| dc.description.abstract | Groundwater is one of Taiwan′s important water resources. Excessive use of groundwater can lead to various problems such as salt water intrusion and land subsidence. The most common method of water storage is through surface water reservoirs. However, over-reliance on surface water reservoirs can sometimes pose problems due to land acquisition, pollution issues, evaporation, or infiltration. Additionally, constructing reservoirs in relatively flat areas, such as coastal regions, faces many restrictions. Aquifer Storage and Recovery (ASR) is a water storage method that involves collecting water from the surface during periods of abundance and injecting it into underground aquifers for storage, to be used in the future when needed. Drilling itself is time-consuming and labor-intensive, and it is obviously not feasible to conduct it in all locations. Therefore, artificial neural network (ANN) is a way to use limited data to estimate the entire range of data. The purpose of this study is to use ANN for spatial interpolation to identify feasible sites in Taiwan for ASR. The straight-line distance between coordinates and observation well locations is used as input parameters for the artificial neural network, with transmissivity values and groundwater quality as output results. The results from the artificial neural network indicate that the best predictive performance occurs with two hidden layers, each with 16 neurons. Finally, a river distribution map is incorporated to filter and select optimal ASR sites in Taiwan, ensuring that there is a water source around the selected locations. These results can be used by government agencies to determine suitable ASR sites. | en_US |