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  • 學位論文

應用長短期記憶模型建置雨水下水道系統水位預報模式

Long-Short Term Memory Networks for Storm Sewer Water Level Forecasting

指導教授 : 張麗秋

摘要


近年來受到氣候變遷與全球暖化之影響,世界各地發生極端水文事件的頻率增加,對人類生命與財產安全造成極大威脅。臺灣降雨量在空間與時間分布呈現不均勻現象在近年來更趨嚴重,除了受到梅雨鋒面與颱風影響外,各縣市也因短延時強降雨頻繁發生,導致雨水下水道水位驟升溢出路面,短時間內即造成市區局部區域發生積淹水情形,甚至大範圍嚴重淹水災情,救災單位整備與應變時間相對變短。 本研究以臺北市中山抽水站集水區為研究區域,應用長短期記憶模型(LSTM)與倒傳遞類神經網路(BPNN)建置人工智慧下水道水位預報模式,預報中山抽水站內池水位未來10至60分鐘之水位;依照不同輸入因子數量將模式分為兩種子模式,輸入I型(所有雨量站)與輸入II型(部分雨量站),針對雨量站間之距離調整輸入因子,分析兩種不同輸入因子對於LSTM與BPNN模式預測之準確性,並討論降雨與下水道水位變化之關係。 由輸入I型與輸入II型綜合比較結果可得知,調整輸入項之雨量站對於模式訓練上有一定影響,顯示雨量站、下水道水位測站與抽水站有相當關聯性;LSTM模式在輸入I型與輸入II型之結果表現均優於BPNN模式,可證明LSTM模式在學習資料特性上有掌握水位關係,對於預測抽水站內池水位未來10至60分鐘有較佳之預測結果,有助於輔助抽水站操作人員在暴雨時期參考並做出即時且準確之決策,亦可作為抽水機啟閉之依據,漸少專業人員在判斷上的壓力。

並列摘要


In recent years, the occurrence of extreme hydrological events is more frequent, due to climate change and global warming, posing a great threat to human life and property security. The uneven distribution of rainfall in space and time in Taiwan has become more serious in recent years. In addition to the impact of plum rain(mei-yu) and typhoon, short duration high intensity storms also could flood counties and cities, the water overflowed from the sewer on to the road surface in a short period of time, emergency response agencies’ response time is insufficient. This study took the Zhongshan Pumping Station watershed in Taipei City as the research area. The artificial intelligence sewer water level forecasting model was constructed using the long-short term memory model (LSTM) and back-propagation neural network (BPNN), forecasting the water levels of the front storage pool in Zhongshan pumping station for the next 10 to 60 minutes (T+1~T+6). The model is divided into two sub-models, the first submodel uses all rain stations as inputs (type I) and second submodel selects partial rain station (type II). This study analyzed the forecast result of LSTM and BPNN models with two different input factors, and discussed the relationship between rainfall and sewer water level. In comparison with the results of type I, type II showed that there is a considerable correlation between rain station, sewer station and pumping station, adjusting model input has an effect on model training. In conclusion, this study proved that the LSTM model has get the hang of data property in training model, the results also demonstrate that the LSTM model is more accurately forecast water levels of the front storage pool in type I and type II than BPNN model. The proposed methodology can provide water level information to decision-makers and residents for taking precautionary measures against flooding.

參考文獻


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