Comparison of Neural Network and Neuro-Fuzzy Techniques to Improve the DRASTIC Frame Work (Case Study: Shabestar plain Aquifer)
2020
Asghari Moghaddam, Asghar | Kadkhodaie, Fatemeh | Barzegar, Rahim | Gharekhani, Maryam
Increasing population and rising water requirements have raised the use of freshwater resources such as groundwater. Therefore, assessing the vulnerability of groundwater is a suitable method for identifying the vulnerable areas and protecting these resources. Shabestar plain in East Azarbaijan province is an active agricultural area and the use of groundwater resources in this plain is important due to the shortage of annual precipitation. In this study, the DRASTIC frame work was used to assess the vulnerability of the Shabestar plain aquifer. The amount of DRASTIC vulnerability index in the study area was calculated as 53.3to 118.3. Given that the weights of the DRASTIC frame work were somewhat expert, so the main purpose of this study was improvement of the DRASTIC by two methods of Neural Network and Neuro-Fuzzy. DRASTIC inputs were introduced as inputs of the both artificial intelligence models. The corrected DRASTIC index with nitrate concentration was considered as the outputs of the models. Nitrate values were categorized into two groups of train and test. After training the model the results of the model were evaluated at the test step with nitrate concentration. The results showed that the both artificial intelligence models had the high ability to improve the DRASTIC model. Nevertheless, the neuro-fuzzy model having a higher correlation coefficient with nitrate was a suitable method for assessing the vulnerability of Shabestar plain aquifer.
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Эту запись предоставил University of Tabriz