即時洪水預報模式為重要且實用的非工程性洪災消減措施，本研究以洪水水位為預報變量，應用支撐向量機及模糊推理模式來發展洪水預報模式，即時預報蘭陽大橋未來一至六小時的洪水水位及其機率分布。
首先採用發展自統計學習理論的支撐向量機來建構定值水位預報模式，以水文反應時間的觀點來考慮輸入變量的階數，並依此建立三種不同架構的水位預報模式進行比較與探討；為克服傳統上以試誤法推估支撐向量機參數的缺點，本研究採用兩階段格網搜尋法配合交互驗證來進行系統化且較徹底的參數優選。經由六場洪水事件的驗證，結果顯示支撐向量機模式均具有良好的水位預報效能，且證實變量階數決定方法的合理性。
接著針對支撐向量機水位預報模式的預報誤差序列，分別建立支撐向量迴歸與線性迴歸二種誤差修正模式來即時更新水位預報值，並藉由誤差序列的統計分析與檢定來探討模式更新修正的效能。
最後本研究採用模糊推理模式來進行水位的機率預報，方法為改進模糊推理過程中的解模糊化步驟，使得模糊推理的輸出結果為機率函數；利用此方法估算水位預報誤差的機率分布，再結合支撐向量機水位預報模式的定值預報水位，則可得到洪水水位的即時機率預報值。經由水位歷線的區間預報及超過警戒水位的洪水案例探討，可實際展示機率水位預報的實用性。

A flood warning system is a crucial non-structural approach for flood mitigation, and the essential part of the system is a real-time flood forecasting model. The flood stage, a more relevant variable than the discharge in practice, is used as the output variable to establish a real-time probabilistic flood forecasting model in this study. The Lan-Yang River was chosen as the study basin, and 18 flood events for model calibration and validation were extracted from collected hourly river stage and rainfall data.
The support vector machine, a novel artificial intelligence-based method developed from statistical learning theory, was adopted to construct a deterministic flood stage forecasting model. The lags of the input variables are determined by applying the hydrological concept of the time of response, and a two-step grid search method is applied to find the optimal parameters, and thus to overcome the difficulties in constructing the learning machine. Two structures of models and a pruned model were developed to perform one- to six-hour-ahead stage forecasts.
Two error correction models were then proposed to update the deterministic stage forecasts. The forecasting and updating performance was assessed by statistical tests pertaining to the forecast error series. The results demonstrate that although the deterministic stage forecasts are satisfactory, the updating method still can enhance the model performance to a small extent.
Finally, the probabilistic flood stage forecasting was performed by applying a fuzzy inference model. This study modified the defuzzification process in order to produce probability distributions pertaining to the forecast errors that quantify the total uncertainty of the forecasting. The probabilistic stage forecasts can then be constructed by adding the error distributions to the deterministic stage forecasts. Consequently, the real-time probabilistic flood stage forecasting can be performed, and its applicability is demonstrated.

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