| 摘要(英) |
Taiwan is highly dependent on groundwater resources, making water quality important. In recent years, due to the severe over-pumping of groundwater in the alluvial fan area of Taiwan′s Choushui River for agricultural irrigation and aquaculture, the concentration of arsenic in the water has increased, which in turn affects the water safety of groundwater, the growth of crops and even harms human health. Therefore, investigating and predicting changes in groundwater arsenic concentrations will help strengthen the use and management of water resources. At present, studies related to arsenic pollution of groundwater have not paid attention to the impact of artificial water pumping. At the same time, previous studies have pointed out that groundwater pumping amount can be estimated by using the electricity consumption of pumping motors. This study uses two different machine learning algorithms, Random Forest and Artificial Neural Network, Therefore, this study uses the electricity consumption data of the pumping wells in the Choushui alluvial fan area, the water level data of the observation wells in different aquifers and the rainfall data of the meteorological station as the characteristics. At the same time, two machine learning algorithms, Random Forest and Artificial Neural Network, were used to construct a prediction model of arsenic concentration in groundwater in the Choushui alluvial fan area, and the key characteristics of arsenic concentration changes caused by groundwater pumping were explored. The two monitoring wells with the highest arsenic concentration in the alluvial fan of Choushui is Dongxing Elementary School in Changhua County and Taixi Elementary School in Yunlin County, the Coefficient of Determination (R2) of the prediction model constructed by artificial neural network reached 0.723 and 0.723. And the Correlation Coefficient (COR) reached 0.999 and 0.989. In addition, the results of feature importance analysis show that the pumping activities of the pumping wells in the east half of the monitoring wells will have a greater impact on the arsenic concentration in the water. At the same time, the fluctuation of the water level of the second aquifer and the fourth aquifer in the groundwater level observation well has an important influence on the prediction model of groundwater arsenic concentration. Therefore, the results of this study show that the artificial neural network can effectively predict the arsenic concentration in groundwater using the power consumption data of the pumping wells in the Choushui alluvial fan area, the water level data of the observation wells in different aquifers and the rainfall data of the meteorological station. |
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