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ORIGINAL ARTICLE
Year : 2020  |  Volume : 9  |  Issue : 1  |  Page : 16

Forecasting nitrate concentration in babol groundwater resources using the grey model (1,1)


1 Social Determinants of Health Research Center (SDHRC), Department of Environmental Health Engineering, School of Public Health, Hamadan University of Medical Science, Hamadan, Iran
2 Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
3 Department of Environmental Health Engineering, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
4 Department of Environmental Health Engineering, Babol University of Medical Sciences, Babol, Iran
5 Research Center for Health, Safety and Environment (RCHSE), Alborz University of Medical Sciences, Karaj, Iran
6 Department of Environmental Health Engineering; Student Research Committee, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran

Correspondence Address:
Hossein Faraji
Department of Environmental Health Engineering, School of Public Health, Hamadan University of Medical Sciences, Hamadan
Iran
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijehe.ijehe_14_19

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Aims: Nitrate can enter water bodies through using chemical fertilizers and discharging the effluents from municipal and industrial sewage treatment plants. As a superiority to the conventional statistical models, Grey models (GMs) require only a limited amount of representative data to estimate the behavior of unknown systems. In the current study, the nitrate concentration of the year 2023 in Babol groundwater resources was forecasted by using GM, namely GM (1, 1). Materials and Methods: This descriptive-cross-sectional study was performed in the city of Babol. The data of 63 wells in urban and rural areas during the warm and cold seasons between 2007 and 2017 were supplied from the Health Center and Babol Rural Water and Sewage Company. In data set, the observed values between 2007 and 2015 were used to fit models, and the observed values between 2016 and 2017 were used to evaluate the accuracy of the model's predictions. To assess the efficiency of the model fitted and precision of the predicted values, we used indexes of forecast absolute error, small error probability, and the proportion of variance statistical metrics. Results: Simulated results showed that the accuracy of the model GM (1, 1) to predict and forecast both data sets is entirely appropriate and reliable. The forecasting values of nitrate concentration of the year 2023 and 8 years later, for urban and rural areas in warm and cold seasons, are 21.30 and 7.30 and 15.63 and 5.34 mg/L, respectively. Conclusion: Although the predicted concentration of nitrate in the studied area is lower than that the standard concentration suggested by the World Health Organization, all water resources should be protected effectively.


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