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۱Zoning of groundwater contaminated by Nitrate using geostatistics method (case study: Bahabad plain, Yazd, Iran)Zoning of Groundwater Contaminated by Nitrate Using Geostatistics Methods (Case Study: Bahabad Plain, Yazd, Iran)
اطلاعات انتشار: Desert، نوزدهم،شماره۱، ۲۰۱۴، سال
تعداد صفحات: ۸
Groundwater quality management is one of the most important issues in many arid and semi–arid regions, including Iran. Nitrate (NO3 –) is one of the most common anions contaminating groundwater. This study aimed to range nitrate concentrations in water resources in Bahabad plain in Yazd province. To evaluate the nitrate data in this descriptive study, 260 nitrate samples from 13 wells in Bahabad were assessed from 2003 to 2013. The two interpolation techniques of kriging and inverse distance weighting (IDW) were used to obtain the spatial distribution of groundwater quality parameters by means of Arcview GIS 10 software. The results of this study showed that the kriging method is more accurate than IDW for groundwater quality mapping, based on the lower root mean square error (RMSE) of kriging. Nitrate levels in samples from regional wells were lower than standard levels for Iran and the world. However, nitrate contamination tended to increase from 2003 to 2013. Furthermore, the greatest nitrate contamination was found in the southern part of Bahabad. In conclusion, kriging seems to be an appropriate method for estimating nitrate levels in groundwater in Bahabad. We recommend action be taken in order to stop the increasing trend of groundwater nitrate contamination in this area.

۲Modelling the formation of Ozone in the air by using Adaptive Neuro–Fuzzy Inference System (ANFIS) (Case study: city of Yazd, Iran)
اطلاعات انتشار: Desert، نوزدهم،شماره۲، ۲۰۱۴، سال
تعداد صفحات: ۵
The impact of air pollution and environmental issues on public health is one of the main topics studied in many cities around the world. Ozone is a greenhouse gas that contributes to global climate. This study was conducted to predict and model ozone of Yazd in the lower atmosphere by an adaptive neuro–fuzzy inference system (ANFIS). All the data were extracted from 721 samples collected daily over two successive years, from April 2012 to 29 March 2014. The concentration of pollutants and meteorological variables including NOX, temperature, wind speed and wind direction were considered as input and ozone (O3) as the output of model. The results showed that among five membership functions used in the model, the Gaussian membership function with R2 equal to 0.949, RMSE equal to 2.430 and correlation coefficient equal to 0.974 was obtained as the best model to predict the concentration of ozone in the lower atmosphere. This study showed that predicting and modelling ozone using an adaptive neuro–fuzzy inference system (ANFIS) is appropriate and, due to the expansion of the city of Yazd in the not too distant future, it is necessary to pay more attention to the permissible threshold values of pollutants such as ozone.

۳Groundwater quality assessment using artificial neural network: A case study of Bahabad plain, Yazd, Iran
اطلاعات انتشار: Desert، بيستم،شماره۱، ۲۰۱۵، سال
تعداد صفحات: ۷
Groundwater quality management is the most important issue in many arid and semi–arid countries, including Iran.Artificial neural network (ANN) has an extensive range of applications in water resources management. In this study, artificial neural network was developed using MATLAB R2013 software package, and Cl, EC, SO4 and NO3 qualitative parameters were estimated and compared with the measured values, in order to evaluate the influence of key input parameters. The number of neurons in the hidden layer was obtained by the trial–and error method. For this purpose, data from 260 water samples of 13 wells in Bahabad plain were collected during 2003– 2013. The results show that the performance of ANN model was more accurate for Cl (R=0.96), EC(R=0.98), and SO4(R 0.95), using back–propagation algorithms according to the best chosen input parameters. It was observed that the use of ANN model for NO3 was not very accurate, perhaps this was because of the different water sources or the impact of other parameters; thus, this result is in contrast with the study of Diamantopoulou et al. (2005). However, this study confirms that the number of neurons in the hidden layer cannot be found using a specific formula (double the number of inputs plus one) for all parameters but can be obtained using a trial–and–error method.
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