雲林沿海地區地下水質變化分析：（II）倒傳遞類神經網路法

本文第二部份採用因子分析建立雲林沿海地區地下水水質之主要因子「海水鹽化因子」及「砷污染因子」，然後應用具有自我組織、自我學習的能力及非線性特性之倒傳遞類神經網路來預測地下水質變化。首先探討隱藏層神經元數目改變是否會對水質預測結果造成明顯差異；並藉由不同的BP網路輸入模式探討水質預測結果之準確性。結果顯示隱藏層之神經元數目對BP網路訓練及預測效果並不明顯，而輸入層中若包含相關性較大的近期變量則會增加網路預測效果。此外，並建立預測值的信賴區間，以提供預測値的可信度，結果顯示BP網路能學習此水質複雜變化之能力，並於預測時能獲得良好之可信度。

關鍵字

倒傳遞類神經網路；地下水水質；預測

並列摘要

The second part of the study adopts the seawater salinization factor and arsenic pollutant factor to forecast the variation of ground water quality in Yun-Lin coastal area. Back-Propagation (BP) neural network which has the characteristics of self-organizing, self-learning and nonlinearity is selected to forecast future variation of groundwater quality. The influence of hidden nodes to the water quality forecasting is discussed first, the accuracy of the water quality forecasting results using different BP network input model are also analyzed. The results show that the hidden nodes are not a significant factor to BP network training and forecasting. Using recent variations data with high relativity in the input layer gives better results on network forecasting. Besides, the confident intervals of each forecasting value are also computed. The results indicate that the neural network is capable to describe the complex variation of groundwater quality and provide good forecasting reliability.

並列關鍵字

Back-propagation neural network ； Groundwater quality ； Forecasting

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