Akbarzadeh, M., Ghahraman, B., & Davary, K. (2016). Identification of homogeneous stations for quality monitoring network of Mashhad aquifer based on nitrate pollution. Journal of Water and Soil, 30(5), 1382-1393. (In Farsi).
Barzegar, R., Fijani, E., Moghaddam, A. A., & Tziritis, E. (2017). Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. Science of the Total Environment, 599, 20-31.
Ebrahimi Varzane, S., TishehZan, P., & Akhondali, A. m. (2019). Evaluation of Groundwater-Surface Water Interaction by Using Cluster Analysis (Case Study: Western Part of Dezful-Andimeshk Plain). Iran Water Resources Research, 15(3), 246-257. (In Farsi).
Javadi, S., Hashemy, S., Mohammadi, K., Howard, K., & Neshat, A. (2017). Classification of aquifer vulnerability using K-means cluster analysis. Journal of hydrology, 549, 27-37.
Kardan, M. H., & Roozbahani, A. (2015). Evaluation of Bayesian networks model in monthly groundwater level prediction (Case study: Birjand aquifer). Journal of Water and Irrigation Management, 5(2), 139-151. (In Farsi).
Lee, S., Lee, K.-K., & Yoon, H. (2019). Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors. Hydrogeology Journal, 27(2), 567-579.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Paper presented at the Proceedings of the fifth Berkeley symposium on mathematical statistics and probability.
Maier, H. R., & Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental modelling & software, 15(1), 101-124.
Moghaddam, H., Banihabib, M., & Javadi, S. (2018). Quantitative sustainability analysis of aquifer system (case study: South Khorasan-Birjand aquifer). Journal of Water and Soil, 31(6). (In Farsi).
Nayak, P. C., Rao, Y. S., & Sudheer, K. (2006). Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water resources management, 20(1), 77-90.
Nikbakht, J., & Nouri, S. (2017). Clustering Observation Wells Network and Forecasting Groundwater Level by Artificial Neural Networks (Case Study: Marageh Plain). water and Soil Science, 27(1), 281-294. (In Farsi).
Rakhshandehroo, G., Akbari, H., Afshari Igder, M., & Ostadzadeh, E. (2017). Long-term groundwater-level forecasting in shallow and deep wells using wavelet neural networks trained by an improved harmony search algorithm. Journal of Hydrologic Engineering, 23(2), 04017058.
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, 53-65.
Soroush, F., & Seifi, A. (2019). Application of a Self-Organizing Map for Clustering the Groundwater Quality in Kerman Province and Assessment its Suitability for Drinking and Irrigation Purposes. JWSS-Isfahan University of Technology, 23(2), 281-302. (In Farsi).