1) the W-WRFEM based on QPESUMS radar rainfall with the weighting method, Rank Reciprocal Method, performs better than the other weighting methods; (2) the W-WRFEM coupled with the error correction model by RF has a better performance than the error correction model by SVM and has the ability to improve 1-hour- and 2-hour-ahead rainfall forecasting.

江衍銘、張斐章。(2006)。「以遙測資訊建立類神經網路雲分類系統於雨量推估及洪水預測」。農業工程學報。
張斐章、蔡孟蓉、江衍銘及謝明昌。(2013)。「整合遙測資訊於山區雨量推估」。航測及遙測學刊。
蔡孟蓉、江衍銘及張斐章。(2007)。「類神經網路結合衛星影像於颱風降雨預報」。台灣水利。
李明軒。(2008)。「支撐向量機與模糊理論於流量預報即時誤差修正之研究」。國立成功大學水利及海洋工程學系碩士論文。
陳憲宗。(2006)。「支撐向量機及模糊推理模式應用於洪水水位之即時機率預報」。國立成功大學水利及海洋工程學系博士論文。
劉鑌鋈。(2009)。「利用機器學習修正QPESUMS雷達估計降雨」。國立成功大學水利及海洋工程學系碩士論文。
鐘柏顯。(2013)。「探討乾溼日移轉機率在氣候變遷下對水文特性之影響」。國立成功大學水利及海洋工程學系碩士論文。
經濟部水利署。(2011)。「沿海低地排水系統淹水預警模式之研究-二仁溪以南至林邊溪(1/2)」。
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
Byeong Seok, A. (2011). Compatible Weighting Method with Rank Order Centroid: Maximum Entropy Ordered Weighted Averaging Approach. European Journal of Operational Research, 212(3), 552-559.
Casanova, S., & Ahrens, B. (2009). On the Weighting of Multimodel Ensembles in Seasonal and Short-Range Weather Forecasting. Monthly Weather Review, 137(11), 3811-3822.
Fan, H. (2013). Land-Cover Mapping in the Nujiang Grand Canyon: Integrating Spectral, Textural, and Topographic Data in a Random Forest Classifier. International Journal of Remote Sensing, 34(21), 7545-7567.
Fletcher, R. (1986). Practical Methods of Optimization: Unconstrained Optimization: J. Wiley.
Genuer, R., Poggi, J. M., & Tuleau-Malot, C. (2010). Variable Selection Using Random Forests. Pattern Recognition Letters, 31(14), 2225-2236.
Grecu, M., & Krajewski, W. F. (2000). A Large-Sample Investigation of Statistical Procedures for Radar-Based Short-Term Quantitative Precipitation Forecasting. Journal of Hydrology, 239(1-4), 69-84.
Hogan, R. J., O'Connor, E. J., & Illingworth, A. J. (2009). Verification of Cloud-Fraction Forecasts. Quarterly Journal of the Royal Meteorological Society, 135(643), 1494-1511.
Hong, W. C., & Pai, P. F. (2007). Potential Assessment of the Support Vector Regression Technique in Rainfall Forecasting. Water Resources Management, 21(2), 495-513.
Hong, Y., Hsu, K. L., Sorooshian, S., & Gao, X. G. (2004). Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System. Journal of Applied Meteorology, 43(12), 1834-1852.
Hsiao, L. F., Yang, M. J., Lee, C. S., Kuo, H. C., Shih, D. S., Tsai, C. C., et al. (2013). Ensemble Forecasting of Typhoon Rainfall and Floods over a Mountainous Watershed in Taiwan. Journal of Hydrology, 506, 55-68.
Hsu, K. L., Gao, X. G., Sorooshian, S., & Gupta, H. V. (1997). Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks. Journal of Applied Meteorology, 36(9), 1176-1190.
Kulkarni, M. A., Acharya, N., Kar, S. C., Mohanty, U. C., Tippett, M. K., Robertson, A. W., et al. (2012). Probabilistic Prediction of Indian Summer Monsoon Rainfall Using Global Climate Models. Theoretical and Applied Climatology, 107(3-4), 441-450.
Lee, C. S., Ho, H. Y., Lee, K. T., Wang, Y. C., Guo, W. D., Chen, D. Y. C., et al. (2013). Assessment of Sewer Flooding Model Based on Ensemble Quantitative Precipitation Forecast. Journal of Hydrology, 506, 101-113.
Lin, G. F., Chen, G. R., Huang, P. Y., & Chou, Y. C. (2009). Support Vector Machine-Based Models for Hourly Reservoir Inflow Forecasting During Typhoon-Warning Periods. Journal of Hydrology, 372(1-4), 17-29.
Lin, G. F., Chen, G. R., Wu, M. C., & Chou, Y. C. (2009). Effective Forecasting of Hourly Typhoon Rainfall Using Support Vector Machines. Water Resources Research, 45.
Lin, G. F., & Chen, L. H. (2005). Application of an Artificial Neural Network to Typhoon Rainfall Forecasting. Hydrological Processes, 19(9), 1825-1837.
Lin, G. F., Jhong, B. C., & Chang, C. C. (2013). Development of an Effective Data-Driven Model for Hourly Typhoon Rainfall Forecasting. Journal of Hydrology, 495, 52-63.
Liong, S. Y., & Sivapragasam, C. (2002). Flood Stage Forecasting with Support Vector Machines. Journal of the American Water Resources Association, 38(1), 173-186.
Monira, S. S., Faisal, Z. M., & Hirose, H. (2010). Comparison of Artificially Intelligent Methods in Short Term Rainfall Forecast. Proceedings of the 13th International Conference on Computer and Information Technology (ICCIT 2010), 39-44.
Pal, M. (2003). Random Forests for Land Cover Classification. IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 3510-3512 vol.3516.
Palmer, D. S., O'Boyle, N. M., Glen, R. C., & Mitchell, J. B. O. (2007). Random Forest Models to Predict Aqueous Solubility. Journal of Chemical Information and Modeling, 47(1), 150-158.
Peters, J., De Baets, B., Verhoest, N. E. C., Samson, R., Degroeve, S., De Becker, P., et al. (2007). Random Forests as a Tool for Ecohydrological Distribution Modelling. Ecological Modelling, 207(2-4), 304-318.
Shin, D. W., Cocke, S., & Larow, T. E. (2003). Ensemble Configurations for Typhoon Precipitation Forecasts. Journal of the Meteorological Society of Japan, 81(4), 679-696.
Skamarock, W. C., Klemp, J. B., Dudhia, J. D., Gill, O., Barker, D. M., Wang, W., et al. (2005). A Description of the Advanced Research Wrf Version 2. NCAR Tech, Note TN-468_STR.
Sorooshian, S., Hsu, K. L., Gao, X., Gupta, H. V., Imam, B., & Braithwaite, D. (2000). Evaluation of Persiann System Satellite-Based Estimates of Tropical Rainfall. Bulletin of the American Meteorological Society, 81(9), 2035-2046.
Stefanova, L., & Krishnamurti, T. N. (2002). Interpretation of Seasonal Climate Forecast Using Brier Skill Score, the Florida State University Superensemble, and the Amip-I Dataset. Journal of Climate, 15(5), 537-544.
Vapnik, V. N. (1995). The Nature of Statistical Learning Theory: Springer-Verlag New York, Inc.
Yeh, T. C. (2002). Typhoon Rainfall over Taiwan Area: The Empirical Orthogonal Function Modes and Their Applications on the Rainfall Forecasting. Terrestrial Atmospheric and Oceanic Sciences, 13(4), 449-468.