Abstract
Monitoring of groundwater quality is an important tool to facilitating adequate information about water management in respective areas. Nitrate concentration in aquifer systems is crucial problem in intensive agricultural regions of Indian subcontinent. Nitrate is one of the qualitative parameter of groundwater and its enrichment leads to human health implications, hence it entails precise periodic extent. In the present study, artificial neural network (ANN) model with back propagation algorithm was implemented to predict groundwater quality and its suitability of Kadava River basin in Nashik district. The groundwater qualitative data were collected from 40 dug/bore wells in pre and post monsoon season of 2011. In this context, significant correlated parameters viz., EC, TDS, TH, Ca, Mg, Na, Cl, CO3, HCO3 and SO4 for pre monsoon; EC, TDS, TH, Mg, Na, Cl, F, CO3, HCO3 and SO4 were considered in post monsoon season. In case of the study area, among 40 groundwater samples, 52.50% and 65% showed higher concentration than the permissible limit (45 mg/L) of Bureau of Indian standards of nitrate in pre and post monsoon season. As a result, the optimal network architectures obtained through R software as 10-8-1 and 10-6-1 for training and 10-6-1 and 10-6-1 are used in testing pre and post monsoon season data set respectively. The simulated outputs track the measured and predicted NO3 values with coefficient of determination (R 2), residual mean square error (RMSE) and mean absolute relative error (MARE) for training and testing data. Accordingly, it is promising to manage groundwater resources in an easier manner with proposed ANN model.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Agarwal R (2012) Nitrate contamination in ground water samples of Gangapur city town (Sawai Madhopur District) Rajasthan. J Chem Biol Phys Sci 2(1):511–513
APHA (2005) Standard methods for the examination of water and wastewater, 21st edn. American Public Health Association, New York
Assaf H, Saadeh M (2009) Geostatistical assessment of groundwater nitrate contamination with reflection on DRASTIC vulnerability assessment: the case of the Upper Litani Basin, Lebanon. Water Resour Manage 23:775–796
BIS (2012) Indian standards specification for drinking water, BIS: 10500: 2012. http://www.cgwb.gov.in/Documents/WQ-standards.pdf
Burkardt MR, Kolpin DW (1993) Hydrogeologic and landuse factors associated with the herbicide and nitrate occurrence in near surface aquifers. J Envviron Qual 22:646–656
Central Groundwater Board (CGWB) (2014) http://cgwb.gov.in/district_profile/maharashtra/nashik.pdf
Chang FJ, Tsai WP, Chen HK, Yam RSW, Herricks EE (2013) A self organizing radial basis network for estimating riverine fish diversity. J Hydrol 476:280–289
Chen J, Wu H, Qian H, Gao Y (2016) Assessing nitrate and fluoride contaminants in drinking water and their health risk of rural residents living in a semiarid region of northwest china. Exposure Health (London). doi:10.1007/s12403-016-0231-9
Comly HH (1987) Cyanosis in infants caused by nitrates in well water. J Am Med Assoc 257:2788–2792
Ehteshami M, Farahani ND, Tavassoli S (2016) Simulation of nitrate contamination in groundwater using artificial neural networks. Model Earth Syst Environ 2(1):1–10
Fijani E, Nadiri AA, Moghaddam AA, Tsai FTC, Dixon B (2013) Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh–Bonab plain aquifer, Iran. J Hydrol 503:89–100
Geological Survey of India (GSI) (2001) District resource Map. GSI, Nashik district
Goyal SK, Chaudhary BS, Singh O, Sethi GK, Thakur PK (2010) GIS based spatial distribution mapping and suitability evaluation of groundwater quality for domestic and agricultural purpose in Kaithal district, Haryana state, India. Environ Earth Sci 61(8):1587–1597.
Hubbard RK, Sheridan JM, Adriano DC, Iskandar AK, Murarka IP (1994) Nitrates in groundwater in the southeastern USA. Contamination of groundwaters, pp 303–345
Kumar PS (2017) Geostatistical modeling of fluoride enrichment and nitrate contamination in the groundwater of Lower Bhavani Basin in Tamil Nadu, India. Model Earth Syst Environ 3(1):1
Laurent M, François A, Marie MJ (2010) Assessment of groundwater quality during dry season in Southeastern Brazzaville, Congo
Maharashtra pollution control board (MPCB) CSIR-National Environmental Engineering Research Institute (NEERI) (2014) Water quality status of water bodies of Maharashtra with resources to analytical and statistical tool (2007–2011) http://mpcb.gov.in/ereports/pdf/waterreport2007-2011.pdf
Maithani S (2009) A neural network based urban growth model of an Indian city. J Indian Soc Remote Sens 37(3):363–376
Meisinger JJ, Randall GW (1991) Estimating nitrogen budgets for soil-crop systems. Managing nitrogen for groundwater quality and farm profitability, (managingnitroge), pp 85–124
Panaskar DB, Wagh VM, Muley AA, Mukate SV, Pawar RS, Aamalawar ML (2016) Evaluating groundwater suitability for the domestic, irrigation, and industrial purposes in Nanded Tehsil, Maharashtra, India, using GIS and statistics. Arabian J Geosci 9(13):615
Reddy AGS (2013) Geochemical evaluation of nitrate and fluoride contamination in varied hydrogeological environs of Prakasam district, southern India. Environ Earth Sci 71:4473–4495
Salami ES, Ehteshami M (2015) Simulation, evaluation and prediction modeling of river water quality properties (case study: Ireland rivers). Int J Eng Sci Technol 12(10):3235–3242. doi:10.1007/s13762-015-0800-7
Sharma V, Negi SC, Rudra RP, Yang S (2003) Neural networks for predicting nitrate-nitrogen in drainage water. Agric Water Manag 63(3):169–183
Strebel O, Duynisveld W, Bo¨ttcher J (1989) Nitrate pollution of groundwater in Western Europe. Agric Ecosyst Environ 26(3):189–214
Subramani T, Elango L, Damodarasamy SR (2005) Groundwater quality and its suitability for drinking and agricultural use in Chithar River Basin, Tamil Nadu, India. Environ Geol 47:1099–1110
Todd DK (1980) Groundwater hydrology, 2nd edn. Wiley, New York
Toth J (1999) Groundwater as a geologic agent: an overview of the causes, processes, and manifestations. Hydrogeol J 7(1):1–14
Wagh VM, Panaskar DB, Muley AA, Mukate SV, Lolage YP, Aamalawar ML (2016b) Prediction of groundwater suitability for irrigation using artificial neural network model: a case study of Nanded tehsil, Maharashtra, India. Model Earth Syst Environ 2(4):196
Wagh VM, Panaskar DB, Varade AM, Mukate SV, Gaikwad SK, Pawar RS, Muley AA, Aamalawar ML (2016a) Major ion chemistry and quality assessment of the groundwater resources of Nanded tehsil, a part of southeast Deccan Volcanic Province, Maharashtra, India. Environ Earth Sci 75(21):1418
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wagh, V.M., Panaskar, D.B. & Muley, A.A. Estimation of nitrate concentration in groundwater of Kadava river basin-Nashik district, Maharashtra, India by using artificial neural network model. Model. Earth Syst. Environ. 3, 36 (2017). https://doi.org/10.1007/s40808-017-0290-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s40808-017-0290-3