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Chuen-Fa Ni received his Ph.D. at the Department of Civil and Environmental Engineering, Michigan State University, MI, USA, in 2005. He is a licensed Hydraulic Engineer with industrial working experience from 1999 to 2001. He joined the Graduate Institute of Applied Geology, National Central University (NCU), in 2007 and became a full professor at NCU in 2016. NCU awarded him the Distinguished Professor Award in 2020. He has published over 70 refereed papers, including book chapters, technical notes, and discussions. His research interests include numerical modeling of flow and contaminant transport in porous media and fractured rocks, stochastic approaches for uncertainty analyses and inverse modeling, laboratory and field experiments to characterize hydrogeological properties cross-scale, seawater intrusion and reactive transport in coastal aquifers, land subsidence monitoring and coupled hydro-mechanical modeling, and development of web platforms for groundwater modeling and data analysis.
- Ni, C.F., Vu, T.D., Li, W.C., Tran, M.T., Bui, V.C., Troung, M.H., 2023. Stochastic-based approach to quantify the uncertainty of groundwater vulnerability, Stochastic Environmental Research and Risk Assessment, 37, 1897–1915. https://doi.org/10.1007/s00477-022-02372-2.
- Huynh, T.M.H., Ni, C.F., Su, Y.S., Nguyen, V.C.N., Lee, I.H., Lin, C.P., Nguyen, H.H., 2022. Predicting heavy metal concentrations in shallow aquifer systems based on low-cost physiochemical parameters using machine learning techniques, International Journal of Environmental Research and Public Health, 19, 12180. https://doi.org/10.3390/ijerph191912180.
- Chang, C.M., Ni, C.F., Li, W.C., Lin, C.P., Lee, I.H., 2022. Quantitation of the uncertainty in the prediction of flow fields induced by the spatial variation of the fracture aperture, Engineering Geology, 299, 106568.
- Chang, C.M., Ni, C.F., Li, W.C., Lin, C.P., Lee, I.H., 2021. Stochastic analysis of the variability of groundwater flow fields in heterogeneous confined aquifers of variable thickness, Stochastic Environmental Research and Risk Assessment, 36, 2503–2518. https://doi.org/10.1007/s00477-021-02125-7
- Vu, T.D., Ni, C.F., Li, W.C., Truong, M.H., Hsu, S.M. 2021. Predictions of groundwater vulnerability and sustainability by an integrated index-overlay method and physical-based numerical model. Journal of Hydrology, 596, 126082. https://doi.org/10.1016/j.jhydrol.2021.126082
- Su, Y.S., Ni, C.F., Li, W.C., Lee, I.H., Lin, C.P., 2020. Applying deep learning algorithms to enhance simulations of large-scale groundwater flow in IoTs, Applied Soft Computing Journal, 92, July 2020, 106298. https://doi.org/10.1016/j.asoc.2020.106298
- Lu, C.H., Ni, C.F., Chang, C.P., Yen, J.Y., Chuang, R.Y., 2018. Coherence Difference Analysis of Sentinel-1 SAR Interferogram to Identify Earthquake-Induced Disasters in Urban Areas. Remote Sensing, 10(8), 1318. https://doi.org/10.3390/rs10081318
- Lu, C.H., Ni, C.F., Chang, C.P., Chen, Y.A., Yen, J.Y., 2016. Geostatistical data fusion of multiple type observations to improve land subsidence monitoring resolution in the Choushui River Fluvial Plain, Taiwan, Terrestrial, Atmospheric and Oceanic Sciences. 27(4), 505-520.