In response to increasing flood risks driven by the climate crisis, urban areas require advanced forecasting and informed decision-making to support sustainable development. This study seeks to improve the reliability of reservoir-based flood forecasting and ensure adequate lead time for effective response measures. The main objectives are to predict hourly downstream flood discharge at a reference point, compare discharge predictions from a single reservoir with a four-hour lead time against those from three reservoirs with a seven-hour lead time, and evaluate the accuracy of data-driven approaches. The study takes place in the Han River Basin, located in Seoul, South Korea. Approaches include two non-deep learning (NDL) (random forest (RF), support vector regression (SVR)) and two deep learning (DL) (long short-term memory (LSTM), gated recurrent unit (GRU)). Scenario 1 incorporates data from three reservoirs, while Scenario 2 focuses solely on Paldang reservoir. Results show that RF performed 4.03% (in R²) better than SVR, while GRU performed 4.69% (in R²) better than LSTM in Scenario 1. In Scenario 2, none of the models showed any outstanding performance. Based on these findings, we propose a two-step reservoir-based approach: Initial predictions should utilize models for three upstream reservoirs with long lead time, while closer to the event, the model should focus on a single reservoir with more accurate prediction. This work stands as a significant contribution, making accurate and well-timed predictions for the local administrations to issue flood warnings and execute evacuations to mitigate flood damage and casualties in urban areas.

为了应对气候危机导致的日益增加的洪水风险，城市地区需要提前预测和明智的决策，以支持可持续发展。本研究旨在提高基于水库的洪水预测的可靠性，并确保为有效的响应措施提供足够的准备时间。主要目标是预测参考点每小时下游洪水流量，将单个水库的排放预测（提前期为 4 小时）与三个水库（提前期为 7 小时）的排放预测进行比较，并评估数据驱动方法的准确性。该研究在位于韩国首尔的汉江流域进行。方法包括两种非深度学习 （NDL）（随机森林 （RF）、支持向量回归 （SVR））和两种深度学习 （DL）（长短期记忆 （LSTM）、门控循环单元 （GRU））。情景 1 包含来自三个油藏的数据，而情景 2 仅关注 Paldang 油藏。结果显示，在场景 1 中，RF 的性能比 SVR 高 4.03%（在 R² 中），而 GRU 的性能比 LSTM 高 4.69%（在 R² 中）。在场景 2 中，没有一个模型表现出任何出色的性能。基于这些发现，我们提出了一种基于储层的两步方法：初始预测应利用三个提前期较长的上游储层模型，而在接近事件时，模型应侧重于具有更准确预测的单个储层。这项工作做出了重大贡献，为地方政府发布了洪水警报和执行疏散以减轻城市地区的洪水破坏和人员伤亡做出了准确和及时的预测。