- Aller, L., Bennet, T., Lehr, J.H., Petty, R.J., & Hackett, G. (1987). DRASTIC: a standardized system for evaluating groundwater pollution potential using hydrogeological settings. EPA/600/2–87/035. US Environmental Protection Agency, Ada, OK, USA.
- Antonogeorgos, G., Panagiotakos, D.B., Priftis, K.N., & Tzonou, A. (2009). Logistic regression and linear discriminant analyses in evaluating factors associated with asthma prevalence among 10-to 12-years-old children: Divergence and similarity of the two statistical methods. International journal of pediatrics, 2009.
- Celico, F., & Naclerio, G. (2005). Verification of a DRASTIC-based method for limestone aquifers. Water international, 30(4), 530-537.
- Chitsazan, M., & Akhtari, Y. (2009). A GIS-based DRASTIC model for assessing aquifer vulnerability in Kherran Plain, Khuzestan, Iran. Water resources management, 23(6), 1137-1155.
- Dixon, B. (2004). Prediction of groundwater vulnerability using an integrated GIS-based neuro-fuzzy techniques. Journal of Spatial Hydrology, 4 (2), 1-14.
- Fritch, T.G., Mcknight, C.L., Yelderman Jr, J.C., & Arnold, J.G. (2000). An aquifer vulnerability assessment of the Paluxy aquifer, central Texas, USA, using GIS and a modified DRASTIC approach. Environmental management, 25(3), 337-345.
- Jain, A.K., & Jha, C.K. (2017). Dropout Classification through Discriminant Function Analysis: A Statistical Approach.
- Jang, C.S., Lin, C.W., Liang, C.P., & Chen, J.S. (2016). Developing a reliable model for aquifer vulnerability. Stochastic environmental research and risk assessment, 30(1), 175-187.
- Javadi, S., Hashemy, S.M., Mohammadi, K., Howard, K.W.F., & Neshat, A. (2017). Classification of aquifer vulnerability using K-means cluster analysis. Journal of hydrology, 549, 27-37.
- Javadi, S., Kavehkar, N., Mohammadi, K., Khodadadi, A., & Kahawita, R. (2011). Calibrating DRASTIC using field measurements, sensitivity analysis and statistical methods to assess groundwater vulnerability. Water International, 36(6), 719-732.
- Javadi, S., Kavehkar, N., Mousavizadeh, M.H., & Mohammadi, K. (2010). Modification of DRASTIC model to map groundwater vulnerability to pollution using nitrate measurements in agricultural areas. Journal of Agricultural Science and Technology, 13, 239-249.
- Jmal, I., Ayed, B., Boughariou, E., Allouche, N., Saidi, S., Hamdi, M., & Bouri, S. (2017). Assessing groundwater vulnerability to nitrate pollution using statistical approaches: a case study of Sidi Bouzid shallow aquifer, Central Tunisia.Arabian Journal of Geosciences, 10(16), 364.
- Kholghi, M., Hassanzadeh, H., & Keyvanpour, M. (2010, May). Classification and evaluation of data mining techniques for data stream requirements. In Computer Communication Control and Automation (3CA), 2010 International Symposium on (Vol. 1, pp. 474-478). IEEE.
- Kosaki, T., Wasano, K., & Juo, A.S. (1989). Multivariate statistical analysis of yield-determining factors. Soil Science and Plant Nutrition, 35(4), 597-607.
- Lee, (2005). Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Journal of Remote Sensing, 26, 1477-1491.
- Li, R., Merchant, J. W., & Chen, X. H. (2014). A geospatial approach for assessing groundwater vulnerability to nitrate contamination in agricultural settings. Water, Air, & Soil Pollution, 225(12), 2214.
- Liang, C.P., Jang, C.S., Liang, C.W., & Chen, J.S. (2016). Groundwater Vulnerability Assessment of the Pingtung Plain in Southern Taiwan. International journal of environmental research and public health, 13(11), 1167.
- Magyar, N., Kovacs, J., Tanos, P., Trasy, B., Garamhegyi, T., & Hatvani, I. G. (2017). Application of Combined Cluster and Discriminant Analysis to Make the Operation of Monitoring Networks More Economical. World Academy of Science, Engineering and Technology, International Journal of Marine and Environmental Sciences, 4(5).
- Mair, A., & El-Kadi, A.I. (2013). Logistic regression modeling to assess groundwater vulnerability to contamination in Hawaii, USA. Journal of contaminant hydrology, 153, 1-23.
- Mishra, D., Chakrabarty, R., Sen, K., Pal, S.C., & Mondal, N.K. (2023). Groundwater vulnerability assessment of elevated arsenic in Gangetic plain of West Bengal, India; Using primary information, lithological transport, state-of-the-art approaches. Journal of Contaminant Hydrology, 104195.
- Mohammadi, K., Niknam, R., & Majd, V.J. (2009). Aquifer vulnerability assessment using GIS and fuzzy system: a case study in Tehran–Karaj aquifer, Iran. Environmental Geology, 58(2), 437-446.
- Neshat, A., Pradhan, B., Pirasteh, S., & Shafri, H.Z.M. (2014). Estimating groundwater vulnerability to pollution using a modified DRASTIC model in the Kerman agricultural area, Iran. Environmental Earth Sciences, 71(7), 3119-3131.
- Nolan, B. T., Hitt, K. J., & Ruddy, B. C. (2002). Probability of nitrate contamination of recently recharged groundwaters in the conterminous United States. Environmental science & technology, 36(10), 2138-2145.
- Poulsen, J., & French, A. (2008). Discriminant function analysis. Retrieved from.
- Shirzadi, , Saro, L., Hyun-Joo, Oh., & Chapi, K. (2012). A GIS-based logistic regression model in rock fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran. Natural Hazard, 64, 1639-1656.
- Sinan, M., & Razack, M. (2009). An extension to the DRASTIC model to assess groundwater vulnerability to pollution: application to the Haouz aquifer of Marrakech (Morocco). Environmental Geology, 57(2), 349-363.
- Tabachnick, B.G., & Fidell, L.S. (1996). Using Multivariate Statistics. Harper Collins College Publishers: New York. Tabachnick and Fidell compare and contrast statistical packages, and can be used with a modicum of pain to understand SPSS result print-outs.
- Torkashvand, M., Neshat, A., Javadi, S., & Pradhan, B. (2021). New hybrid evolutionary algorithm for optimizing index-based groundwater vulnerability assessment method. Journal of Hydrology, 598, 126446.
|
