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Evaluation of Groundwater Vulnerability Using Data Mining Technique in Hashtgerd Plain | ||
| فیزیک زمین و فضا | ||
| مقاله 4، دوره 42، شماره 4، دی 1395، صفحه 35-41 اصل مقاله (476.36 K) | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2016.57743 | ||
| نویسندگان | ||
| Saman Javadi* 1؛ S. Mehdy Hashemy2 | ||
| 1Department of Water Engineering, College of Abouraihan, University of Tehran, | ||
| 2Department of Water Engineering, College of Abouraihan, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| Groundwater vulnerability assessment would be one of the effective informative methods to provide a basis for determining source of pollution. Vulnerability maps are employed as an important solution in order to handle entrance of pollution into the aquifers. A common way to develop groundwater vulnerability map is DRASTIC. Meanwhile, application of the method is not easy for any aquifer due to choosing appropriate constant values of weights and ranks. Clustering technique would be an influential method for regionalization of groundwater flow zone to facilitate vulnerability assessment of groundwater aquifers. In this study, a new approach using k-means clustering is applied to make vulnerability maps. Four features of depth to groundwater, hydraulic conductivity, recharge value and vadose zone were considered at the same time as features of clustering. Five regions are recognized out of the Hashtgerd plain. Each zone corresponds to a different level of vulnerability. The results show that clustering provides a realistic vulnerability map so that, Pearson’s correlation coefficients between nitrate concentrations and clustering vulnerability is 72%. | ||
| کلیدواژه‌ها | ||
| Groundwater؛ Vulnerability assessment؛ Clustering؛ Data Mining | ||
| عنوان مقاله [English] | ||
| Evaluation of Groundwater Vulnerability Using Data Mining Technique in Hashtgerd Plain | ||
| چکیده [English] | ||
| Due to simple operation and no needs for expensive infrastructure construction to convey water from a source to farm lands, groundwater becomes the most important sources of agricultural water supply in Iran. However, the contamination of aquifers is a major concern in many countries, specifically in areas without effective groundwater protection and management. Therefore, groundwater vulnerability assessment would be one of the effective informative methods to provide a basis for determining source of pollution. Assessment of groundwater vulnerability is often done by intrinsic vulnerability, which considers hydro-geological conditions. The concept of vulnerability of aquifers was introduced for first time by Marget in 1986. The first definition of vulnerability was proposed by Marget and it means the degree of groundwater contamination by pollution reaching an groundwater system. Overlay and index method could be mentioned as existing method to assess intrinsic vulnerability of groundwater. Moreover the vulnerability index is relatively, dimensionless and immeasurable and depends to hydrogeology and geology of aquifer characteristics. Since then, many researchers applied many methods and techniques to provide a standard way for evaluation of vulnerability. As it mentioned before, it should be noted that all of methods is relatively and dimensionless, using various data depended to sort of aquifer. Compared to other models, DRASTIC model, which is an overlay and index method, is the most popular index that used in many researches. The DRASTIC model is easy to implement and provides a good basis for assessment of groundwater vulnerability in facing contamination. Also it needs a relatively small amount of data that is often available in many aquifers. Vulnerability maps applied as one of the effective management ways to qualitative management. Several models like DRASTIC were applied to this end and many researchers have tried to introduce approaches to provide more realistic constant ranks and weights using in the models. Meanwhile application of DRASTIC model is highly influenced by assigning the weights and ranks. Therefore, it is necessary to use a model depended on variable weights and ranks according to the aquifer features. In this study, clustering technique is employed in regionalization of groundwater flow zone so as to vulnerability assessment of a groundwater case study. To this end, K-Means clustering as unsupervised pattern recognition technique is applied. Thanks to intelligent algorithm of the clustering in finding similarities out of the dataset, the proposed method of this research is capable to be used in each aquifer without considering calibration. The method is employed in a large-scale aquifer in center of Iran and finding results are compared with vulnerability maps of the regions created by DRASTIC approach. In this paper clustering algorithm as one of the applicable data mining methods is used taking the advantage of be independent from constant ranks and weights. In another word, vulnerability map of each region is provided by clustering according to the specific features of each region. Optimum number of clusters is obtained 4 numbers by applying cluster validity index. Cluster map is then created based on spatial location of the created clusters representing the vulnerability map of the region. The map shows that upstream parts of the field are in high risk of groundwater vulnerability. Here, just four influential parameters are used by the clustering method in providing maps. Other parameters could be considered by clustering based on specific characteristics of each aquifer. | ||
| کلیدواژه‌ها [English] | ||
| Groundwater, Vulnerability assessment, Clustering, Data Mining | ||
| مراجع | ||
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Aller, L., Bennet, T., Lehr, J. H., Petty, R. J. and Hackett, G., 1987, Drastic: a standardized system for evaluating groundwater pollution potential using hydrogeological settings, US Environmental Protection Agency.
Baalousha, H., 2006, Vulnerability assessment for the Gaza Strip, Palestine using Drastic, Environmental Geology, 50, 405-414.
Batelaan, O., De Smedt F. and Triest L., 2003, Regional ground-water discharge: phreatophyte mapping, groundwater modelling and impact analysis of land-use change, J. Hydrol., 275 (1-2) 86-108.
Davies, D. L. and Bouldin, D. W., 1979, A cluster separation measure, IEEE transaction on pattern analysis and machine intelligence, 1(4), 224-227.
Feil, B., 2006, Fuzzy clustering in process of data mining, PHD thesis, Department of Process Engineering, University of Veszprem Hungry.
Frapporti, G., Vriend, P. and Van Gaans, P. F. M., 1993, Hydro-geochemistry of the shallow Dutch groundwater: interpretation of the national groundwater quality monitoring network, Water Resources Research, 29(9), 2993-3004.
Han, J. and Kamber, M., 2006, Data mining, concepts and techniques, San Francisco, U.S.A: Morgan Kaufman Publishers.
Javadi, S., Kavehkar, N., Mohammadi, K., Khodadi, A. and Kahawita, K. 2011a, Calibration Drastic using field measurements, sensitivity analysis and statistical method to assess groundwater vulnerability, Water International, 36, 719-732.
Javadi, S., Kavehkar, N., Mousavizadeh, M. H. and Mohammadi, K., 2011b, Modification of Drastic model to map groundwater vulnerability to pollution using nitrate measurements in agricultural areas, Journal of Agricultural Science Technology, 13, 239-249.
Kim, D. W., Lee, K. H. and Lee, D., 2004, On cluster validity index for estimation of the optimal number of fuzzy clusters. Journal of Pattern Recognition Society, 37(10), 2009-2025.
Koskela J., 2004, Pattern recognition in water resources management, A literature review and an application to long-term inflow forecasting, Master of Science thesis, Department of Civil and Environmental Engineering, Helsinki University of Technology.
Margat, J., 1968, Vulnerabilite des mappes d’eau souterraine a la pollution, Orleans: BRGM Publication.
Neshat, A., Pradhan, B. and Dadras, M., 2014, Groundwater vulnerability assessment using an improved Drastic method in GIS Resources, Conservation and Recycling, 86, 74-86.
Niknam, R., Mohammadi, K. and Majd, V. J., 2009, Aquifer vulnerability assessment using GIS and fuzzy system: a case study in Tehran-Karaj aquifer, Iran. Environmental Geology, 58, 437-446.
Nobre, R. C. M., Filho, O. C. R., Mansur, W. J., Nobre, M. M. M. and Cosenza, C. A. N., 2007, Groundwater vulnerability and risk mapping using GIS, modeling and a fuzzy logic tool, Journal of Contaminant Hydrology, 94, 277-292. Ochsenkuhn, K. M., Kontoyannakos, J. and Ochsenku hn-Petropulu, M., 1997, A new approach to a hydrochemical study of groundwater flow, Journal of Hydrology, 194(1), 64-75.
Panagopoulos, G. P., Antonakos, A. K. and Lambrakis, N. J., 2006, Optimization of the Drastic method for groundwater vulnerability assessment via the use of simple statistical methods and GIS, Hydrogeology Journal, 14, 894-911.
Pedroli, B., 1990, Classification of shallow groundwater types in a dutch covers and landscape, Journal of Hydrology, 115, 361-375.
Riley, J. A., Steinhorst, R. K., Winter, G. V. and Williams, R. E., 1990, Statistical analysis of the hydrochemistry of ground waters in Columbia River basalts, Journal of Hydrology, 119(1-4), 245-262.
Saidi, S., Bouria, S., Dhiaa, H. B. and Anselmeb, B., 2011, Assessment of groundwater risk using intrinsic vulnerability and hazard mapping: application to Souassi aquifer, Tunisian Sahel, Agricultural Water Management, 98, 1671-1682.
Theodoridis, S. and Koutroumbas, k., 2003, Pattern recognition, Second edition. USA: Elsevier press.
Van der Heijden, F. and Duin, R. P. W. and De Ridder, D. and Tax, D. M. J., 2004, Classification, parameter estimation and state estimation, West Sussex, England, John wiley & sons Ltd.
Wang, J., He, J. and Chen, H., 2012, Assessment of groundwater contamination risk using hazard quantification, a modified Drastic model and groundwater value, Beijing Plain, China. Sci. Total Environ, 432, 216-226.
Weatherill, G. and Burton, P. W., 2008, Delineation of shallow seismic source zones using K-means cluster analysis, with application to the Aegean region, Geophysical Journal International, 176(2), 565-588.
Zhou, H., Wang, G. and Yang, Q., 1999, A multi-objective fuzzy pattern recognition model for assessing groundwater vulnerability based on the Drastic system, Hydrological Sciences Journal, 44(4), 611-618. | ||
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