Document Type : Research Paper
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.
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.