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Exploiting Two Intelligent Models to Predict Water Level: A Field Study of Urmia Lake, Iran

Authors: Mohammad Ali Ghorbani, Shahab Kavehkar, Valeriy Khokhlov, Afshin Ashrafzadeh, Sabereh Darbandi


Water level forecasting using records of past time series is of importance in water resources engineering and management. For example, water level affects groundwater tables in low-lying coastal areas, as well as hydrological regimes of some coastal rivers. Then, a reliable prediction of sea-level variations is required in coastal engineering and hydrologic studies. During the past two decades, the approaches based on the Genetic Programming (GP) and Artificial Neural Networks (ANN) were developed. In the present study, the GP is used to forecast daily water level variations for a set of time intervals using observed water levels. The measurements from a single tide gauge at Urmia Lake, Northwest Iran, were used to train and validate the GP approach for the period from January 1997 to July 2008. Statistics, the root mean square error and correlation coefficient, are used to verify model by comparing with a corresponding outputs from Artificial Neural Network model. The results show that both these artificial intelligence methodologies are satisfactory and can be considered as alternatives to the conventional harmonic analysis.

Keywords: Artificial Neural Networks, Forecasting, Genetic Programming, Comparative Analysis, Water-Level variation

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