創建新穎動態類神經網路於水文環境系統

Abstract

水為人類賴以為生的重要資源，水與環境在複雜的交互作用下形成的水文環境系統孕育了各種生命，使得人類社會得以繁榮發展。然而近年來由於氣象、水文、地文及人為開發等各種錯綜複雜之因素影響，水文環境系統逐漸面臨失衡，加上全球氣候變遷現象日趨顯著，導致突發性極端水文事件發生之頻率提高，都市下游河川水質劣化。為能進一步分析掌握其變化，本論文發展新穎系統化動態類神經網路與相關之分析方法應用於水文環境中兩大重要議題：水文量與水質之推估。動態回饋式類神經網路(Recurrent neural networks, RNNs)具內部回饋連結，對於複雜且具有回饋特性的水文環境系統之模擬有較高之精確性，因此近年來受到相當地重視與大量地應用。本論文第一部分發展並推導多時刻強化型即時回饋線上學習演算法於回饋式類神經網路 (Reinforced real-time recurrent learning algorithm for RNNs, R-RTRL NN)。此演算法可充分利用最新的觀測值與網路過去之預測值，不斷地遞迴修正網路權重參數，改善多時刻預報之可靠度與精確度。而為了驗證此演算法之可靠性與效能，本研究針對著名的混沌時間序列與石門水庫颱洪時期之入流量進行二、四至六時刻之多階段預報。此外亦選用了三個常用之類神經網路模式(兩個動態與一個靜態類神經網路)進行效能比較。結果顯示發展之R-RTRL類神經網路較其他類神經網路於多時刻混沌時間序列與水庫入流量預報整體表現優異，且可有效減緩線上學習法於多時刻預報網路權重參數調整延遲之問題。本論文第二部分則發展一系統化動態類神經網路模擬架構 (Systematical dynamic-neural modeling, SDM)。SDM主要包含Gamma test與非線性自回歸與外部輸入類神經網路(Nonlinear autoregressive with exogenous input, NARX)。Gamma test可有效且快速地篩選影響目標變數最為顯著之輸入因子組合；NARX類神經網路則具輸出層至輸入層之回饋連結，有優異的時間與空間推估能力。本論文運用發展之SDM於都市防洪抽水站多時刻前池水位預報，並探討回饋連結於不同模式情境下之貢獻。而建構之水位預報模式之準確度與穩定性皆相當高，可有效率且準確地預測洪汛時期臺北市玉成抽水站之前池水位。此外，SDM亦可應用於區域地下水砷濃度與河川總磷濃度之時空推估，精確度與可靠度皆較傳統倒傳遞類神經網路(BPNN)高，並有效解決進行區域水質推估常面臨之問題，如：影響因子組合之選取、資料稀少、過度描述與推估效果不佳等。而藉由NARX類神經網路之回饋連結與輸入因子資訊，可進一步將標的水質序列間隔較長之推估時間尺度轉換為與輸入因子相同且較短之觀測頻率，提供額外資訊進行水質狀況評估。整體而言，本論文創建之新穎動態類神經網路與分析方法：R-RTRL NN與SDM皆具廣泛之應用性，十分適合分析處理或推估水文環境中水文量與水質變化等重要議題，提供政府有關單位於水庫、都市河川與地下水等經營管理之參考資訊。

Water is a precious and scarce resource on the Earth and can be utilized by human beings. Due to the complex interaction between hydrological, meteorological, geographical factors and human activities with climate change effects, the hydro-environment that we live by is facing imbalanced conditions, such as intensive storms and typhoons with short durations and the degradation of the water quality in groundwater and urban rivers. Therefore, this dissertation is dedicated to the two main problems encountered in hydro-environmental systems: water quantity and water quality, and endeavors to develop novel dynamic artificial neural networks and modeling schemes to overcome problems for analyzing and estimating the dynamic variability of water quantity and water quality. Recurrent neural networks (RNNs) are computationally powerful nonlinear models that are capable of extracting dynamic behaviors from complex systems through internal recurrence and have attracted much attention for years. In the first part of this dissertation, a multi-step-ahead (MSA) reinforced real-time recurrent learning algorithm for RNNs (R-RTRL NN) is developed for adjusting connection weights by incorporating the latest observed values and model outputs into the online training process, and the sequential formulation of the R-RTRL NN is derived. To demonstrate its reliability and effectiveness, the proposed R-RTRL NN is implemented to make 2-, 4- and 6-step-ahead forecasts through a famous benchmark chaotic time series and a reservoir inflow series during typhoon events in North Taiwan. Numerical and experimental results indicate that the R-RTRL NN not only achieves superior performance than the comparative networks but also significantly improves the precision of MSA forecasts with effective mitigation in time-lag problems for both chaotic time series and reservoir inflow case during typhoon events. In the second part of the dissertation, the systematical dynamic-neural modeling (SDM) scheme that consists of the Gamma test for input factor selection and the nonlinear autoregressive with exogenous input (NARX) network for spatio-temporal estimation is proposed. The SDM is then applied to urban flood control to explore the contribution of recurrent connections and provide reliable results for forecasting the floodwater storage pond (FSP) water level in the Yu-Cheng pumping station. And the SDM is further utilized to estimate the regional arsenic (As) and total phosphate (TP) concentrations in groundwater and river systems, respectively. Results demonstrate that the SDM satisfactorily overcomes the difficulty raised by traditional methods in estimating the temporal and spatial variability of water quality parameters, such as identification of key input factors, data scarcity issue, model over-fitting problem and poor estimation performance. In addition, the SDM bears the ability to reconstruct the time series of the estimated water quality parameter from the original monitoring scale to a shorter monitoring scale through the recurrent connections of the NARX network. In summary, the two developed novel techniques in learning algorithm and modeling scheme, the R-RTRL NN and SDM, have broad applicability and are suitable to deal with water quantity and water quality issues in hydro-environmental systems, which beneficially provides useful information to water authorities for the management of reservoir operation, river basin, urban flood control and groundwater contamination.