Urmia Lake Drying Process Modeling Based On Remotely Sensed Images and Artificial Neural Networks

Urmia Lake is about two decades, each year experiencing 40 cm of water level reduction. In this study, the Urmia Lake drought process is investigated by analyzing multi-temporal remote sensing images. The proposed technique is comprising the following three steps. In the first stage, Landsat 7 and 8 satellite images for 2012-2017 are investigated. In the second stage, each of the batch images is separated for each image of four areas of the blue zone, shallow water, soil and salt. In the third step, neural network trained for 2012-2016 as input and tested for 2017 as an output and a model has been developed for future changes in the next year (2018). The results showed that the trend of changes in Urmia Lake in the last 6 years from 2012 through 2014 witnessed a decrease in water and shallow water regions in July and in the last two years the amount of water and shallow regions has increased. In the following, modelling the process of changes using neural network for 2018 also indicated that in the next year we will see an increase in the water supply in the lake, which is not significant.