Comparison of different combination methods in simultaneous use of frameworks abilities to assess groundwater vulnerability


1 Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Iran

2 Department of Geology, Faculty of Sciences, University of Urmia, Iran

3 Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh, Iran


Ahar aquifer is one of the most active agricultural and livestock areas. The development of these activities has led to an increase in the use of chemical and natural fertilizers. Therefore, in this research, groundwater vulnerability in Ahar aquifer has been investigated using three frameworks; DRASTIC, SINTACS and GODS to identify vulnerable areas and prevent groundwater resources from more contamination. To use the simultaneous advantage of all three frameworks, two unsupervised and supervised combination techniques were applied to combine them. Invalidation step, nitrate concentration data and its correlation coefficient with the vulnerability index were used. The results show that the artificial neural networks (ANNs) model has better performance with a better determination coefficient (R2) and correlation coefficient (CI) than other techniques. Therefore, the supervised method has a higher ability than the weighted averaging method and can be used to determine the vulnerability of other areas.


Aller, L., Bennet, T., Leher, J., Petty, R., Hackett, G., 1987. DRASTIC: A Standardized system for evaluating groundwater pollution potential using hydro-geological settings, Kerr Environmental Research Laboratory. U.S Environmental Protection Agency Report, (EPA/600/2-87/035).
Anil, K.J., Mao, J., Mohiuddin, K.M., 1996. Artificial neural network: A tutorial, IEEE.
Antonakos, A.K., 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 333(2-4), 288-304.‏
ASCE, Task Committee on Application of Artificial Neural Networks in Hydrology, 2000 Artificial Neural Network in hydrology, part I and II. Journal of Hydrologic Engineering 5(2), 115-137.
Asghari Moghaddam, A., Gharekhani, M., Nadiri, A.A., Kord, M., Fijani, A., 2017. Evaluation of intrinsic vulnerability of Ardabil plain using DRASTIC, SINTACS and SI methods. Journal of Geography and Planning 57-74 (In Persian).
Babiker, I. S., Mohamed, M. A., Hiyama, T., Kato, K., 2005. A GIS-based DRASTIC model for assessing aquifer vulnerability in Kakamigahara Heights, Gifu Prefecture, central Japan. Science of the Total Environment 345(1-3), 127-140.
Boughriba. M., Barkaoui. A., Zarhloule. Y., Lahmer. Z., El-Houadi. B., Verdoya. M., 2009. Groundwater vulnerability and risk mapping of the Angad transboundary aquifer using DRASTIC index method in GIS environment. Arabian Journal of Geoscience 3, 207-220.
Chilton, P., Vlugman, A., Foster, S., 1990. A groundwater pollution risk assessment for public water supply sources in Barbados. American Water Resources Association International Conference on Tropical Hydrology and Caribbean Water resources, San Juan de Puerto Rico, p. 279-289.
Chitsazan, N., Nadiri, A.A., Tsai, F.T.C., 2015. Prediction and structural uncertainty analyses of artificial neural networks using hierarchical Bayesian model averaging. Journal of Hydrology 528, 52-62.
Civita, M., 1990. Legenda unificata per le Carte della vulnerabilita dei corpi idrici sotterranei/ Unified legend for the aquifer pollution vulnerability Maps, Studi sulla Vulnerabilita degli Acquiferi. Pitagora Edit, Bologna.
Corniello, A., Ducci, D., Napolitano, P., 1997. Comparison between parametric methods to evaluate aquifer pollution vulnerability using GIS: an example in the “Piana Campana”, southern Italy. Engineering Geology and the Environment, Balkema, Rotterdam, p. 1721-1726.‏
De Ridder, N.A., 1968. Hydrogologic Study of Varamin-Garmsar Area.
Fijani, E., Nadiri, A. A., Moghaddam, A. A., Tsai, F.T.C., 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 503, 89-100.
Javanshir, G., Nadiri, A.A., Sadeghfam, S., Abbas Novinpour, E., 2016. Introducing a new method to aquifer vulnerability assessment of Moghan plain based on combination of DRASTIC, SINTACS and SI methods. Journal of Ecohydrology 4, 491-503 (In Persian).
Lesquyer, J.L., Riou, R., Babakhani, A., 1978. Ahar Geological Map 1: 250000. Geological and Mineral Survey of Iran (In Persian).
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 Geology58 (2), 437-446.‏
Nadiri, A., Fijani, E., Tsai, F., Asghari Moghaddam, A., 2013b. Supervised Committee Machine with Artificial Intelligence for Prediction of Fluoride Concentration. Hydroinformatics Journal 15(4), 1474-1490.
Nadiri, A.A., 2013a. Comparison of efficiency of numerical and artificial intelligence models in aquifer management (Case Study: Tasuj Plain). PhD Thesis, University of Tabriz, (In Persian).
Nadiri, A.A., Gharekhani, M., Khatibi, R., 2018b. Mapping aquifer vulnerability indices using artificial intelligence-running multiple frameworks (AIMF) with supervised and unsupervised learning. Water Resources Management 32(9), 3023-3040.‏
Nadiri, A.A., Gharekhani, M., Khatibi, R., Sadeghfam, S., Moghaddam, A.A., 2017a. Groundwater vulnerability indices conditioned by supervised intelligence committee machine (SICM). Science of the Total Environment 574, 691-706.
Nadiri, A.A., Sedghi, Z., Kazemian, N., 2018a. Optimization of DRASTIC method using ANN to evaluating of vulnerability of multiple Varzqan. Iranian Journal of Ecohydrology 4(4), 1089-1103 (In Persian).
Nadiri, A.A., Sedghi, Z., Khatibi, R., Gharekhani, M., 2017b. Mapping vulnerability of multiple aquifers using multiple models and fuzzy logic to objectively derive model structures. Science of the Total Environment 593, 75-90.
Nadiri, A.A., Sedghi, Z., Khatibi, R., Sadeghfam, S., 2018c. Mapping specific vulnerability of multiple confined and unconfined aquifers by using artificial intelligence to learn from multiple DRASTIC frameworks. Journal of Environmental Management 227, 415-428.
Nakhostin Rouhi, M., Rezaei Moghaddam, M.H., Rahimpour, T., 2017. Groundwater vulnerability zonation using DRASTIC and SI models in GIS (Case Study: Ajabshir Plain). Iranian Journal of Ecohydrology 4, 578-599 (In Persian).
Niknam, R., Mohammadi, K., Majd, V.J., 2007. Groundwater Vulnerability Evaluation of Tehran-Karaj Aquifer Using DRASTIC Method and Fuzzy Logic. Iran- Water Resources Research 3, 39-47 (In Persian).
Paez, G., 1990. Evaluacion de la vulnerabilidad a la contaminacion de las agues subterraneas en el Valle del Cauca. Informe Ejecutivo, Corporeginal del Valle del Cauca, Cauca, Colombia.
Panagopoulos, G., Antonakos, A., Lambrakis, N., 2006. Optimization of the DRASTIC method for groundwater vulnerability assessment via the use of simple statistical methods and GIS. Hydrogeology Journal 14, 894-911.
Secunda, S., Collin, M.L., Melloul, A.J., 1998. Groundwater vulnerability assessment using a composite model combining DRASTIC with extensive agricultural land use in Israel’s Sharon region. Journal of Environmental Management 54, 39-57.
Tayfur, G., Nadiri, A.A., Asghari Moghaddam, A., 2014. Supervised intelligent committee machine method for hydraulic conductivity estimation. Water Resources Management 28, 1173-1184.
Vrba, J., Zoporozec, A., 1994. Guidebook on mapping groundwater vulnerability. International Contributions to Hydrogeology, Verlag Heinz Heise GmbH and Co, KG.