Document Type : Research Paper


1 Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran.

2 Department of Watershed Management Engineering, Faculty of Natural Resources, Lorestan University, Lorestan, Iran

3 Research and Education Center for Agriculture and Natural Resources of Lorestan Province, Lorestan, Iran



Agriculture is not only the largest user of groundwater resources throughout the world but also its economy is highly dependent on these sources. Thanks to having more effective parameters and subsequently more accurate results, the classification methods in many fields, such as sustainable agriculture has been taken into consideration. Discriminant analysis models are more complex, more accurate and more efficient in comparison to modern methods. In current study, the areas with infiltration potential located in some parts of Khomein, Shazand, Azna, Aligudarz and Durood areas (Marboreh watershed) were went under investigation using the mixture discriminant analysis (MDA) model. For this purpose, the infiltration samples gathered by double ring test, with the environment-effecting layers on infiltration, were prepared and then introduced to R_studio, employed to run MDA. In order to assess the results, validation indices (ROC curve, CCI, TSS, Recall and Precision indices) were used. According to the results, 6.2, 6.1, 12.7, 13.3 and 15.9% of areas of Shazand, Khomein, Durood, Azna and Aligodarz respectively lie in highly potential infiltration, whereas 1.1 16.5, 14.3, 19.6 and 10.8% of those areas were found to have extremely potential infiltration. Most of these areas have sandy soil texture and Quaternary formations with agricultural and range land uses. The accuracy indices that obtained as 0.89%, 76.66, 0.53, 0.91% and 0.73%, witnessing the acceptance and excellence of model performance. The results of this study can be useful in the decision-making for managers and planners regarding to the groundwater recharge in accordance with urban and agricultural needs, because groundwater resources and ensuring their stability are the main factors for sustainable agriculture.


A comprehensive report of Aligoders, the second, third, sixth and sixteenth chapters. (2013). Pars Rai Ab Consulting Engineering Company.
Agarwal, R. & Garg, P.K. (2016). Remote sensing and GIS based groundwater potential & recharge zones mapping using multi-criteria decision-making technique. Water resources management, 30(1), pp. 243-260.
Aghazadeh, N., Chitsazam, M. & Mirzayi, Y. (2019). Assessing the potential and actual amounts of aquifer recharge in urban areas and mapping the areas prone to artificial recharge using GIS and AHP. Case study: Urmia urban aquifer. Advanced Applied Geology, 9(2), pp. 56-67. (In Persian)
Alizadeh, A. (1988). Drainage irrigation, the fifth chapter. Publications of Ferdowsi University of Mashhad. (In Persian)
Behyari, M., Alizadeh, A. & Mahmodian, S. (2017). Evaluation of active structure effect on subsidence hazard insight to Analytical Hierachy Process. Advanced Applied Geology, 7(2), pp. 49-56. (In Persian)
Beven. K. & Freer. J. (2001) A dynamic TOPMODEL, Hydrological Processes, 15(10), pp. 1993-2011.
Chen, W., Li, Y., Xue, W., Shahabi, H., Li, S., Hong, H., Wang, X., Bian, H., Zhang, S. & Pradhan, B. (2020a). Modeling flood susceptibility using data-driven approaches of naïve bayes tree, alternating decision tree, and random forest methods. Sci. Total Environ. 701, 134979.
Chen, W., Pourghasemi, H.R. & Naghibi, S.A. (2018). Prioritization of landslide conditioning factors and its spatial modeling in shangnan county, china using gis-based data mining algorithms. Bull. Eng. Geol. Environ. 77, pp. 611–629.
Chen, W., Zhao, X., Tsangaratos, P., Shahabi, H., Ilia, I., Xue, W., Wang, X. & Ahmad, B.B. (2020b). Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping. J. Hydrol. 583, 124602.
Chenini, I., Mammou, A.B. & El May, M. (2010). Groundwater recharge zone mapping using GIS-based multi-criteria analysis: a case study in Central Tunisia (Maknassy Basin). Water resources management, 24(5), pp. 921-939.
Conforti. M., Aucelli. P. P, Robustelli.G. & Scarciglia.F. (2011). Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy), Nat Hazards, 56(3), pp. 881–898.
Döll, P., & Flörke, M. (2005). Global-scale estimation of diffuse groundwater recharge: model tuning to local data for semi-arid and arid regions and assessment of climate change impact.
Explanatory studies of soil protection and watershed management of Marbareh watershed and a small part of Tireh River in the north of Dorud. (2001). Lorestan Regional Water Company, management of basic studies of water resources.
Fagbohun, B.J. (2018). Integrating GIS and multi-influencing factor technique for delineation of potential groundwater recharge zones in parts of Ilesha schist belt, southwestern Nigeria. Environmental earth sciences, 77(3), p.69.
Fallah, S., Ghobadinia, M., Shokrgozar Darabi, M. & Ghorbani Dashtaki, S. (2012). A study on sustainability of groundwater resources of Darab plain, Iran. Iranian Journal of Water Research in Agriculture, 26(2), pp. 161- 172. (In Persian)
Fishman, R. M., Siegfried, T., Raj, P., Modi, V., & Lall, U. (2011). Over‐extraction from shallow bedrock versus deep alluvial aquifers: Reliability versus sustainability considerations for India's groundwater irrigation. Water Resources Research, 47(6).
Foster, S.S.D. & Chilton, P.J. (2003). Groundwater: the processes and global significance of aquifer degradation. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 358(1440), pp. 1957-1972.
Gerland, P., Raftery, A. E., Ševčíková, H., Li, N., Gu, D., Spoorenberg, T. & Bay, G. (2014). World population stabilization unlikely this century. Science, 346(6206), pp. 234-237.
Gurdak, J. J., Walvoord, M. A. & McMahon, P. B. (2008). Susceptibility to Enhanced Chemical Migration from Depression-Focused Preferential Flow, High Plains Aquifer All rights reserved. Vadose Zone Journal, 7(4), pp. 1218-1230.
Hastie, T., & Tibshirani, R. (1996). Discriminant analysis by Gaussian mixtures. Journal of the Royal Statistical Society B, 58, pp. 155-176.
Hastie, T., Tibshirani, R. & Friedman, J. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
Herrera‐Pantoja, M. & Hiscock, K. M. (2008). The effects of climate change on potential groundwater recharge in Great Britain. Hydrological Processes: An International Journal, 22(1), pp. 73-86.
Hoang, N.D. & Bui, D.T. (2018). Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: A multi-dataset study. Bull. Eng. Geol. Environ. 77, pp. 191–204.
Holman, R. R., Farmer, A. J., Davies, M. J., Levy, J. C., Darbyshire, J. L., Keenan, J. F. & Paul, S. K. (2009). Three-year efficacy of complex insulin regimens in type 2 diabetes. New England Journal of Medicine, 361(18), pp. 1736-1747.
Huberty, C. J. (1984). Issues in the use and interpretation of discriminant analysis. Psychological bulletin, 95(1), 156.
Huberty, C. J. (1994). Why multivariable analyses?. Educational and Psychological Measurement, 54(3), pp. 620-627.
Jaafarzadeh, M. S., Tahmasebipour, N., Haghizadeh, A., Pourghasemi, H. R. & Rouhani, H. (2021). Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models. Scientific Reports, 11(1), 5587.
Jaafarzadeh, M. S., Tahmasebipour, N., Haghizadeh, A., Pourghasemi, H. R. & Rouhani, H. (2022). Prediction of susceptible areas for groundwater recharge based on maximum entropy model. Advanced Applied Geology, 11(4), pp. 723-739. (In Persian)
Ju, J., Kolaczyk, E.D. & Gopal, S. (2003). Gaussian mixture discriminant analysis and sub-pixel land cover characterization in remote sensing. Remote Sensing of Environment, 84(4), pp. 550-560.
Jyrkama, M. I. & Sykes, J. F. (2007). The impact of climate change on spatially varying groundwater recharge in the Grand River watershed (Ontario). Journal of Hydrology, 338(3-4), pp. 237-250.
Kalantar, B., Al-Najjar, H.A., Pradhan, B., Saeidi, V., Halin, A.A., Ueda, N. & Naghibi, S.A. (2019). Optimized conditioning factors using machine learning techniques for groundwater potential mapping. Water, 11(9), pp. 1909.
Khan, D., Ejaz, N., Khan, T. A., Saeed, T. U. & Attaullah, H. (2015). Sustainable groundwater–a need of sustainable agriculture. International Journal of Civil Engineering, 13(3), pp. 305-320.
Li, X. & Wang, Y. (2013). Applying various algorithms for species distribution modelling. Integr. Zool. 8, pp. 124–135.
Li. X., Zhao. S., Yang. H., Cong. D. & Zhang. Z. (2017). A bi-band binary mask-based land-use change detection using Landsat 8 OLI imagery. Sustainability, 9(3), pp: 479.
Lim, T. S., Loh, W. Y. & Shih, Y. S. (2000). A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine learning, 40, pp. 203-228.
Liu. X., He. J., Yao. Y., Zhang. J., Liang. H., Wang. H. & Hong. Y. (2017). Classifying urban land use by integrating remote sensing and social media data. International Journal of Geographical Information Science, 31(8), pp. 1675-1696.
Lucà. F., Conforti. M. & Robustelli. G. (2011). Comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria, South Italy, Geomorphology, 134(3), pp. 297-308.
Maleki Rad, Z., Almasian, M., Pourkarmani, M. & Zarei Sahamiyeh, R. (2018). Application of GIS in the study of faults in Lorestan province. The first national geological conference of Iran.
Malet, J.-P. Maquaire, O. Thiery, Y. Puissant, A. van Beek, L.P. van Asch, T.W. & Remaître, A. (2007). Landslide risk zoning-what can be expected from model simulations? A tentative application in the south french alps. Guidel. Mapp. Areas Risk Landslides Eur. 23, 31.
Marker. M., Pelacani. S. & Schroder. B. (2012). A functional entity approach to predict soil erosion processes in a small Plio -Pleistocene Mediterranean catchment in Northern Chianti, Italy, Geomorphology, 125(4), pp. 530-540.
McLachlan, G.J. (2004). Discriminant analysis and statistical pattern recognition (Vol. 544). John Wiley & Sons.
Mehraban, M., Golkarian, A. & Khosravi, K. (2017). Evaluation of gully erosion sensitivity using the maximum entropy model (case study: Shorluk area, Razavi Khorasan province). The Third National Conference on Soil and Watershed Conservation, pp. 964-975. (In Persian)
Mirhashemi, S. H., Haghighatjou, P., Mirzaei, F. & Panahi, M. (2018). Using CART algorithm in predicting groundwater table fluctuations inside and outside of an irrigation system (case study: irrigating area of Qazvin). Iranian Journal of Soil and Water Research, 49(2), pp. 385-395. (In Persian)
Mogaji, K.A., Omosuyi, G.O., Adelusi, A.O. & Lim, H.S. (2016). Application of GIS-based evidential belief function model to regional groundwater recharge potential zones mapping in hardrock geologic terrain. Environmental Processes, 3(1), pp. 93-123.
Naghibi, S. A., Pourghasemi, H. R., Pourtaghi, Z. S. & Rezaei, A. (2015). Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran. Earth Science Informatics, 8(1), pp. 171-186.
National Research Council. Panel on Discriminant Analysis Classification and Clustering (1988). Discriminant Analysis and Clustering.
Panel on Discriminant Analysis, Classification, and Clustering. (1989). Discriminant analysis and clustering. Statistical Science, 4, pp. 34-69.
Raghavendra, N. S. & Deka, P. C. (2015). Sustainable development and management of groundwater resources in mining affected areas: a review. Procedia Earth and Planetary Science, 11, pp. 598-604.
Rausch, J.R. & Kelley, K. (2009). A comparison of linear and mixture models for discriminant analysis under nonnormality. Behavior Research Methods, 41(1), pp.85-98.
Regmi. A.D., Devkota. K.C., Yoshida. K., Pradhan. B., Pourghasemi. H.R., Kumamoto. T. & Akgun. A. (2014). Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya, Arab J Geosci, 7 (2), pp. 725–742.
Richey, A. S., Thomas, B. F., Lo, M. H., Famiglietti, J. S., Swenson, S. & Rodell, M. (2015). Uncertainty in global groundwater storage estimates in a Total Groundwater S tress framework. Water Resources Research, 51(7), pp. 5198-5216.
Rwanga. S.S. & Ndambuki. J.M. (2017), Accuracy assessment of land use/land cover classification using remote sensing and GIS, International Journal of Geosciences, 8(04), pp: 611.
Salmani, H., Saber Chenari, K., Rostami Khalaj, M. & Jahandideh, O. (2016). Performance comparision of Information Value and Density Area Methods for spring potential in Ghurchay Watershed, Golestan Province. Hydrogeology, 1(1), pp. 12-28. (In Persian)
Samadi, J. & Samadi, J. (2017). Spatial-Temporal Modeling of Groundwater Level Variations of Urban and Rural Areas in Kashan Aquifer Using GIS Techniques. Journal of Environment Science and Technology, 19(1), pp. 63-77. (In Persian)
Schmid, U., Roesch, P., Krause, M., Harz, M., Popp, J. & Baumann, K. (2009). Gaussian mixture discriminant analysis for the single-cell differentiation of bacteria using micro-Raman spectroscopy. Chemometrics and Intelligent Laboratory Systems, 96(2), pp. 159-171.
Sekertekin. A., Marangoz. M. & Akcin. H. (2017). Pixel-based classification analysis of land use land cover using sentinel-2 and landsat-8 data, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.
Thomas, A. & Tellam, J. (2006). Modelling of recharge and pollutant fluxes to urban groundwaters. Science of the total environment, 360(1-3), pp.158-179.
Wang, Guirong, Xi Chen, & Wei Chen. "Spatial prediction of landslide susceptibility based on GIS and discriminant functions." ISPRS International Journal of Geo-Information 9, no. 3 (2020): 144.
Zehtabian, G., Rafiei Imam, A., Alavi Panah, K. & Jafari, M. (2004). Investigating the underground water of Varamin plain for irrigation of agricultural lands. Geographical Research, 48, pp. 91-102. (In Persian)
Zhao, G. Pang, B. Xu, Z. Peng, D. & Xu, L. (2019). Assessment of urban flood susceptibility using semi-supervised machine learning model. Sci. Total Environ. 659, 94.