以類神經網路建構濁水溪流域地下水位推估模式

Abstract

近年來臺灣因工商業蓬勃發展使得用水需求量激增，地下水因其成本低廉且取用方便等優點而成為枯水期或缺乏儲水設施地區之重要水源。本研究以濁水溪流域上游山區為研究案例以建立地下水位變動推估模式，首先進行雨量、流量及地下水位變動之水文環境分析，利用相關性分析得知地下水位變動與雨量有一天之稽延關係；與流量為當日之稽延關係。接者透過地下水位觀測井的地質結構資料探討淺層與深層地下水位觀測井的地下水位變動關係，結果顯示黏土層為導致深淺層地下水位觀測井地下水位變動量相關性低的原因，而礫石層則為深淺層地下水位觀測井地下水位變動趨勢呈現一致的原因。 本研究期找出影響地下水位變動的有效降雨量，經由使用徐昇氏多邊形法計算後之集水區平均雨量作為門檻值以進行雨量篩選，並與各地下水位觀測井地下水位變動量做相關性分析，結果可分為四個類型: 緩升型、階梯型、緩降型、無規則型，同時以地下水位觀測井的深度與地質資料探討該相關成因。 因影響地下水位變動的因素眾多，故本研究分別採用具優越非線性映射能力及高度精確性之倒傳遞類神經網路(BPNN)與具模糊規則庫之調適性網路模糊推論系統(ANFIS)建構地下水位變動之推估模型，並且針對雨量、流量、雨量加流量等三種模式輸入組合進行模式推估之比較探討，成果顯示BPNN與ANFIS都有相當良好的推估的表現。緊鄰濁水溪的地下水位觀測井之地下水位變動受河川側向補注的影響較大，地下水位變動推估模式採流量輸入表現較佳；距離濁水溪主流較遠的地下水位觀測井之地下水位變動則受河川側向補注影響較小，地下水位變動推估模式採雨量輸入表現較佳；而結合雨量加流量兩種不同資訊的地下水位變動推估模式具更好的推估結果。了解山區水資源與地下水位變動之交互影響機制，將有助於未來進一步探討山區水資源之涵養策略，以期減緩下游地層下陷之問題。

In the past decade water demand has increased drastically due to the rapid development in economy and industry in Taiwan. Groundwater has become an important water source during drought periods and/or at the areas short of water storage facilities owing to its advantages such as low-cost and easy-to-access. So far, few studies have discussed about the estimation of the groundwater level variations at the mountainous areas of the Central Taiwan. Therefore, it will be beneficial to develop a reliable model for precisely estimating groundwater level variations at mountainous areas. The mountainous area at the upstream of the Zhuoshui River basin is used as a case study. First, the hydro-system and hydro-environment of the mountainous area are investigated. Second, the correlation among rainfall, streamflow and groundwater level variation is analyzed. The time lag between groundwater level variation and rainfall is one day while the time lag between groundwater level variation and steamflow is within one day. Third, the geological structure of the groundwater monitoring wells is used to explore the relationship of groundwater level variations between shallow groundwater wells and deep groundwater wells. The results show that the groundwater level variations of shallow groundwater wells have low relationship with those of deep ones in the clay layer, while the groundwater level variations of shallow and deep groundwater wells have similar trends. This research also investigates the effective impacts of rainfall amount on groundwater level variations and uses the Thiessen polygon method to calculate the average rainfall over the basin area based on different thresholds for threshold screening purpose. The correlations among groundwater level variations and rainfall filtered by different thresholds are analyzed and classified into four types: slow-ascending type; ladder-type; slow-descending type; and random type. In addition, the depth and geological structure of groundwater wells are used to find out the causes of those four types. Because the impacts on the variations of groundwater level are nonlinear, we uses both the backpropagation neural network (BPNN) due to its superior nonlinear mapping ability and high model accuracy and the adaptive network fuzzy inference system (ANFIS) with a fuzzy rule base to construct estimation models for groundwater level variations. We conduct a comparison study among different model input combinations: rainfall only; streamflow only; and rainfall and streamflow, and the results show that all the BPNN and ANFIS models perform well. Besides, the groundwater level variations of groundwater wells near the Zhuoshui River are much influenced by the lateral recharge from the river and the estimation models with streamflow as the only input perform better. While the groundwater level variations of groundwater wells far from the Zhuoshui River are influenced less by the lateral recharge from the river and the estimation models with rainfall as the only input perform better. The estimation models with rainfall and streamflow as inputs perform the best. Understanding the interactive recharge mechanisms between mountainous water resources and groundwater can facilitate future discussion on mountainous water resource conservation strategy for alleviating land subsidence in downstream areas.