Groundwater Level Forecasting Using Wavelet-Artificial Neural Network and Its Comparison with MODFLOW Numerical Model in Qorveh Plain


Faculty Geology Department of Sciences, Kharazmi University, Tehran, Iran


Groundwaters are considered as an important source of water production in the world. Considering water resources shortage in the recent years, it is very important to utilize and optimize groundwater resources. To understand the importance of these resources and their optimal management, one needs to predict precisely the groundwater level fluctuations. These fluctuations are caused by different factors such as climate, temperature, precipitation, evaporation, addition of water to and extraction of it from the aquifer (recharge and discharge) and so on. Nowadays, different models of groundwater level fluctuations have been proposed. But the popular method concerned by hydrogeological engineers in the recent years has been Wavelet-Artificial Neural Network utilization. The current study firstly considerd the principles of these methods. It then dealt with a case study of Qorveh plain. In the final stage, it compared the results obtained from the model with the results obtained from the MODFLOW numerical model. The net outcomes showed that the wavelet-neural network method has been more efficient than the numerical method.


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