Classification of Groundwater Level Data Using SOM to Develop ANN-Based Forecasting Model
V. Nourani1, A. Hosseini Baghanam2, F. Daneshvar Vousoughi3, M.T. Alami4

1V. Nourani, Department of Civil Eng., School of Eng., Univ. of Minnesota, USA.
2A. Hosseini Baghanam, Dept. of Water Resources Eng.,Faculty of Civil Eng. Univ. of Tabriz, Iran
3F. Daneshvar Vousoughi, Dept. of Water Resources Eng.,Faculty of Civil Eng. Univ. of Tabriz, Iran
4M.T. Alami, Dept. of Water Resources Eng.,Faculty of Civil Eng. Univ. of Tabriz, Iran

Manuscript received on February 15, 2012. | Revised Manuscript received on February 20, 2012. | Manuscript published on March 05, 2012. | PP: 464-469 | Volume-2 Issue-1, March 2012. | Retrieval Number: A0482032212/2012©BEIESP
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Abstract: Prediction of groundwater level in a watershed plays a crucial role in management of groundwater resources, especially in a semi-arid area where there is immense need to groundwater resources in order to prepare the requirement water for agriculture, municipal and industrial affairs. The aim of this study is to present a mathematical based model to estimate the groundwater level (GWL) in Ardabil located at northwest of Iran, with association of some hydrological data (e.g., rainfall, discharge, etc.). In this way identifying various zones with similar groundwater level can be a promising idea which leads to appropriate overview on water table of the study area as well as efficient modeling. For this purpose, the Self Organizing Map (SOM) was used to cluster the homogenous monitoring piezometers in the plain by utilizing GWL and Universal Transverse Mercator (UTM) data. The sensitivity analysis was performed over normalized and non-normalized data of GWL and UTM in order to investigate their effects on clustering. Conventional K-Means method was applied to verify the results of SOM method. The central piezometer of each cluster was selected as a representative by means of statistical technique. Afterwards the three layer feed forward Artificial Neural Network (ANN) model was utilized to calibrate a model via historical groundwater level records from the representative wells and relevant hydro-meteorological data. The last step was performed by simulating water table level of the representative piezometer from each zone of the plain via proposed model, to compare the computed and observed data. The results reveal the suitability of SOM clustering method with normalized data of GWL and also identify the specific piezometers that the GWL of them can represent the GWL in a particular region. Thus, adequate measures should be devoted on preserving such important monitoring piezometers and reliable data can be obtained from them in order to generalize the GWL data to that specific region. The modeling results can be utilized to frame the corresponding strategies to reduce the monitoring cost and to enhance the cost-effective benefits. The proposed methodology can be referred as a management plan for groundwater resources

Keywords: Ardabil Plain, Artificial Neural Network, Clustering, Groundwater level, Self Organizing Map.