連續型降雨逕流模式可模擬長時間尺度之流域水文歷程，對於水資源管理與水文現象探討具有重要應用價值。本研究以石門水庫、翡翠水庫、德基水庫、集集攔河堰、曾文水庫及高屏溪攔河堰六個集水區為研究對象，採用修正型HBV（Hydrologiska Byråns Vattenbalansavdelning）水文模式，將流量依流量延時曲線劃分為11個區間，並建立自適應模糊多目標函數，透過外部控制程序自動最小化各流量區間之誤差，進行HBV水文模式參數之率定。本研究導入長短期記憶網路（long short-term memory, LSTM）模式作為誤差修正工具，建立兩種AI強化型HBV水文模式（AI-enhanced HBV），分別為直接流量校正與模擬殘差修正兩種架構，並與兩種單一模式（HBV水文模式及LSTM模式）進行比較分析。研究結果顯示，自適應模糊多目標函數應用於各集水區之模擬結果，在流量延時曲線及多項評鑑指標上，與手動調整結果皆維持一致表現，顯示該方法可在不降低模擬精度之情況下，有效降低人工調整成本並提升率定效率。在模型比較方面，AI強化型HBV水文模式整體表現優於單一模型，其中以模擬殘差修正架構於多數集水區及評鑑指標上具有最佳表現，顯示其在流量模擬精度與穩定性上具有較顯著優勢；直接流量校正架構則呈現穩定但較保守之改善效果。

Continuous rainfall–runoff models are valuable for simulating long-term watershed hydrological processes and supporting water resources management. This study investigates six watersheds in Taiwan, including Shimen Reservoir, Feitsui Reservoir, Deji Reservoir, Jiji Weir, Zengwen Reservoir, and Gaoping River Weir. A modified HBV hydrological model was adopted, and streamflow was divided into 11 intervals based on the flow duration curve. An adaptive fuzzy multi-objective function was developed to automatically minimize errors in each flow interval through an external control procedure for HBV parameter calibration.
In addition, a long short-term memory (LSTM) network was introduced as an error-correction tool to construct two AI-enhanced HBV models: direct streamflow correction and residual error correction. These models were compared with standalone HBV and LSTM models. Results show that the adaptive fuzzy multi-objective function achieved simulation performance comparable to manual adjustment in terms of flow duration curves and multiple evaluation metrics, while reducing manual calibration effort and improving efficiency. Overall, the AI-enhanced HBV models outperformed the standalone models. The residual error correction framework provided the best performance in most watersheds and metrics, indicating superior accuracy and stability, whereas direct streamflow correction produced stable but more conservative improvements.

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