以深度學習方法建立地下水位預警之風險評估模型

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翁采寧(Tsai-Ning Weng) 查詢紙本館藏

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論文名稱

以深度學習方法建立地下水位預警之風險評估模型
(Development of risk assessment model for groundwater level by wavelet-deep learning approach with smart pumping data)

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摘要(中)

地下水作為人類生活及經濟發展中至關重要的存在，亦是穩定的水資源來源之一，因此面臨水資源短缺時，如何妥善利用地下水，成為一個非常重要的課題。而過往文獻大多以月份作為時間尺度，且通常以該地區過往歷史水位資料作為建模過程中的唯一輸入因子，如此可見目前對於以小尺度資料分析角度出發，且利用多項因子探討並預測地下水水位的研究仍相當缺乏。
故本研究以高雄大寮地區為例，除了降雨、潮汐、溫度及濕度的歷史小時觀測資料外，更加入過往文獻中，難以估計卻深刻地影響著地下水位變動的抽水資料。以及結合小波進行深度學習模型建立：藉由小波特徵萃取方式得到各項因子在時間域下的變動特徵，以及影響地下水位的延遲時間，進而透過遞歸神經網路（recurrent neural networks，RNN）、長短期記憶模型（long short-term memory，LSTM）及門控循環單元神經網絡 (Gate Recurrent Unit, GRU)等深度學習方法，歸納並預測出多項變動因子在不同的時間延遲下對於地下水位的影響，最後以均方根誤差(root mean square error, RMSE)、決定係數(coefficient of determination,R2)等評估係數評估模型是否可信，並在最終 LSTM 模型的結果中得到 RMSE : 0.97、MAE：0.76、MSE：0.95、R2：0.5；表示本研究能以事先瞭解並預測大寮地區可能產生的不同水位變動情形，為地下水位預測提供了一種可行而準確的方法，這將可作為智慧地下水資源管理與風險評估的一個重要參考，達到地下水資源永續利用的目標。

摘要(英)

Groundwater, as a vital existence in human life and economic development, is also one of the stable sources of water resources. Therefore, how to properly utilize groundwater becomes a very important issue when faced with water shortages. However, most of the previous literature uses monthly data as the time scale, and usually uses the historical water level data of the area as the only input factor in the modeling process without considering pumping information and rainfall. This shows that the current studies of small-scale data which is based on the use of multiple factors with hydrological mechanisms to explore and predict the groundwater level is still quite lacking.
Therefore, this study proposed a novel framework combining wavelet analysis and deep learning models called wavelet-deep learning models and taking the Daliao area of Kaohsiung as an example. From the historical hourly observation data during 2017/08/23-2020/01/30, including groundwater level, smart pumping measurement, tidal, and meteorological data. After abstracting important features of each factor with groundwater level by wavelet transform, using deep learning algorithms such as recurrent neural networks (RNN), long short-term memory (LSTM) model and gate recurrent unit (GRU) to summarize and predict the impact of multiple variable factors on the groundwater level under different time lags. The results of hourly prediction show that the performance of the LSTM model, GRU model and RNN model are both reliable in which values of the root mean square error (RMSE) were obtained 0.97, 1.04 and 1.01, respectively.
This study provides a feasible and accurate approach for groundwater level prediction by understanding and predicting different water level changes that may occur in the Daliao area in advance. As a result, the study will be an important reference for groundwater resources management and risk assessment, and achieve the goal of sustainable use of groundwater resources.

關鍵字(中)

★ 地下水
★ 風險評估
★ 智慧水管理
★ 深度學習

關鍵字(英)

★ Groundwater prediction
★ Wavelet transform
★ Risk assessment
★ Deep learning

論文目次

目錄
摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 ix
第一章緒論
1-1 研究背景與動機 1
1-2 研究問題與目的 5
1-3 研究流程 7
1-3 論文結構 9
第二章文獻回顧 11
2-1 地下水資源管理之挑戰 11
2-2 降雨與地下水的交互作用 14
2-3 地下水的影響因子 15
2-4 資料科學應用於地下水資源管理 16
2-4-1 小波轉換分析 17
2-4-2 線性迴歸模型 19
2-4-3 深度學習 21
第三章研究方法 24
3-1 研究架構 24
3-2 研究區域概述 26
3-3 資料蒐集及描述 28
3-3-1 地下水位觀測資料 28
3-3-2 影響地下水位因子資料(含時間尺度及基本統計量) 32
3-4 特徵萃取與特徵重要性評估方法 38
3-4-1 連續小波轉換分析 (Continuous Wavelet Transform) 38
3-4-2 逆小波轉換分析 (Inverse Continuous Wavelet Transform) 41
3-4-3 交叉小波變換分析 (Cross wavelet transform) 42
3-5 深度學習網路神經模型 44
3-5-1 遞歸神經網路模型(Recurrent Neural Network, RNN) 49
3-5-2 長短期記憶神經網路模型( Long Short-Term Memory ,LSTM) 50
3-5-3 門控循環單元神經網絡 (Gate Recurrent Unit, GRU) 53
第四章結果分析 55
4-1 連續小波轉換分析分解結果 55
4-2 逆小波轉換分析分析結果 59
4-3 交叉小波驗證結果分析 62
4-4 深度神經網路推估結果 68
4-5 線性迴歸推估結果 77
第五章討論與結論 79
5-1討論 79
5-2結論 84
5-3建議 86
5-4貢獻 87
參考文獻 88
評審意見回覆表 98

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Taylor, R. G., Todd, M. C., Kongola, L., Maurice, L., Nahozya, E., Sanga, H., & MacDonald, A. M. (2013). Evidence of the dependence of groundwater resources on extreme rainfall in East Africa. Nature Climate Change, 3(4), 374-378.

Owor, M., Taylor, R. G., Tindimugaya, C., & Mwesigwa, D. (2009). Rainfall intensity and groundwater recharge: empirical evidence from the Upper Nile Basin. Environmental Research Letters, 4(3), 035009.

Yu, H. L., & Chu, H. J. (2010). Understanding space–time patterns of groundwater system by empirical orthogonal functions: a case study in the Choshui River alluvial fan, Taiwan. Journal of Hydrology, 381(3-4), 239-247.

Dagès, C., Paniconi, C., & Sulis, M. (2012). Analysis of coupling errors in a physically-based integrated surface water–groundwater model. Advances in water resources, 49, 86-96.

Ebel, B. A., Mirus, B. B., Heppner, C. S., VanderKwaak, J. E., & Loague, K. (2009). First‐order exchange coefficient coupling for simulating surface water–groundwater interactions: Parameter sensitivity and consistency with a physics‐based approach. Hydrological Processes: An International Journal, 23(13), 1949-1959.

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指導教授

林遠見(Yuan-Chien Lin)

審核日期

2021-8-12

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