Comparison of Vulnerability of the Southwest Tehran Plain Aquifer with Simple Weighting Model (ALPRIFT Model) and Genetic Algorithm (GA)

Nderground land subsidence due to extraction of groundwater resources is one of the events that have been observed in abundance in Iran. If it is not properly managed, this phenomenon can cause irreparable damage to the areas affected by this phenomenon. Population growth and crop technology have also led to more use of groundwater resources in some parts of Iran.
The rate of land subsidence due to excessive groundwater abstraction in Tehran plain has increased over recent years. Under such conditions, its harmful effects on agricultural land, buildings, vital arteries, and deformations of the earth's surface develop in the form of hollows, cracks and fissure in the earth. In this research, using the ALPRIFT model, which is a simple weighing model, consists of seven layers that are effective on subsidence (aquifer media, land use, pumping of groundwater, recharge, impact of aquifer thickness, fault distance, groundwater level decline). The level of vulnerability of plain subsidence was obtained by combining layers in the Arc GIS for a period of one year (2015-2016) and the InSAR satellite radar map was used to validate this model. Despite the acceptability of the results, the genetic algorithm was used to improve the results and optimize the weights. The results showed that the optimization of the weights applied by the genetic algorithm, with increasing correlation coefficient from 0.44 to 0.75, between the subsidence index and the subsidence in the plain, is more capable of evaluating subsidence