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. |
參考文獻 |
1. 楊偉甫, "台灣地區水資源利用現況與未來發展問題". 臺灣水環境再生協會, 用水合理化與新生水水源開發論壇, (2010).
2. 台灣經濟部水利署, "107年各標的用水統計年報". (2018).
3. 王聖瑋, "地下的藍寶石專題報導". 科學發展月刊第 563 期, (2019).
4. 國家環境毒物研究中心, "砷 Arsenic". (2013).
5. 許正一, "土壤重金屬知多少". 科學發展月刊第 468 期, (2011).
6. Liu, C.-W., K.-H. Lin, and Y.-M. Kuo, "Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan". Science of the total environment, (2003). 313(1-3): p. 77-89.
7. Smith, A.H., E.O. Lingas, and M. Rahman, "Contamination of drinking-water by arsenic in Bangladesh: a public health emergency". Bulletin of the World Health Organization, (2000). 78(9): p. 1093-1103.
8. 行政院環保署, "地下水背景砷濃度潛勢範圍及來源判定流程". (2013).
9. Fan, R.A., "結合 HEC-RAS 與 MODFLOW 模式評估濁水溪沖積扇地下水位及地層下陷". Taiwan Water Conservancy, (2019). 67(1): p. 91-102.
10. Zheng, C. and P.P. Wang, "MT3DMS: a modular three-dimensional multispecies transport model for simulation of advection, dispersion, and chemical reactions of contaminants in groundwater systems; documentation and user’s guide". (1999).
11. 潘柏成, "抽水引致地層下陷造成砷釋出至地下水層之數值模擬與情境分析". (2018).
12. Garg, S. and S. Singh, "Modeling of arsenic transport in groundwater using MODFLOW: A case study". Int. J. Geomatics Geosci, (2016). 7(1): p. 56-81.
13. 高力山 and 張斐章, "應用類神經網路推估區域地下水中砷污染之研究". 農業工程學報, 57, (2011): p. 88-102.
14. 林政華, "以類神經網路探討雲林沿海地區地下水砷濃度與水質特徵". (2012).
15. Liang, C.-P., et al., "A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan’s Lanyang Plain". International Journal of Environmental Research and Public Health, (2021). 18(21): p. 11385.
16. Park, Y., et al., "Development of enhanced groundwater arsenic prediction model using machine learning approaches in Southeast Asian countries". Desalination and Water Treatment, (2016). 57(26): p. 12227-12236.
17. Smith, R., R. Knight, and S. Fendorf, "Overpumping leads to California groundwater arsenic threat". Nature communications, (2018). 9(1): p. 1-6.
18. Xiao, C., T. Ma, and Y. Du, "Arsenic releasing mechanisms during clayey sediments compaction: An experiment study". Journal of Hydrology, (2021). 597: p. 125743.
19. 歐東坤, "嘉南地區地下水砷濃度之研究". (2005).
20. 馮寶蓮, "地下水井抽水量推估之研究-以台南縣後壁鄉、安定鄉及高雄縣大樹鄉、林園鄉為例", in 環境與安全衛生工程所. (2006), 國立高雄第一科技大學: 高雄市. p. 134.
21. Islam, S., et al., Arsenic accumulation in rice: consequences of rice genotypes and management practices to reduce human health risk. Environment international, 2016. 96: p. 139-155.
22. Pedregosa, F., et al., "Scikit-learn: Machine learning in Python". the Journal of machine Learning research, (2011). 12: p. 2825-2830.
23. 張斐章 and 張麗秋, "類神經網路導論: 原理與應用". (2014): 滄海圖書資訊出版.
24. Winter, E., "The shapley value". Handbook of game theory with economic applications, (2002). 3: p. 2025-2054.
25. 單信瑜, "台灣地下水資源使用與水質現況". (2005).
26. 何薪, "河套平原農業灌溉影響下地下水中砷遷移富集規律研究". (2010), 武漢: 中國地質大學. |