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Exploiting Two Intelligent Models to Predict Water Level: A field study of Urmia lake, Iran
BibTeX
@MISC{Kavehkar_exploitingtwo,
author = {Shahab Kavehkar and Mohammad Ali Ghorbani and Valeriy Khokhlov and Afshin Ashrafzadeh and Sabereh Darb},
title = {Exploiting Two Intelligent Models to Predict Water Level: A field study of Urmia lake, Iran},
year = {}
}
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Abstract
Abstract—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
Keyphrases
field study urmia lake predict water level intelligent model water resource engineering past time series present study reliable prediction genetic programming daily water level variation time interval sea-level variation coastal river coastal engineering observed water level water level affect groundwater table abstract water level hydrological regime hydrologic study low-lying coastal area artificial neural network
