02167nas a2200241   4500000000100000000000100001008004100002260001200043653002800055653003200083653003300115653001800148100001600166700001600182700001600198700001800214245014500232856005800377300000900435490001300444520145400457022001401911       9998                            d  c04/202410aArtificial Intelligence10aChuoshui River Alluvial Fan10aGroundwater Level Prediction10aWater Pumping1 aYu-Sheng Su1 aYu-Cheng Hu1 aYun-Chin Wu1 aChing-Teng Lo00aEvaluating the Impact of Pumping on Groundwater Level Prediction in the Chuoshui River Alluvial Fan Using Artificial Intelligence Techniques  uhttps://www.ijimai.org/journal/bibcite/reference/3447  a1-100 vIn press3 aOver the past decade, excessive groundwater extraction has been the leading cause of land subsidence in Taiwan's Chuoshui River Alluvial Fan (CRAF) area. To effectively manage and monitor groundwater resources, assessing the effects of varying seasonal groundwater extraction on groundwater levels is necessary. This study focuses on the CRAF in Taiwan. We applied three artificial intelligence techniques for three predictive models: multiple linear regression (MLR), support vector regression (SVR), and Long Short-Term Memory Networks (LSTM). Each prediction model evaluated the extraction rate, considering temporal and spatial correlations. The study aimed to predict groundwater level variations by comparing the results of different models. This study used groundwater level and extraction data from the CRAF area in Taiwan. The dataset we constructed was the input variable for predicting groundwater level variations. The experimental results show that the LSTM method is the most suitable and stable deep learning model for predicting groundwater level variations in the CRAF, Taiwan, followed by the SVR method and finally the MLR method. Additionally, when considering different distances and depths of pumping data at groundwater level monitoring stations, it was found that the Guosheng and Hexing groundwater level monitoring stations are best predicted using pumping data within a distance of 20 kilometers and a depth of 20 meters.   a1989-1660