Antonakos, A. K. and Lambrakis, N.J. 2007. Development and testing of three hybrid methods for the assessment of aquifer vulnerability to nitrates, based on the drastic model, an example from NE Korinthia, Greece. Journal of Hydrology, Vol. 333(2), pp: 288–304.
Asghari Moghaddam, A., Fijani, E. and Nadiri, A. 2010. Groundwater Vulnerability Assessment Using GIS-Based DRASTIC
Model in the Bazargan and Poldasht Plains. Journal of Environmental Studies, Vol. 35, pp: No. 52.
Bellman, R. 2003. Dynamic programming. Mineola, NY: Dover Publications 366 pp.
Booker, D.J. and Snelder, T. H. 2012. Comparing methods for estimating flow duration curves at ungauged sites. Journal of Hydrology 434–435, 78–94.
Boulesteix, A.L, Janitza, S. Kruppa, J, and König IR. 2012. Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol.24 (2), pp: 493-507.
Breiman, L. 1996. Bagging predictors. Machine Learning, Vol. 24(2), pp: 40-123.
Breiman, L. 2001. Random Forests. Machine Learning, Vol. 45(1), pp: 5–32.
Chehata, N., Guo, L. and Mallet, C. 2009. Airborne lidar feature selection for urban classification using random forests. International Archives of the Photogrammetry, Journal of Remote Sensing and Spatial Information Sciences, Vol. 39, pp: 207-12.
Critto, A., Carlon, C. and Marcomini, A. 2003. Characterization of contaminated soil and groundwater surrounding an illegal landfill by principal component analysis and kriging. Journal of Environmental Pollution, Vol. 122(2), pp: 235–44.
Dixon, B.A. 2009. Case study using support vector machines, neural networks and logistic regression in a GIS to identify wells contaminated with nitrate-N. Journal of Hydrogeology, Vol. 17(6), pp: 1507–20.
Duda, R.O., Hart, P.E. and Stork, D.G. 2011. Pattern classification. 2nd. Edition. New York Efron B and Tibshirani R, 1993. In An introduction to the bootstrap. Vol. 57, pp: CRC press.
Emberger, L. 1952. Sur le quotient pluviothermique. C.R. Sciences, Vol. 234, pp: 2508-2511.
Fijani, E., Nadiri, A., Asghari Moghaddam, A., Tsai, F.T. C. and Dixon, B. 2013. Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh–Bonab plain aquifer, Iran. Journal of Hydrology, Vol. 503, pp: 89–100.
Friedl, M. A., Brodley, C. E. and Strahler, A. H. 1999. Maximizing land cover classification accuracies produced by decision trees at continental to global scales. IEEE Trans Geoscience Remote Sensing, Vol. 37(2), pp: 969–77.
Gislason, PO. Benediktsson, JA. and Sveinsson, JR. 2004. Random forest classification of multisource remote sensing and geographic data. Journal of Geoscience and Remote Sensing Symposium, Vol. 2, pp: 1049-52.
Guo, L., Chehata, N., Mallet,C. and Boukir, S. 2011. Relevance of airborne lidar and multispectral imagedata for urban scene classification using Random Forests. ISPRS Journal of Photogram Remote Sensing, Vol. 66(1), pp: 56–66.
Guyon, I. and Elisseeff, A. 2003. An introduction to variable and feature selection. Journal of Machine Learning Reserch, Vol. 3, pp: 1157–82.
Ko, B., Gim, J. and Nam, J. 2011. Cell image classification based on ensemble features and random forest. Journal of Electronics Letters, Vol. 47, pp: 638-9.
Kotsiantis, S. and Pintelas, P. 2004. Combining bagging and boosting .International Journal of Computational Intelligence, Vol. 1(4), pp: 324–33.
Lehmann, P., and D. 2009. Evaporation and capillary coupling across vertical textural contrasts in porous media, journal of Physics, Vol. 80(4), pp: 18-46.
Nadiri, A., Fijani, E., Tsai,T.C. and Asghari Moghaddam, A.2013. Supervised Committee Machine with Artificial Intelligence for Prediction of Fluoride Concentration, Journal of Hydroinformatics. Vol. 15, pp: 1474–1490.
Pal, M. 2005. Random Forest classifier for remote sensing classification. International Journal of Remote Sensing, Vol. 26(1), pp: 217–22.
Pahlavan Rad, M.R., Toomanian, N., Khormali, F., Brungard, C., Komaki, C.B, and Bogaert, P. 2014. Updating soil survey maps using random forest and conditioned Latin hypercube sampling in the loess derived soils of northern Iran. Journal of Geoderma, Vol. 232, pp: 97–106
Peters, J., Baets, B. D.,Verhoest, N. E. C., Samson, R., Degroeve, S. and Becker, P. D. 2007. Random Forests as a tool for ecohydrological distribution modelling. Journal of Ecol Model, Vol. 207(2–4), pp: 304–18.
Quinlan, J. R. 1993. C4.5 programs for machine learning. San Mateo, CA: Morgan Kaurmann 303 pp.
Quinlan, J.R. 1986. Induction of decision trees. Journal of Machine Learning, Vol. 1(1), pp: 81-106.
Rodriguez, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M. and Rigol-Sánchez, J. P. 2012d. An assessment of the effectiveness of a Random Forest classifier for land-cover classification. ISPRS Journal of Photogramm Remote Sensing, Vol. 67, pp: 91-104.
Schapire, R. 1990. The strength of weak learnability. Journal of Machine learning, Vol. 5, pp: 197-227.
Thapinta, A. and Hudak, P. 2003. Use of geographic information systems for assessing groundwater pollution potential by pesticides in Central Thailand. International journal of Environmental, Vol. 29, pp: 87–93
Tilahun, K. and Merkel, B. J .2010. Assessment of Groundwater Vulnerability to Pollution in Dire Dawa, Ethiopia using DRASTIC. Journal of Environmental Earth Sciences, Vol. 59, pp: 1485-1496.
Todd, D. K. 1980. Groundwater hydrology, John Wiley and Sons, New York.
Vrba, J. and Zoporozec, A. 1994. Guidebook on mapping groundwater vulnerability. International Contributions to Hydrogeology. 139 pp.
WHO (World Health Organization). 2009. Guideline for Drinking Water Quality.
Zabet, T. A .2002. Evaluation of aquifer vulnerability to contaminant potential using DRASTIC method. Journal of Environmental Geology, Vol. 43(1-2), pp: 203-208.
)