全球暖化與氣候變遷不斷加劇，導致極端降雨之強度與頻率日益嚴重，更造成淹水事件頻傳，為儘早得知淹水風險的嚴重程度，降雨監視、水情即時監控系統及淹水預報扮演著至關重要的角色，目前既有的淹水資訊預測主要可分為兩種方法，包含水文模型及數據驅動模型，但預測之資訊仍不夠充足，如政府所裝設之淹水感測器只可監控即時淹水資訊，而無法掌握其未來淹水之變化量，若能獲得該資訊，則可提供災害防救及應變之參考。
有鑑於此，本研究旨在運用深度學習結合注意力機制建立預測未來時刻之淹水感測器的深度。由兩階段預測模型組成，第一部分以分類型之隨機森林（Random forest）建構淹水事件偵測模型；第二部分以時空注意力機制結合具轉換閥之長短期記憶（Transformation-gated LSTM）建置未來三小時淹水預測模型。與機器學習基線模型相比，本研究於預測未來三小時的RMSE損失為最低值，而預測第一小時（T+1）準確的淹水感測器達76%，相較於傳統水文模型，本研究使用數據驅動模型具有快速運算的優勢，在數秒內即可得知未來淹水深度之資訊。透過視覺化時空注意力機制中的權重值，以解釋各淹水感測器與各變量之間的對應關係，並可針對不同地理特性之淹水感測器進行深入分析，給予不同層面之解釋能力及參考依據。本研究特點在於不僅考慮模型績效，還可掌握影響各感測器之時空資訊，提供相應的防災訊息。

With global warming and climate change, the intensity and occurrence rate of heavy rainfall events are significantly increasing in many areas, resulting in the potential for flood hazards. Therefore, it is crucial to forecast the future trends of floods to understand the timely risk and control flood effectively. However, current information is not sufficient, e.g., the flood sensors installed by the government can only observe real-time information, it is hard to grasp the sudden change in the amount of flood depth values. Nonetheless, given the abovementioned information, the future flood can be known and provided to the related decision maker to enhance decision quality.
Focusing on the realistic needs, this study constructs a method to forecast the flood depth of flood sensors. The description of each stage is illustrated as follows: First, based on the information provided by the precipitation data, a classifier model based on Random Forest algorithm is built to detect whether the flood sensor will be flooded or not. Second, a regression prediction model based on Transformation-gated LSTM and attention mechanism is deployed, including using real-time and forecasting rainfall information to forecast the flood depth values for the flood sensors. Compare with traditional hydrological models, this study applies data-driven models which is less time-consuming. By visualizing the attention weights, we can gain insight into the relationship between the input features and the model. This information can be interpreted and derived to a decision strategy, the potential risks of floods can be mitigated, leading to a reduction in the extent of flooding damage.

國家災害防救科技中心. 民生示警公開資料平台 https://alerts.ncdr.nat.gov.tw/calamityAlertSearch_history.aspx

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