Construct intelligent groundwater level estimation models–A case study at Zhuoshui River Basin in Taiwan
Date Issued
2014
Date
2014
Author(s)
Huang, Jun-Lin
Abstract
The shortage of water resources is a global problem. Due to the steep slopes and gradients of rivers, rapid flows and uneven spatio-temporal rainfall distributions in Taiwan, most of rainfalls flow directly into the ocean. Groundwater has become an important water resource because of its low cost and easy extraction. The upstream zone and the proximal alluvial fan of the Zhuoshui River are good natural groundwater recharge areas. However, the over extraction of groundwater occurs in the coastland of southwestern Taiwan, which results in serious land subsidence. To obtain and estimate the trend of groundwater level variations for making countermeasures in response to future possible land subsidence areas, this study establishes the relationships between rainfall, streamflow and groundwater level and constructs intelligent groundwater level estimation models for the upstream zone and the proximal alluvial fan of the Zhuoshui River basin based on long-term observed data of streamflow, groundwater level and rainfall, which can provide valuable information for the prevention and treatment of land subsidence.
In this study, data of groundwater level, streamflow and rainfall recorded in the Jhuoshuei River basin during 2002-2011 were obtained from the Water Resources Agency, Taiwan. The correlation analysis is first applied to building the relationships between monthly mean groundwater level and monthly mean streamflow as well as monthly mean rainfall. Artificial neural networks (ANNs), which resemble the human thinking process and possess a great ability to handle non-linear complex systems, are implemented to configure estimation models. By taking various input combinations into account, the most suitable estimation model of groundwater level can be established by the back propagation neural network (BPNN). The results demonstrate that the constructed estimation models can suitably estimate monthly groundwater level with high correlation (larger than 0.8).
For investigating the mechanism of groundwater level variation, a sensitivity analysis is then conducted on input variables of the estimation model by using the partial derivative method. Based on the distributions of the partial derivative values corresponding to each inputs (rainfall, streamflow and discharge), we establish the relationships between inputs and output (groundwater level) and identify rainfall as the most significant key factor. Then the impacts of rainfall amount on groundwater level variation can be obtained by the sensitivity analysis. The results of the proposed approach can be used as a valuable reference to water resources management and conservation.
Subjects
地下水位
類神經網路
敏感度分析
倒傳遞類神經網路
水資源管理
偏微分
Type
thesis
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