台灣地區河川的生態環境已受相當程度的改變，為了人類需求大多數的河川都具有人工結構物用以調節水資源，這些結構物將會導致河川中最重要的流水型態產生改變，而自然流態的變化卻是能夠架構完整的河川生態系統，雖然河川保育觀念的提昇，但工程上常使用的生態流量管理法大部份屬於固定流量管理，這種單調性的環境變化對於生態環境會導致物種的多樣性降低並影響生態系統的穩定性等，所以河川保育的觀念應該不只著重於棲息地的復育，對於河川水流的流態變化也是重要的考量。
本研究欲推估未設測站的自然流態，設計選取全台42個上游未受人為控制且流量紀錄約20年的流量測站，採用可以量化河川的自然流態的31個水文改變指標(Indicator of Hydrologic Alteration，IHA)作為依變數，並使用水文改變指標作為分類變數進行流態均一區的劃分，再利用地理資訊系統取得地文及雨量參數作為自變數。推估模式的建立將利用可模擬描述非線性系統的類神經網路，採用具有一隱藏層的倒傳遞類神經網路作為本研究的推估模式。
從研究結果顯示台灣地區流量測站具有三類別的流態特性，而各組水文改變指標與集水區地文及雨量參數因子具有不同的相關特性。在類神經模式的水文改變指標推估值誤差結果顯示，各組指標的誤差百分比平均落在8到21%，且相關係數平均達到0.7以上呈現高度相關，表示本研究設計的類神經模式具有準確推估流量測站之自然流態的能力。
最後以多元迴歸模式進行指標推估比較，其結果顯示大部份的指標推估誤差表現上，類神經網路模式具有較優異的表現，顯示類神經網路推估模式能夠有效的描述模擬複雜的水文系統。

Maintaining the integrity of the river ecosystem must rely on natural flow regimes. In the past, the ecological streamflow management often used the minima flow approach, but many studies have shown that flow regulation is one of the most critical factors that cause the ecological environment of the river basin deterioration. The variability of the flow regimes strongly affects important habitat physical factors, such as flow rate, water depth and river erosion and deposition changes.
This study uses an artificial neural network approach that could simulate complex nonlinear model. The back-propagation neural network (BPN) with one hidden layer is used for estimating ungauged natural flow regime. A set of 42 gauging stations with no artificially upstream flow control mechanism and about 20 years streamflow record of each station has been selected for this study. Geographic Information System is used to obtain the physiographic characteristics and climate characteristics for model inputs. In order to quantify the natural river flow regime, the 31 Indicators of Hydrologic Alteration (IHA) are used as model outputs. Results show that the range of the each group’s mean absolute percentage error is 8~21% and the correlation coefficient of test samples is up to 0.7.
At last, a multiple regression model is used to compare with ANN model. The results show that the neural network model reveals much better performance in most estimating errors of indicators estimation than the regression model does. It suggests that the neural network model could effectively simulate the complex hydrological system.

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