Spatio-temporal Analysis of Groundwater Level Using Clustering Method Combined with Artificial Neural Network

Document Type : Research Paper


1 Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Civil Engineering, Faculty of Technical Engineering, Qom University of Technology, Qom, Iran

3 Department of Water Resources Research, Water Research Institute, Ministry of Energy, Tehran, Iran


Long-term planning and proper management of groundwater resources utilization are essential to ensure a reliable supply of water to countries, especially in arid and semi-arid regions. Therefore, it is necessary to employ appropriate models to predict the spatial and temporal fluctuations of aquifers and their future behavior. This study aimed to apply zoning strategies to Miandoab aquifer and predict its spatial and temporal groundwater level using an artificial neural network. First, the six parameters of transmissivity coefficient, groundwater level, ground elevation, withdrawal, rainfall, and discharge were spatially clustered to identify their effect on the simulation model. Three clustering approaches of single-parameter, three-parameter and integrated-parameter were evaluated using some statistical indices. The number of suitable clusters was determined using silhouette width. Groundwater level data (2002-2012) from 77 observational wells were used for model training and validation. Results showed that the correlation clustering approach performs better than the other methods. Precipitation, aquifer recharge, aquifer discharge, and groundwater level of the previous month were inputs to the back-propagation artificial neural network (ANN) for predicting a two-year period of groundwater level. The results showed that the correlation coefficients of variation in 6 clusters were 0.71- 0.97, and the RMSE variations were 0.19 - 0.58, indicating appropriate accuracy of this approach for predicting groundwater level.


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).