Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz Plain, Iran

Journal of Environmental Health Science and Engineering

Volumn 14, Issue 1, 2016, Pages

  Baghapour, Mohammad Ali ^a   Nobandegani, Amir Fadaei ^a   Talebbeydokhti, Nasser ^b   Bagherzadeh, Somayeh ^c   Nadiri, Ata Allah ^d   Gharekhani, Maryam ^d   Chitsazan, Nima ^e  

^a SHIRAZ UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^b SHIRAZ UNIVERSITY   (Iran)

^c Ab Ati Pazhooh Consulting Engineers Company   (Iran)

^d UNIVERSITY OF TABRIZ   (Iran)

^e Research Engineer at EnTech Engineering   (United States)

Author keywords

Artificial neural network; Composite DRASTIC; Nitrate Vulnerability; Shiraz aquifer

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CONCENTRATION (COMPOSITION); MODELING; NITRATE; OPTIMIZATION; UNCONFINED AQUIFER; VULNERABILITY; WATER QUALITY; WATER TABLE;

FARS; IRAN; SHIRAZ;

EID: 85007351168     PISSN: None     EISSN: 2052336X     Source Type: Journal    
DOI: 10.1186/s40201-016-0254-y     Document Type: Article

References (34)

1. Tilahun K, Merkel BJ. Assessment of groundwater vulnerability to pollution in Dire Dawa, Ethiopia using DRASTIC. Environ Earth Sci. 2010;59:1485-96.
2. Sinan M, Razack M. An extension to the DRASTIC model to assess groundwater vulnerability to pollution: application to the Haouz aquifer of Marrakech (Morocco). Environ Geol. 2009;57:349-63.
3. Jamaludin N, Sham SM, Smail SNS. Health risk assessment of nitrate exposure in well water of residents in intensive agriculture area. AJAS. 2013;10:442-8.
4. Rahman A. A GIS based DRASTIC model for assessing groundwater vulnerability in shallow aquifer in Aligarh, India. J Appl Geography. 2008;28(1):32-53.
5. Manos B, Papathanasiou J, Bournaris T. A multicriteria model for planning agricultural regions within a context of groundwater rational management. J Environ Manage. 2010;91:1593-600.
6. Sener E, Sener S, Davraz A. Assessment of aquifer vulnerability based on GIS and DRASTIC methods: a case study of the Senirkent-Uluborlu Basin (Isparta, Turkey). H ydrogeol J. 2009;17:2023-35.
7. Babiker IS, Mohamed MAA, Hiyama T, Kato K. A GIS-based DRASTIC model for assessing aquifer vulnerability in Kakamigahara Heights, Gifu Prefecture, central Japan-central Japan. Sci Total Environ. 2005;345:127-40.
8. Stigter TY, Ribeiro L, Carvalho Dill AMM. Evaluation of an intrinsic and a specific vulnerability assessment method in comparison with groundwater salinisation and nitrate contamination levels in two agricultural regions in the south of Portugal. Hydrogeol J. 2006;14:79-99.
9. Secunda S, Collin ML, Melloul AJ. Groundwater vulnerability assessment using a composite model combining DRASTIC with extensive agricultural land use in Israel's Sharon region. J Environ Manage. 1998;54:39-57.
10. Al-Adamat RAN, Foster IDL, Baban SMJ. Groundwater vulnerability and risk mapping for the Basaltic aquifer of the Azraq basin of Jordan using GIS, Remote sensing and DRASTIC. J Appl Geography. 2003;23:303-24.
11. Saidi S, Bouri S, Ben Dhia H. Groundwater vulnerability and risk mapping of the Hajeb-jelma aquifer (Central Tunisia) using a GIS-based DRASTIC model. Environ Earth Sci. 2010;59:1579-88.
12. Martinez-Bastida JJ, Arauzo M, Valladolid M. Intrinsic and specific vulnerability of groundwater in central Spain: the risk of nitrate pollution. Hydrogeol J. 2010;18:681-98.
13. Dixon B. Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis. J Hydrolo. 2005;309:17-38.
14. Dixon B. Groundwater vulnerability mapping: a GIS and fuzzy rule based integrated tool. J Appl Geography. 2005;25:327-47.
15. Dixon B. A case study using support vector machines, neural networks and logistic regression in a GIS to identify wells contaminated with nitrate-N. Hydrogeol J. 2009;17:1507-20.
16. Maier HR, Jain A, Dandy GC, Sudheer KP. Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environ Model Softw. 2010;25:891-909.
17. Nourani V, Mogaddam AA, Nadiri AA. An ANN based model for spatiotemporal groundwater level forecasting. Hydrol Processes. 2008;22:5054-66.
18. Chitsazan N, Nadiri AA, Tsai FTC. Prediction and structural uncertainty analyses of artificial neural networks using hierarchical Bayesian model averaging. J Hydrology. 2015;528:52-62.
19. Baghapour MA, Talebbeydokhti N, Tabatabee H, Nobandegani AF. Assessment of Groundwater Nitrate Pollution and Determination of Groundwater Protection Zones Using DRASTIC and Composite DRASTIC (CD) Models: The Case of Shiraz Unconfined Aquifer. J Health Sci Surveillance Sys. 2014;2:54-65.
20. Shirazi SM, Imran HM, Akib S, Yusop Z, Harun ZB. Groundwater vulnerability assessment in the Melaka State of Malaysia using DRASTIC and GIS techniques. Environ Earth Sci. 2013;70:2293-304.
21. Yin L, Zhang E, Wang X, Wenninger J, Dong J, Guo L, Huang J. A GIS-based DRASTIC model for assessing groundwater vulnerability in the Ordos Plateau, China. Environ Earth Sci. 2013;69:171-85.
22. Jamrah A, Al-Futaisi A, Rajmohan N, Al-Yaroubi S. Assessment of groundwater vulnerability in the coastal region of Oman using DRASTIC index method in GIS environment. Environ Monit Assess. 2008;147:125-38.
23. Chitsazan M, Akhtari Y. A GIS-based DRASTIC model for assessing aquifer vulnerability in Kherran Plain, Khuzestan, Iran. Water Resour Manage. 2009;23:1137-55.
24. Hopfield JJ. Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci U S A. 1982;79:2554-8.
25. Fijani E, Nadiri AA, Mogaddam AA, Mogaddam AA, Tsai FTC, Dixon B. Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh. Bonab plain aquifer, Iran. J Hydrolo. 2013;503:89-100.
26. Nadiri A, Hassan MM, Asadi S. Supervised Intelligence Committee Machine to Evaluate Field Performance of Photocatalytic Asphalt Pavement for Ambient Air Purification. Transportation Research Record: TRB. 2015;2528:96-105.
27. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. Artificial neural network in hydrology, part I and II. J Hydraul ENG-ASCE. 2000;5:115-37.
28. Brahim FB, Khanfir H, Bouri S. Groundwater vulnerability and risk mapping of the Northern Sfax Aquifer, Tunisia. Arab J Sci Eng. 2012;37:1405-21.
29. Prasad RK, Singh VS, Krishnamacharyulu SKG, Banerjee P. Application of drastic model and GIS: for assessing vulnerability in hard rock granitic aquifer. Environ Monit Assess. 2011;176:143-55.
30. Mahvi AH, Nouri J, Babaei AA, Nabizadeh R. Agricultural activities impact on groundwater nitrate pollution. Int J Environ Sci Tech. 2005;2:41-7.
31. Dahan O, Babad A, Lazarovitch N, Russak EE. Nitrate leaching from intensive organic farms to groundwater. Hydrol Earth Syst Sci. 2013;18:333-41.
32. Kraft GJ, Stites W. Nitrate impacts on groundwater from irrigated-vegetable systems in a humid north-central US sand plain. Agric Ecosyst Environ. 2003;100:63-74.
33. Esmaeili A, Moore F, Keshavarzi B. Nitrate contamination in irrigation groundwater, Isfahan, Iran. Environ Earth Sci. 2014;72:2511-22.
34. Shen Y, Lei H, Yang D, Kanae SH. Effects of agricultural activities on nitrate contamination of groundwater in a Yellow River irrigated region. Water Quality: Current Trends and Expected Climate Change Impacts. IAHS Publ. 2011;348:73-80.