Development of a Strategy Using Spatial Analysis and Neural Network for Spatial Analysis of Water Levels at the Time of Drought

Urmia lake due to the presence of various species of wildlife, species of vegetation on the islands, create a natural balance in the Azerbaijan region,  tourist, recreational and social value, medical value, reserve of the Bio sepehr and as well as a wetland of international importance is special. Over the last few decades , use remote sensing technology to detect trends such changes various researchers have drawn attention to themselves. Factors that have caused Urmia lake will be in such a situation  is varied. But in general, they can be divided into two categories :The factors that played a role in humans includes free use of water resources , agriculture unbridled development around the lake, and environmental factors like climate change , which according to the reduction of heavens and evaporation of Urmia lake water And reducing the flow volume and reduce annual temperature the lake ecosystem has been affected. Study of meteorological parameters of Urmia lake and investigation of its level changes in order to apply water resources management is important. Recent studies show which level and volume of lake water relatively decreasing. Urmia lake water level from 1992 to 1997 significantly increased and decreased from 1997 to 2009 and has remained almost constant since 2010. As a result, to rebuild the lake and managing the water resources of this lake is necessary, the role of effective parameters is determined. Therefore, neural network method was used in this research,meteorological parameters such as evaporation,temperature, precipitation, and annual amounts of groundwater abstraction of  wells around the Urmia lake and the amount of water entering the lake, between 1997 and 2011, as input parameters And the annual altitude and area of the lake water entered the neural network as output parameters. In this research, the Levenberg rules were used to train the network. After training model by meteorological parameters, it was determined that the neural network model approximates the data in a perfectly accurate and accurate manner. It can also be predicted that changes in height and area occur by changing each of the parameters. This network estimates the lake area of Urmia at 3% error and 97% accuracy and lake level of 0/8 m. The correlation coefficient of the removal was obtained with the height and the range of -0.4. The correlation coefficient of precipitation with 2 dependent parameters was obtained +0.15 Input flow rate of +0.4.  After reviewing the model, it was found that the removal parameter from underground wells and the Input water volume into the lake compared to other parameters have a more significant effect on altitude and area. The results indicate that water use for agriculture and harvesting of water resources have increased And also the crops that are grown are products with a high water consumption pattern And also the water stored behind the dams has reduced the inflow to the lake.