Groundwater Simulation Using Artificial Neural Networks and ArcGIS Under Different Scenarios (Case Study: Mahyar Plain)

Abstract

North Mahyar plain in Isfahan, is one of the plains which has confront water crisis. In these circumstances, attention to the capacity of the resources and proper management is an important issue to pass this condition. Chitsazan et al. (2013) noted that artificial neural network is able to figure out the relation of hydrologic parameter. Also, this tool can be used in water resources management (Coppola et al., 2005). Therefore, by applying artificial neural network, water-table data and cropping pattern in plain, three land-use scenarios were designed. Water table was simulated for water year of 2008 with one-layer network, Levenberg–Marquardt algorithm, and three functions in MATLAB­­_R2012a. Water table map was prepared by using simulated water table in ArcGIS 10.2, and zoning was performed according to the costs of water pumping. Results showed that 61-86 percent of the plain in all three scenarios had medium limitation. Also, using different management in field like fallowing and planting low-water use crops caused 3 and 5 percent increase in acreage of "without limitation" lands, respectively.

Keywords

• mapping
• cropping pattern
• water table
• field water management