Drying of urmia lake: modeling of level fluctuations

Background: Urmia Lake, the second largest hyper-saline lake of the world, has experienced lack of water and other environmental issues in recent years. Now, there is a danger of the lake drying out, which will affect the region and its inhabitants. This study aimed to present a model which can relate the water level of the lake to effective factors

Methods: Parameters that influence water level, such as precipitation, evaporation, water behind dams, and the previous year's water level, were considered in the modeling procedure. The proposed model, based on evolutionary polynomial regression, can be used to evaluate salt marshes produced in the region in recent years

Results: Results show that the high surface-area-to-depth ratio of Urmia Lake is most influential on its drying; however, omitting this characteristic as an inherent one, the main cause is the construction of dams on rivers in the Urmia Lake basin

Conclusion: The proposed model predicts that by 2015, the water level of Urmia Lake will fall below 1269 m, and by 2030, the lake will dry out completely

Using ANN and EPR models to predict carbon monoxide concentrations in urban area of Tabriz

Background: Forecasting of air pollutants has become a popular topic of environmental research today. For this purpose, the artificial neural network [AAN] technique is widely used as a reliable method for forecasting air pollutants in urban areas. On the other hand, the evolutionary polynomial regression [EPR] model has recently been used as a forecasting tool in some environmental issues. In this research, we compared the ability of these models to forecast carbon monoxide [CO] concentrations in the urban area of Tabriz city

Methods: The dataset of CO concentrations measured at the fixed stations operated by the East Azerbaijan Environmental Office along with meteorological data obtained from the East Azerbaijan Meteorological Bureau from March 2007 to March 2013, were used as input for the ANN and EPR models

Results: Based on the results, the performance of ANN is more reliable in comparison with EPR. Using the ANN model, the correlation coefficient values at all monitoring stations were calculated above 0.85. Conversely, the R2 values for these stations were obtained <0.41 using the EPR model

Conclusion: The EPR model could not overcome the nonlinearities of input data. However, the ANN model displayed more accurate results compared to the EPR. Hence, the ANN models are robust tools for predicting air pollutant concentrations