نوع مقاله : مقاله پژوهشی
نویسندگان
1 PhD in Evaluation Environmental and Land Use Planning, Faculty of Natural Resources & Environment, Malayer University, Malayer, Iran.(
2 Assistant professor, research group of environmental assessment and risks, Research Center for Environment and Sustainable Development (RCESD), Department of Environment, Tehran, Islamic Republic of Iran.
3 Ph.D. Student of Environmental Pollution, Faculty of Natural Resources & Environment, Malayer University, Malayer, Iran.
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
The main purpose of the present study is to modeling the variation of ground-water quality parameters from 2001 to 2018 and predicting its quality for 2027. To achieve it, we accessed parameters which included total hardness (TH), total dissolved solids (TDS), sodium (Na), sulfates (SO4), and chlorides (Cl) which acquired from thirty-four wells in Dehloran Plain, Ilam. Due to the large number of wells, the samples were classified through cluster analysis into six clusters. To determine the number of clusters, a hierarchical clustering method was used. Five time-series models of autoregressive (AR), moving-average (MA), auto-regressive moving-average (ARMA), autoregressive integrated moving-average (ARIMA), and seasonal auto-regressive integrated moving-average (SARIMA) were applied to predict the changing ground-water quality. The best model was selected based on the Autocorrelation function (ACF) and Partial autocorrelation function (PACF), Akaike Information Criterion (AIC), and Coefficient of determination (R2). The results of the prediction indicated that the average concentration of Cl and Na will increase in all the clusters in 2027. Moreover, the average of the predicted SO4 will increase in all clusters except for the sixth one. The average of TDS also will increase in the first to third clusters, while it will decline in the fourth, fifth, and sixth clusters. The average of the predicted TH in the first, second, third, and fifth clusters will rise, whereas it will be reduced in the fourth and sixth clusters. It can be concluded that the status of ground-water quality is worsening in Dehloran Plain and in 2027 its quality will become lower compared to previous years.
کلیدواژهها [English]
3- Anttila, P. and Tuovinen, J. P., 2010. Trends of primary and secondary pollutant concentrations in Finland in 1994–2007. Atmos. Environ, 44, pp. 30–41.
4- Behnia, N. and Rezaeian, F., 2015. Coupling wavelet transform with time series models to estimate groundwater level. Springer Berlin Heidelberg, 8, pp. 1866-7538.
5- Box, G.E.P., Jenkins, G.M. and Reinsel, G.C., 1994. Time series analysis: forecasting and control. Prentice Hall, Englewood Cliffs.
6- Cadenas, E. and Rivera, W., 2010. Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model. Renew. Energy, 35, pp. 2732-2738.
7- Chaudhuri, C.h. and Dutta, D., 2014. Mann–Kendall trend of pollutants, temperature and humidity over an urban station of India with forecast verification using different ARIMA models. Environ Monit Assess, 186, pp.4719–4742.
8- Dhayachandhran, K.S. and Jothilakshmi, M., 2020. Quality assessment of ground water along the banks of Adyar river using GIS. Materials Today: Proceedings, 45 (4), pp.6234-6241.
9- D'Urso, P., De Giovanni, L. and Massari, L., 2015. Time series clustering by a robust auto regressive metric with application to air pollution. Chemometrics and Intelligent Laboratory Systems, 141, pp. 107–124.
10- Erdem, E. and Shi, J., 2011. ARMA based approaches for forecasting the tuple of wind speed and direction. Appl Energy, 84(2), pp. 1405–1414.
11- Faruk, D., 2010. A hybrid neural network and ARIMA model for water quality time series prediction. Eng Appl Artif Intell, 23(4), pp. 586–594.
12- Guler, C., Thyne, G. D., McCray, J. E. and Turner, A. K. 2002. Evaluation of graphical and multivariate statistical methods for classification of water chemistry data. Hydrogeology journal, 10, 455-474.
13- Hannan, E.J. 1971. Multiple time series. Wiley, New York.
14- He, Z., Zhang, Y., Guo, Q. and Zhao, X., 2014. Comparative study of artificial neural networks and wavelet artificial neural networks for groundwater depth data forecasting with various curve fractal dimensions. Water resources management, 28(15), pp.5297-5317.
15- Karthika, I.N., Thara, K. and Dheenadayalan, M.S., 2018. Physico-Chemical Study of the Ground Water Quality at Selected Locations in Periyakulam, Theni district, Tamilnadu, India. Materials Today: Proceedings, 5(1), pp.422-428.
16- Kumar, S. and Sangeetha, B., 2020. Assessment of ground water quality in Madurai city by using geospatial techniques. Groundwater for Sustainable Development, 10, p.100297.
17- Liu, H., Wu, H., Lv, X., Ren, Z., Liu, M., Li, Y. and Shi, H., 2019. An Intelligent Hybrid Model for Air Pollutant Concentrations Forecasting: Case of Beijing in China. Sustainable Cities and Society. 47, p.101471.
18- Mirsangari, M, M., Zarandian, A., Mohammadyari, F. and suziedelyte-visockiene, j., 2020. Investigation of the impacts of urban vegetation loss on the ecosystem service of air pollution mitigation in Karaj metropolis, Iran. Journal Environmental Monitoring and Assessment, 192, pp. 1-23.
19- Mirsanjari, M. M. and Mohammadyari, F., 2017, Application of Time-series Model to Predict Groundwater Quality Parameters for Agriculture: (Plain Mehran Case Study), International Conference on Renewable Energy and Environment November 1-3, 2017 Toronto, Canada.
20- Mirzaee, S., Chitsazan, M., CHinipardaz, R. and Samady, H., 2010. Forecast plain SHAHREKORD using time-series models and examine ways to improve, The first regional conference on optimal utilization of water resources and river basins Karon, 46, pp. 1-8.
21- Mirzavand, M. and Ghazavi, R., 2015. A stochastic modelling technique for groundwater level forecasting in an arid environment using time series methods. Water resources management, 29(4), pp.1315-1328.
23- Mohammadyari, F. 2021. Evaluating and Modeling Selected Ecosystem Services with the Approach of Urban Expansion Impacts on landscape patterns in Karaj metropolis. PhD dissertation. Malayer University Faculty of Natural Resources and Environment
24- Panda, D.K. and Kumar, A., 2011. Evaluation of an over-used costal aquifer (Orissa, India) using statistical approaches. Hydrol. Sci. Jour, 56, pp. 486-497.
25- Samadihabashi, R., 2014. Groundwater level prediction using time series model (Case study: Plain Urmia), Master's thesis, University of Urmia, 160pp.
26- Shirmohammadi, B., Vafakhah, M., Moosav,i V. and Moghaddamnia, A. 2013. Application of several data-driven techniques for predicting groundwater level. Water Resour Manag, 27, pp. 419–432.
27- Taneja, K., Ahmad, S.h., Ahmad, K. and Attri, S.D., 2017. Time series analysis of aerosol optical depth over New Delhi using BoxeJenkins ARIMA modeling approach. Atmospheric Pollution Research, 7, pp. 585-596.
28- Wang, H.R., Wang, C., Lin, X. and Kang, J., 2014. An improved ARIMA model for precipitation simulations. Nonlinear Process. Geophys. 21, pp. 1159-1168.
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